# 6. Time Series

Dynare provides a MATLAB/Octave class for handling time series data, which is based on a class for handling dates. Dynare also provides a new type for dates, so that the user does not have to worry about class and methods for dates. Below, you will first find the class and methods used for creating and dealing with dates and then the class used for using time series. Dynare also provides an interface to the X-13 ARIMA-SEATS seasonal adjustment program produced, distributed, and maintained by the U.S. Census Bureau (2020).

## 6.1. Dates

### 6.1.1. Dates in a mod file

Dynare understands dates in a mod file. Users can declare annual, bi-annual, quarterly, or monthly dates using the following syntax:

    1990Y
    1990A
    1990S2
    1990H2
    1990Q4
    1990M11

Note that there are two syntaxes for annual dates (1990A is equivalent to 1990Y), and for bi-annual dates (1990H2 is equivalent to 1990S2).

Behind the scene, Dynare’s preprocessor translates these expressions into instantiations of the MATLAB/Octave’s class `dates` described below. Basic operations can be performed on dates:

plus binary operator (+)

> An integer scalar, interpreted as a number of periods, can be added to a date. For instance, if `a = 1950Q1` then `b = 1951Q2` and `b = a + 5` are identical.

plus unary operator (+)

> Increments a date by one period. `+1950Q1` is identical to `1950Q2`, `++++1950Q1` is identical to `1951Q1`.

minus binary operator (-)

> Has two functions: difference and subtraction. If the second argument is a date, calculates the difference between the first date and the second date (e.g. `1951Q2-1950Q1` is equal to `5`). If the second argument is an integer `X`, subtracts `X` periods from the date (e.g. `1951Q2-2` is equal to `1950Q4`).

minus unary operator (-)

> Subtracts one period to a date. `-1950Q1` is identical to `1949Q4`. The unary minus operator is the reciprocal of the unary plus operator, `+-1950Q1` is identical to `1950Q1`.

colon operator (:)

> Can be used to create a range of dates. For instance, `r = 1950Q1:1951Q1` creates a `dates` object with five elements: `1950Q1`, `1950Q2`, `1950Q3`, `1950Q4` and `1951Q1`. By default the increment between each element is one period. This default can be changed using, for instance, the following instruction: `1950Q1:2:1951Q1` which will instantiate a `dates` object with three elements: `1950Q1`, `1950Q3` and `1951Q1`.

horzcat operator ([,])

> Concatenates dates objects without removing repetitions. For instance `[1950Q1, 1950Q2]` is a `dates` object with two elements (`1950Q1` and `1950Q2`).

vertcat operator ([;])

> Same as `horzcat` operator.

eq operator (equal, ==)

> Tests if two `dates` objects are equal. `+1950Q1==1950Q2` returns `true`, `1950Q1==1950Q2` returns `false`. If the compared objects have both `n>1` elements, the `eq` operator returns a column vector, `n` by `1`, of logicals.

ne operator (not equal, ~=)

> Tests if two `dates` objects are not equal. `+1950Q1~=` returns `false` while `1950Q1~=1950Q2` returns `true`. If the compared objects both have `n>1` elements, the `ne` operator returns an `n` by `1` column vector of logicals.

lt operator (less than, <)

> Tests if a `dates` object preceeds another `dates` object. For instance, `1950Q1<1950Q3` returns `true`. If the compared objects have both `n>1` elements, the `lt` operator returns a column vector, `n` by `1`, of logicals.

gt operator (greater than, >)

> Tests if a `dates` object follows another `dates` object. For instance, `1950Q1>1950Q3` returns `false`. If the compared objects have both `n>1` elements, the `gt` operator returns a column vector, `n` by `1`, of logicals.

le operator (less or equal, <=)

> Tests if a `dates` object preceeds another `dates` object or is equal to this object. For instance, `1950Q1<=1950Q3` returns `true`. If the compared objects have both `n>1` elements, the `le` operator returns a column vector, `n` by `1`, of logicals.

ge operator (greater or equal, >=)

> Tests if a `dates` object follows another `dates` object or is equal to this object. For instance, `1950Q1>=1950Q3` returns `false`. If the compared objects have both `n>1` elements, the `ge` operator returns a column vector, `n` by `1`, of logicals.

One can select an element, or some elements, in a `dates` object as he would extract some elements from a vector in MATLAB/Octave. Let `a = 1950Q1:1951Q1` be a `dates` object, then `a(1)==1950Q1` returns `true`, `a(end)==1951Q1` returns `true` and `a(end-1:end)` selects the two last elements of `a` (by instantiating the `dates` object `[1950Q4, 1951Q1]`).

Remark: Dynare substitutes any occurrence of dates in the `.mod` file into an instantiation of the `dates` class regardless of the context. For instance, `d = 1950Q1` will be translated as `d = dates('1950Q1');`. This automatic substitution can lead to a crash if a date is defined in a string. Typically, if the user wants to display a date:

    disp('Initial period is 1950Q1');

Dynare will translate this as:

    disp('Initial period is dates('1950Q1')');

which will lead to a crash because this expression is illegal in MATLAB. For this situation, Dynare provides the `$` escape parameter. The following expression:

    disp('Initial period is $1950Q1');

will be translated as:

    disp('Initial period is 1950Q1');

in the generated MATLAB script.

### 6.1.2. The dates class

Dynare class: dates
  Members:

    - freq – equal to 1, 2, 4, 12 or 365 (resp. for annual, bi-annual, quarterly, monthly, or daily dates).

    - time – a `n*1` array of integers, the number of periods since year 0 ().

  Each member is private, one can display the content of a member but cannot change its value directly. Note also that it is not possible to mix frequencies in a `dates` object: all the elements must have common frequency.

  The `dates` class has the following constructors:

  Constructor: dates()
  Constructor: dates(FREQ)

    Returns an empty `dates` object with a given frequency (if the constructor is called with one input argument). `FREQ` is a character equal to ’Y’ or ’A’ for annual dates, ’S’ or ’H’ for bi-annual dates, ’Q’ for quarterly dates, ’M’ for monthly dates, or ’D’ for daily dates. Note that `FREQ` is not case sensitive, so that, for instance, ’q’ is also allowed for quarterly dates. The frequency can also be set with an integer scalar equal to 1 (annual), 2 (bi-annual), 4 (quarterly), 12 (monthly), or 365 (daily). The instantiation of empty objects can be used to rename the `dates` class. For instance, if one only works with quarterly dates, object `qq` can be created as:

        qq = dates('Q')

    and a `dates` object holding the date `2009Q2`:

        d0 = qq(2009,2);

    which is much simpler if `dates` objects have to be defined programmatically. For daily dates, we would instantiate an empty daily dates object as:

        dd = dates('D')

    and a `dates` object holding the date `2020-12-31`:

        d1 = dd(2020,12,31);

  Constructor: dates(STRING)
  Constructor: dates(STRING, STRING, ...)

    Returns a `dates` object that represents a date as given by the string `STRING`. This string has to be interpretable as a date (only strings of the following forms are admitted: `'1990Y'`, `'1990A'`, `1990S1`, `1990H1`, `'1990Q1'`, `'1990M2'`, or `'2020-12-31'`), the routine `isdate` can be used to test if a string is interpretable as a date. If more than one argument is provided, they should all be dates represented as strings, the resulting `dates` object contains as many elements as arguments to the constructor. For the daily dates, the string must be of the form yyyy-mm-dd with two digits for the months (mm) and days (dd), even if the number of days or months is smaller than ten (in this case a leading 0 is required).

  Constructor: dates(DATES)
  Constructor: dates(DATES, DATES, ...)

    Returns a copy of the `dates` object `DATES` passed as input arguments. If more than one argument is provided, they should all be `dates` objects. The number of elements in the instantiated `dates` object is equal to the sum of the elements in the `dates` passed as arguments to the constructor.

  Constructor: dates(FREQ, YEAR, SUBPERIOD[, S])

    where `FREQ` is a single character (’Y’, ’A’, ’S’, ’H’, ’Q’, ’M’, ’D’) or integer (1, 2, 4, 12, or 365) specifying the frequency, `YEAR` and `SUBPERIOD` and `S` are `n*1` vectors of integers. Returns a `dates` object with `n` elements. The last argument, `S`, is only to be used for daily frequency. If `FREQ` is equal to `'Y'`, `'A'` or `1`, the third argument is not needed (because `SUBPERIOD` is necessarily a vector of ones in this case).

  Example

  >     do1 = dates('1950Q1');
  >     do2 = dates('1950Q2','1950Q3');
  >     do3 = dates(do1,do2);
  >     do4 = dates('Q',1950, 1);
  >     do5 = dates('D',1973, 1, 25);

  A `dates` object with multiple elements can be considered a one-dimensional array of dates. Standard array operations can be applied to a `dates` object:

  > - square brackets can be used to concatenate dates objects:

  >       >> A = dates('1938Q4');
  >       >> B = dates('1945Q3');
  >       >> C = [A, B];

  > - semicolons can be used to create ranges of dates:

  >       >> A = dates('2009Q2');
  >       >> B = A:A+2;
  >       >> B

  >       B = <dates: 2009Q2, 2009Q3, 2009Q4>

  > - objects can be indexed by an integer or a vector of integer:

  >       >> B(1)

  >       ans = <dates: 2009Q2>

  >       >> B(end)

  >       ans = <dates: 2009Q4>

  >       >> B(1:2)

  >       ans = <dates: 2009Q2, 2009Q3>

  A list of the available methods, by alphabetical order, is given below. Note that by default the methods do not allow in place modifications: when a method is applied to an object a new object is instantiated. For instance, to apply the method `multiplybytwo` to an object `X` we write:

      >> X = 2;
      >> Y = X.multiplybytwo();
      >> X

      2

      >> Y

      4

  or equivalently:

      >> Y = multiplybytwo(X);

  the object `X` is left unchanged, and the object `Y` is a modified copy of `X` (multiplied by two). This behaviour is altered if the name of the method is postfixed with an underscore. In this case the creation of a copy is avoided. For instance, following the previous example, we would have:

      >> X = 2;
      >> X.multiplybytwo_();
      >> X

      4

  Modifying the objects in place, with underscore methods, is particularly useful if the methods are called in loops, since this saves the object instantiation overhead.

  Method: C = append(A, B)
  Method: append_(A, B)

    Appends `dates` object `B`, or a string that can be interpreted as a date, to the `dates` object `A`. If `B` is a `dates` object it is assumed that it has no more than one element.

    Example

    >     >> D = dates('1950Q1','1950Q2');
    >     >> d = dates('1950Q3');
    >     >> E = D.append(d);
    >     >> F = D.append('1950Q3');
    >     >> isequal(E,F)

    >     ans =

    >          1
    >     >> F

    >     F = <dates: 1950Q1, 1950Q2, 1950Q3>

    >     >> D

    >     D = <dates: 1950Q1, 1950Q2>

    >     >> D.append_('1950Q3')

    >     ans = <dates: 1950Q1, 1950Q2, 1950Q3>

  Method: B = char(A)

    Overloads the MATLAB/Octave `char` function. Converts a `dates` object into a character array.

    Example

    >     >> A = dates('1950Q1');
    >     > A.char()

    >     ans =

    >     '1950Q1'

  Method: C = colon(A, B)
  Method: C = colon(A, i, B)

    Overloads the MATLAB/Octave colon (`:`) operator. A and B are `dates` objects. The optional increment `i` is a scalar integer (default value is `i=1`). This method returns a `dates` object and can be used to create ranges of dates.

    Example

    >     >> A = dates('1950Q1');
    >     >> B = dates('1951Q2');
    >     >> C = A:B

    >     C = <dates: 1950Q1, 1950Q2, 1950Q3, 1950Q4, 1951Q1>

    >     >> D = A:2:B

    >     D = <dates: 1950Q1, 1950Q3, 1951Q1>

  Method: B = copy(A)

    Returns a copy of a `dates` object.

  Method: disp(A)

    Overloads the MATLAB/Octave disp function for `dates` object.

  Method: display(A)

    Overloads the MATLAB/Octave display function for `dates` object.

    Example

    >     >> disp(B)

    >     B = <dates: 1950Q1, 1950Q2, 1950Q3, 1950Q4, 1951Q1, 1951Q2, 1951Q3, 1951Q4, 1952Q1, 1952Q2, 1952Q3>

    >     >> display(B)

    >     B = <dates: 1950Q1, 1950Q2, ..., 1952Q2, 1952Q3>

  Method: B = double(A)

    Overloads the MATLAB/Octave `double` function. `A` is a `dates` object. The method returns a floating point representation of a `dates` object, the integer and fractional parts respectively corresponding to the year and the subperiod. The fractional part is the subperiod number minus one divided by the frequency (`1`, `4`, or `12`).

    Example:

    >     >> a = dates('1950Q1'):dates('1950Q4');
    >     >> a.double()

    >     ans =

    >          1950.00
    >          1950.25
    >          1950.50
    >          1950.75

  Method: C = eq(A, B)

    Overloads the MATLAB/Octave `eq` (equal, `==`) operator. `dates` objects `A` and `B` must have the same number of elements (say, `n`). The returned argument is a `n` by `1` vector of logicals. The i-th element of `C` is equal to `true` if and only if the dates `A(i)` and `B(i)` are the same.

    Example

    >     >> A = dates('1950Q1','1951Q2');
    >     >> B = dates('1950Q1','1950Q2');
    >     >> A==B

    >     ans =

    >       2x1 logical array

    >        1
    >        0

  Method: C = ge(A, B)

    Overloads the MATLAB/Octave `ge` (greater or equal, `>=`) operator. `dates` objects `A` and `B` must have the same number of elements (say, `n`). The returned argument is a `n` by `1` vector of logicals. The i-th element of `C` is equal to `true` if and only if the date `A(i)` is posterior or equal to the date `B(i)`.

    Example

    >     >> A = dates('1950Q1','1951Q2');
    >     >> B = dates('1950Q1','1950Q2');
    >     >> A>=B

    >     ans =

    >       2x1 logical array

    >        1
    >        1

  Method: C = gt(A, B)

    Overloads the MATLAB/Octave `gt` (greater than, `>`) operator. `dates` objects `A` and `B` must have the same number of elements (say, `n`). The returned argument is a `n` by `1` vector of logicals. The i-th element of `C` is equal to `1` if and only if the date `A(i)` is posterior to the date `B(i)`.

    Example

    >     >> A = dates('1950Q1','1951Q2');
    >     >> B = dates('1950Q1','1950Q2');
    >     >> A>B

    >     ans =

    >       2x1 logical array

    >        0
    >        1

  Method: D = horzcat(A, B, C, ...)

    Overloads the MATLAB/Octave `horzcat` operator. All the input arguments must be `dates` objects. The returned argument is a `dates` object gathering all the dates given in the input arguments (repetitions are not removed).

    Example

    >     >> A = dates('1950Q1');
    >     >> B = dates('1950Q2');
    >     >> C = [A, B];
    >     >> C

    >     C = <dates: 1950Q1, 1950Q2>

  Method: C = intersect(A, B)

    Overloads the MATLAB/Octave `intersect` function. All the input arguments must be `dates` objects. The returned argument is a `dates` object gathering all the common dates given in the input arguments. If `A` and `B` are disjoint `dates` objects, the function returns an empty `dates` object. Returned dates in `dates` object `C` are sorted by increasing order.

    Example

    >     >> A = dates('1950Q1'):dates('1951Q4');
    >     >> B = dates('1951Q1'):dates('1951Q4');
    >     >> C = intersect(A, B);
    >     >> C

    >     C = <dates: 1951Q1, 1951Q2, 1951Q3, 1951Q4>

  Method: B = isempty(A)

    Overloads the MATLAB/Octave `isempty` function.

    Example

    >     >> A = dates('1950Q1');
    >     >> A.isempty()

    >     ans =

    >       logical

    >       0

    >     >> B = dates();
    >     >> B.isempty()

    >     ans =

    >       logical

    >       1

  Method: C = isequal(A, B)

    Overloads the MATLAB/Octave `isequal` function.

    Example

    >     >> A = dates('1950Q1');
    >     >> B = dates('1950Q2');
    >     >> isequal(A, B)

    >     ans =

    >       logical

    >       0

  Method: C = le(A, B)

    Overloads the MATLAB/Octave `le` (less or equal, `<=`) operator. `dates` objects `A` and `B` must have the same number of elements (say, `n`). The returned argument is a `n` by `1` vector of logicals. The i-th element of `C` is equal to `true` if and only if the date `A(i)` is anterior or equal to the date `B(i)`.

    Example

    >     >> A = dates('1950Q1','1951Q2');
    >     >> B = dates('1950Q1','1950Q2');
    >     >> A<=B

    >     ans =

    >       2x1 logical array

    >        1
    >        0

  Method: B = length(A)

    Overloads the MATLAB/Octave `length` function. Returns the number of elements in a `dates` object.

    Example

    >     >> A = dates('1950Q1'):dates(2000Q3);
    >     >> A.length()

    >     ans =

    >        203

  Method: C = lt(A, B)

    Overloads the MATLAB/Octave `lt` (less than, `<`) operator. `dates` objects `A` and `B` must have the same number of elements (say, `n`). The returned argument is a `n` by `1` vector of logicals. The i-th element of `C` is equal to `true` if and only if the date `A(i)` is anterior or equal to the date `B(i)`.

    Example

    >     >> A = dates('1950Q1','1951Q2');
    >     >> B = dates('1950Q1','1950Q2');
    >     >> A<B

    >     ans =

    >       2x1 logical array

    >        0
    >        0

  Method: D = max(A, B, C, ...)

    Overloads the MATLAB/Octave `max` function. All input arguments must be `dates` objects. The function returns a single element `dates` object containing the greatest date.

    Example

    >     >> A = {dates('1950Q2'), dates('1953Q4','1876Q2'), dates('1794Q3')};
    >     >> max(A{:})

    >     ans = <dates: 1953Q4>

  Method: D = min(A, B, C, ...)

    Overloads the MATLAB/Octave `min` function. All input arguments must be `dates` objects. The function returns a single element `dates` object containing the smallest date.

    Example

    >     >> A = {dates('1950Q2'), dates('1953Q4','1876Q2'), dates('1794Q3')};
    >     >> min(A{:})

    >     ans = <dates: 1794Q3>

  Method: C = minus(A, B)

    Overloads the MATLAB/Octave `minus` operator (`-`). If both input arguments are `dates` objects, then number of periods between `A` and `B` is returned (so that `A+C=B`). If `B` is a vector of integers, the minus operator shifts the `dates` object by `B` periods backward.

    Example

    >     >> d1 = dates('1950Q1','1950Q2','1960Q1');
    >     >> d2 = dates('1950Q3','1950Q4','1960Q1');
    >     >> ee = d2-d1

    >     ee =

    >          2
    >          2
    >          0

    >     >> d1-(-ee)

    >     ans = <dates: 1950Q3, 1950Q4, 1960Q1>

  Method: C = mtimes(A, B)

    Overloads the MATLAB/Octave `mtimes` operator (`*`). `A` and `B` are respectively expected to be a `dates` object and a scalar integer. Returns `dates` object `A` replicated `B` times.

    Example

    >     >> d = dates('1950Q1');
    >     >> d*2

    >     ans = <dates: 1950Q1, 1950Q1>

  Method: C = ne(A, B)

    Overloads the MATLAB/Octave `ne` (not equal, `~=`) operator. `dates` objects `A` and `B` must have the same number of elements (say, `n`) or one of the inputs must be a single element `dates` object. The returned argument is a `n` by `1` vector of logicals. The i-th element of `C` is equal to `true` if and only if the dates `A(i)` and `B(i)` are different.

    Example

    >     >> A = dates('1950Q1','1951Q2');
    >     >> B = dates('1950Q1','1950Q2');
    >     >> A~=B

    >     ans =

    >       2x1 logical array

    >        0
    >        1

  Method: C = plus(A, B)

    Overloads the MATLAB/Octave `plus` operator (`+`). If both input arguments are `dates` objects, then the method combines `A` and `B` without removing repetitions. If `B` is a vector of integers, the `plus` operator shifts the `dates` object by `B` periods forward.

    Example

    >     >> d1 = dates('1950Q1','1950Q2')+dates('1960Q1');
    >     >> d2 = (dates('1950Q1','1950Q2')+2)+dates('1960Q1');
    >     >> ee = d2-d1;

    >     ee =

    >          2
    >          2
    >          0

    >     >> d1+ee
    >     ans = <dates: 1950Q3, 1950Q4, 1960Q1>

  Method: C = pop(A)
  Method: C = pop(A, B)
  Method: pop_(A)
  Method: pop_(A, B)

    Pop method for `dates` class. If only one input is provided, the method removes the last element of a `dates` object. If a second input argument is provided, a scalar integer between `1` and `A.length()`, the method removes element number `B` from `dates` object `A`.

    Example

    >     >> d = dates('1950Q1','1950Q2');
    >     >> d.pop()

    >     ans = <dates: 1950Q1>

    >     >> d.pop_(1)

    >     ans = <dates: 1950Q2>

  Method: C = remove(A, B)
  Method: remove_(A, B)

    Remove method for `dates` class. Both inputs have to be `dates` objects, removes dates in `B` from `A`.

    Example

    >     >> d = dates('1950Q1','1950Q2');
    >     >> d.remove(dates('1950Q2'))

    >     ans = <dates: 1950Q1>

  Method: C = setdiff(A, B)

    Overloads the MATLAB/Octave `setdiff` function. All the input arguments must be `dates` objects. The returned argument is a `dates` object all dates present in `A` but not in `B`. If `A` and `B` are disjoint `dates` objects, the function returns `A`. Returned dates in `dates` object `C` are sorted by increasing order.

    Example

    >     >> A = dates('1950Q1'):dates('1969Q4');
    >     >> B = dates('1960Q1'):dates('1969Q4');
    >     >> C = dates('1970Q1'):dates('1979Q4');
    >     >> setdiff(A, B)

    >     ans = <dates: 1950Q1, 1950Q2,  ..., 1959Q3, 1959Q4>

    >     >> setdiff(A, C)

    >     ans = <dates: 1950Q1, 1950Q2,  ..., 1969Q3, 1969Q4>

  Method: B = sort(A)
  Method: sort_(A)

    Sort method for `dates` objects. Returns a `dates` object with elements sorted by increasing order.

    Example

    >     >> dd = dates('1945Q3','1938Q4','1789Q3');
    >     >> dd.sort()

    >     ans = <dates: 1789Q3, 1938Q4, 1945Q3>

  Method: B = strings(A)

    Converts a `dates` object into a cell of char arrays.

    Example

    >     >> A = dates('1950Q1');
    >     >> A = A:A+1;
    >     >> A.strings()

    >       ans =

    >         1x2 cell array

    >         {'1950Q1'}    {'1950Q2'}

  Method: B = subperiod(A)

    Returns the subperiod of a date (an integer scalar between 1 and `A.freq`). This method is not implemented for daily dates.

    Example

    >     >> A = dates('1950Q2');
    >     >> A.subperiod()

    >     ans =

    >          2

  Method: B = uminus(A)

    Overloads the MATLAB/Octave unary minus operator. Returns a `dates` object with elements shifted one period backward.

    Example

        >> dd = dates('1945Q3','1938Q4','1973Q1');
        >> -dd

        ans = <dates: 1945Q2, 1938Q3, 1972Q4>

  Method: D = union(A, B, C, ...)

    Overloads the MATLAB/Octave `union` function. Returns a `dates` object with elements sorted by increasing order (repetitions are removed, to keep the repetitions use the `horzcat` or `plus` operators).

    Example

    >     >> d1 = dates('1945Q3','1973Q1','1938Q4');
    >     >> d2 = dates('1973Q1','1976Q1');
    >     >> union(d1,d2)

    >     ans = <dates: 1938Q4, 1945Q3, 1973Q1, 1976Q1>

  Method: B = unique(A)
  Method: unique_(A)

    Overloads the MATLAB/Octave `unique` function. Returns a `dates` object with repetitions removed (only the last occurence of a date is kept).

    Example

    >     >> d1 = dates('1945Q3','1973Q1','1945Q3');
    >     >> d1.unique()

    >     ans = <dates: 1973Q1, 1945Q3>

  Method: B = uplus(A)

    Overloads the MATLAB/Octave unary plus operator. Returns a `dates` object with elements shifted one period ahead.

    Example

    >     >> dd = dates('1945Q3','1938Q4','1973Q1');
    >     >> +dd

    >     ans = <dates: 1945Q4, 1939Q1, 1973Q2>

  Method: D = vertcat(A, B, C, ...)

    Overloads the MATLAB/Octave `horzcat` operator. All the input arguments must be `dates` objects. The returned argument is a `dates` object gathering all the dates given in the input arguments (repetitions are not removed).

  Method: B = year(A)

    Returns the year of a date (an integer scalar between 1 and `A.freq`).

    Example

    >     >> A = dates('1950Q2');
    >     >> A.subperiod()

    >     ans =

    >            1950

## 6.2. The dseries class

Dynare class: dseries

  > The MATLAB/Octave `dseries` class handles time series data. As any MATLAB/Octave statements, this class can be used in a Dynare’s mod file. A `dseries` object has six members:

  > arg name:

  >   A `vobs*1` cell of strings or a `vobs*p` character array, the names of the variables.

  > arg tex:

  >   A `vobs*1` cell of strings or a `vobs*p` character array, the tex names of the variables.

  > arg dates dates:

  >   An object with `nobs` elements, the dates of the sample.

  > arg double data:

  >   A `nobs` by `vobs` array, the data.

  > arg ops:

  >   The history of operations on the variables.

  > arg tags:

  >   The user-defined tags on the variables.

  > `data`, `name`, `tex`, and `ops` are private members. The following constructors are available:

  > Constructor: dseries()
  > Constructor: dseries(INITIAL_DATE)

  >   Instantiates an empty `dseries` object with, if defined, an initial date given by the single element `dates` object INITIAL_DATE.

  > Constructor: dseries(FILENAME[, INITIAL_DATE])

  >   Instantiates and populates a `dseries` object with a data file specified by FILENAME, a string passed as input. Valid file types are `.m`, `.mat`, `.csv` and `.xls/.xlsx` (Octave only supports `.xlsx` files and the io package from Octave-Forge must be installed). The extension of the file should be explicitly provided.

  >   A typical `.m` file will have the following form:

  >       FREQ__ = 4;
  >       INIT__ = '1994Q3';
  >       NAMES__ = {'azert';'yuiop'};
  >       TEX__ = {'azert';'yuiop'};

  >       azert = randn(100,1);
  >       yuiop = randn(100,1);

  >   If a `.mat` file is used instead, it should provide the same informations, except that the data should not be given as a set of vectors, but as a single matrix of doubles named `DATA__`. This array should have as many columns as elements in `NAMES__` (the number of variables). Note that the `INIT__` variable can be either a `dates` object or a string which could be used to instantiate the same `dates` object. If `INIT__` is not provided in the `.mat` or `.m` file, the initial is by default set equal to `dates('1Y')`. If a second input argument is passed to the constructor, `dates` object INITIAL_DATE, the initial date defined in FILENAME is reset to INITIAL_DATE. This is typically usefull if `INIT__` is not provided in the data file.

  >   If an `.xlsx` file is used, the first row should be a header containing the variable names. The first column may contain date information that must correspond to a valid date format recognized by Dynare. If such date information is specified in the first column, its header name must be left empty.

  > Constructor: dseries(DATA_MATRIX[,INITIAL_DATE[,LIST_OF_NAMES[,TEX_NAMES]]])
  > Constructor: dseries(DATA_MATRIX[,RANGE_OF_DATES[,LIST_OF_NAMES[,TEX_NAMES]]])

  >   If the data is not read from a file, it can be provided via a \(T \times N\) matrix as the first argument to `dseries` ’ constructor, with \(T\) representing the number of observations on \(N\) variables. The optional second argument, INITIAL_DATE, can be either a `dates` object representing the period of the first observation or a string which would be used to instantiate a `dates` object. Its default value is `dates('1Y')`. The optional third argument, LIST_OF_NAMES, is a \(N \times 1\) cell of strings with one entry for each variable name. The default name associated with column `i` of DATA_MATRIX is `Variable_i`. The final argument, TEX_NAMES, is a \(N \times 1\) cell of strings composed of the LaTeX names associated with the variables. The default LaTeX name associated with column `i` of DATA_MATRIX is `Variable\_i`. If the optional second input argument is a range of dates, `dates` object RANGE_OF_DATES, the number of rows in the first argument must match the number of elements RANGE_OF_DATES or be equal to one (in which case the single observation is replicated).

  > Constructor: dseries(TABLE)

  >   Creates a `dseries` object given the MATLAB Table provided as the sole argument. It is assumed that the first column of the table contains the dates of the `dseries` and the first row contains the names. This feature is not available under Octave or MATLAB R2013a or earlier.

  >   Example

  >   Various ways to create a `dseries` object:

  >       do1 = dseries(1999Q3);
  >       do2 = dseries('filename.csv');
  >       do3 = dseries([1; 2; 3], 1999Q3, {'var123'}, {'var_{123}'});

  >       >> do1 = dseries(dates('1999Q3'));
  >       >> do2 = dseries('filename.csv');
  >       >> do3 = dseries([1; 2; 3], dates('1999Q3'), {'var123'}, {'var_{123}'});

  > One can easily create subsamples from a `dseries` object using the overloaded parenthesis operator. If `ds` is a `dseries` object with \(T\) observations and `d` is a `dates` object with \(S<T\) elements, such that \(\min(d)\) is not smaller than the date associated to the first observation in `ds` and \(\max(d)\) is not greater than the date associated to the last observation, then `ds(d)` instantiates a new `dseries` object containing the subsample defined by `d`.

  > A list of the available methods, by alphabetical order, is given below. As in the previous section the in place modifications versions of the methods are postfixed with an underscore.

  > Method: A = abs(B)
  > Method: abs_(B)

  >   Overloads the `abs()` function for `dseries` objects. Returns the absolute value of the variables in dseries `object` `B`.

  >   Example

  >   >     >> ts0 = dseries(randn(3,2),'1973Q1',{'A1'; 'A2'},{'A_1'; 'A_2'});
  >   >     >> ts1 = ts0.abs();
  >   >     >> ts0

  >   >     ts0 is a dseries object:

  >   >            | A1       | A2
  >   >     1973Q1 | -0.67284 | 1.4367
  >   >     1973Q2 | -0.51222 | -0.4948
  >   >     1973Q3 | 0.99791  | 0.22677

  >   >     >> ts1

  >   >     ts1 is a dseries object:

  >   >            | abs(A1) | abs(A2)
  >   >     1973Q1 | 0.67284 | 1.4367
  >   >     1973Q2 | 0.51222 | 0.4948
  >   >     1973Q3 | 0.99791 | 0.22677

  >   Example (in-place modification version)

  >   >     >> ts0 = dseries(randn(3,2),'1973Q1',{'A1'; 'A2'},{'A_1'; 'A_2'});
  >   >     >> ts0

  >   >     ts0 is a dseries object:

  >   >            | A1       | A2
  >   >     1973Q1 | -0.67284 | 1.4367
  >   >     1973Q2 | -0.51222 | -0.4948
  >   >     1973Q3 | 0.99791  | 0.22677

  >   >     >> ts0.abs_();
  >   >     >> ts0

  >   >     ts0 is a dseries object:

  >   >            | abs(A1) | abs(A2)
  >   >     1973Q1 | 0.67284 | 1.4367
  >   >     1973Q2 | 0.51222 | 0.4948
  >   >     1973Q3 | 0.99791 | 0.22677

  > Method: [A, B] = align(A, B)
  > Method: align_(A, B)

  >   If `dseries` objects `A` and `B` are defined on different time ranges, this function extends `A` and/or `B` with NaNs so that they are defined on the same time range. Note that both `dseries` objects must have the same frequency.

  >   Example

  >   >     >> ts0 = dseries(rand(5,1),dates('2000Q1')); % 2000Q1 -> 2001Q1
  >   >     >> ts1 = dseries(rand(3,1),dates('2000Q4')); % 2000Q4 -> 2001Q2
  >   >     >> [ts0, ts1] = align(ts0, ts1);             % 2000Q1 -> 2001Q2
  >   >     >> ts0

  >   >     ts0 is a dseries object:

  >   >            | Variable_1
  >   >     2000Q1 | 0.81472
  >   >     2000Q2 | 0.90579
  >   >     2000Q3 | 0.12699
  >   >     2000Q4 | 0.91338
  >   >     2001Q1 | 0.63236
  >   >     2001Q2 | NaN

  >   >     >> ts1

  >   >     ts1 is a dseries object:

  >   >            | Variable_1
  >   >     2000Q1 | NaN
  >   >     2000Q2 | NaN
  >   >     2000Q3 | NaN
  >   >     2000Q4 | 0.66653
  >   >     2001Q1 | 0.17813
  >   >     2001Q2 | 0.12801

  >   >     >> ts0 = dseries(rand(5,1),dates('2000Q1')); % 2000Q1 -> 2001Q1
  >   >     >> ts1 = dseries(rand(3,1),dates('2000Q4')); % 2000Q4 -> 2001Q2
  >   >     >> align_(ts0, ts1);                         % 2000Q1 -> 2001Q2
  >   >     >> ts1

  >   >     ts1 is a dseries object:

  >   >            | Variable_1
  >   >     2000Q1 | NaN
  >   >     2000Q2 | NaN
  >   >     2000Q3 | NaN
  >   >     2000Q4 | 0.66653
  >   >     2001Q1 | 0.17813
  >   >     2001Q2 | 0.12801

  >   Example (in-place modification version)

  >   >     >> ts0 = dseries(rand(5,1),dates('2000Q1')); % 2000Q1 -> 2001Q1
  >   >     >> ts1 = dseries(rand(3,1),dates('2000Q4')); % 2000Q4 -> 2001Q2
  >   >     >> ts0

  >   >     ts0 is a dseries object:

  >   >            | Variable_1
  >   >     2000Q1 | 0.80028
  >   >     2000Q2 | 0.14189
  >   >     2000Q3 | 0.42176
  >   >     2000Q4 | 0.91574
  >   >     2001Q1 | 0.79221

  >   >     >> ts1

  >   >     ts1 is a dseries object:

  >   >            | Variable_1
  >   >     2000Q4 | 0.95949
  >   >     2001Q1 | 0.65574
  >   >     2001Q2 | 0.035712

  >   >     >> align_(ts0, ts1);                         % 2000Q1 -> 2001Q2
  >   >     >> ts0

  >   >     ts0 is a dseries object:

  >   >            | Variable_1
  >   >     2000Q1 | 0.80028
  >   >     2000Q2 | 0.14189
  >   >     2000Q3 | 0.42176
  >   >     2000Q4 | 0.91574
  >   >     2001Q1 | 0.79221
  >   >     2001Q2 | NaN

  >   >     >> ts1

  >   >     ts1 is a dseries object:

  >   >            | Variable_1
  >   >     2000Q1 | NaN
  >   >     2000Q2 | NaN
  >   >     2000Q3 | NaN
  >   >     2000Q4 | 0.95949
  >   >     2001Q1 | 0.65574
  >   >     2001Q2 | 0.035712

  > Method: C = backcast(A, B[, diff])
  > Method: backcast_(A, B[, diff])

  >   Backcasts `dseries` object `A` with `dseries` object B’s growth rates (except if the last optional argument, `diff`, is true in which case first differences are used). Both `dseries` objects must have the same frequency.

  > Method: B = baxter_king_filter(A[, hf[, lf[, K]]])
  > Method: baxter_king_filter_(A[, hf[, lf[, K]]])

  >   Implementation of the Baxter and King (1999) band pass filter for `dseries` objects. This filter isolates business cycle fluctuations with a period of length ranging between `hf` (high frequency) to `lf` (low frequency) using a symmetric moving average smoother with \(2K+1\) points, so that \(K\) observations at the beginning and at the end of the sample are lost in the computation of the filter. The default value for `hf` is `6`, for `lf` is `32`, and for `K` is `12`.

  >   Example

  >   >     % Simulate a component model (stochastic trend, deterministic
  >   >     % trend, and a stationary autoregressive process).
  >   >     e = 0.2*randn(200,1);
  >   >     u = randn(200,1);
  >   >     stochastic_trend = cumsum(e);
  >   >     deterministic_trend = .1*transpose(1:200);
  >   >     x = zeros(200,1);
  >   >     for i=2:200
  >   >         x(i) = .75*x(i-1) + u(i);
  >   >     end
  >   >     y = x + stochastic_trend + deterministic_trend;

  >   >     % Instantiates time series objects.
  >   >     ts0 = dseries(y,'1950Q1');
  >   >     ts1 = dseries(x,'1950Q1'); % stationary component.

  >   >     % Apply the Baxter-King filter.
  >   >     ts2 = ts0.baxter_king_filter();

  >   >     % Plot the filtered time series.
  >   >     plot(ts1(ts2.dates).data,'-k'); % Plot of the stationary component.
  >   >     hold on
  >   >     plot(ts2.data,'--r');           % Plot of the filtered y.
  >   >     hold off
  >   >     axis tight
  >   >     id = get(gca,'XTick');
  >   >     set(gca,'XTickLabel',strings(ts1.dates(id)));

  > Method: B = center(A[, geometric])
  > Method: center_(A[, geometric])

  >   Centers variables in `dseries` object `A` around their arithmetic means, except if the optional argument `geometric` is set equal to `true` in which case all the variables are divided by their geometric means.

  > Method: C = chain(A, B)
  > Method: chain_(A, B)

  >   Merge two `dseries` objects along the time dimension. The two objects must have the same number of observed variables, and the initial date in `B` must not be posterior to the last date in `A`. The returned `dseries` object, `C`, is built by extending `A` with the cumulated growth factors of `B`.

  >   Example

  >   >     >> ts = dseries([1; 2; 3; 4],dates('1950Q1'))

  >   >     ts is a dseries object:

  >   >            | Variable_1
  >   >     1950Q1 | 1
  >   >     1950Q2 | 2
  >   >     1950Q3 | 3
  >   >     1950Q4 | 4

  >   >     >> us = dseries([3; 4; 5; 6],dates('1950Q3'))

  >   >     us is a dseries object:

  >   >            | Variable_1
  >   >     1950Q3 | 3
  >   >     1950Q4 | 4
  >   >     1951Q1 | 5
  >   >     1951Q2 | 6

  >   >     >> chain(ts, us)

  >   >     ans is a dseries object:

  >   >            | Variable_1
  >   >     1950Q1 | 1
  >   >     1950Q2 | 2
  >   >     1950Q3 | 3
  >   >     1950Q4 | 4
  >   >     1951Q1 | 5
  >   >     1951Q2 | 6

  >   Example (in-place modification version)

  >   >     >> ts = dseries([1; 2; 3; 4],dates('1950Q1'))
  >   >     >> us = dseries([3; 4; 5; 6],dates('1950Q3'))
  >   >     >> ts.chain_(us);
  >   >     >> ts

  >   >     ts is a dseries object:

  >   >            | Variable_1
  >   >     1950Q1 | 1
  >   >     1950Q2 | 2
  >   >     1950Q3 | 3
  >   >     1950Q4 | 4
  >   >     1951Q1 | 5
  >   >     1951Q2 | 6

  > Method: [error_flag, message ] = check(A)

  >   Sanity check of `dseries` object `A`. Returns `1` if there is an error, `0` otherwise. The second output argument is a string giving brief informations about the error.

  > Method: B = copy(A)

  >   Returns a copy of `A`. If an inplace modification method is applied to `A`, object `B` will not be affected. Note that if `A` is assigned to `C`, `C = A`, then any in place modification method applied to `A` will change `C`.

  >   Example

  >   >     >> a = dseries(randn(5,1))

  >   >     a is a dseries object:

  >   >        | Variable_1
  >   >     1Y | -0.16936
  >   >     2Y | -1.1451
  >   >     3Y | -0.034331
  >   >     4Y | -0.089042
  >   >     5Y | -0.66997

  >   >     >> b = copy(a);
  >   >     >> c = a;
  >   >     >> a.abs();
  >   >     >> a.abs_();
  >   >     >> a

  >   >     a is a dseries object:

  >   >        | Variable_1
  >   >     1Y | 0.16936
  >   >     2Y | 1.1451
  >   >     3Y | 0.034331
  >   >     4Y | 0.089042
  >   >     5Y | 0.66997

  >   >     >> b

  >   >     b is a dseries object:

  >   >        | Variable_1
  >   >     1Y | -0.16936
  >   >     2Y | -1.1451
  >   >     3Y | -0.034331
  >   >     4Y | -0.089042
  >   >     5Y | -0.66997

  >   >     >> c

  >   >     c is a dseries object:

  >   >        | Variable_1
  >   >     1Y | 0.16936
  >   >     2Y | 1.1451
  >   >     3Y | 0.034331
  >   >     4Y | 0.089042
  >   >     5Y | 0.66997

  > Method: B = cumprod(A[, d[, v]])
  > Method: cumprod_(A[, d[, v]])

  >   Overloads the MATLAB/Octave `cumprod` function for `dseries` objects. The cumulated product cannot be computed if the variables in `dseries` object `A` have NaNs. If a `dates` object `d` is provided as a second argument, then the method computes the cumulated product with the additional constraint that the variables in the `dseries` object `B` are equal to one in period `d`. If a single-observation `dseries` object `v` is provided as a third argument, the cumulated product in `B` is normalized such that `B(d)` matches `v` (`dseries` objects `A` and `v` must have the same number of variables).

  >   Example

  >   >     >> ts1 = dseries(2*ones(7,1));
  >   >     >> ts2 = ts1.cumprod();
  >   >     >> ts2

  >   >     ts2 is a dseries object:

  >   >        | cumprod(Variable_1)
  >   >     1Y | 2
  >   >     2Y | 4
  >   >     3Y | 8
  >   >     4Y | 16
  >   >     5Y | 32
  >   >     6Y | 64
  >   >     7Y | 128

  >   >     >> ts3 = ts1.cumprod(dates('3Y'));
  >   >     >> ts3

  >   >     ts3 is a dseries object:

  >   >        | cumprod(Variable_1)
  >   >     1Y | 0.25
  >   >     2Y | 0.5
  >   >     3Y | 1
  >   >     4Y | 2
  >   >     5Y | 4
  >   >     6Y | 8
  >   >     7Y | 16

  >   >     >> ts4 = ts1.cumprod(dates('3Y'),dseries(pi));
  >   >     >> ts4

  >   >     ts4 is a dseries object:

  >   >        | cumprod(Variable_1)
  >   >     1Y | 0.7854
  >   >     2Y | 1.5708
  >   >     3Y | 3.1416
  >   >     4Y | 6.2832
  >   >     5Y | 12.5664
  >   >     6Y | 25.1327
  >   >     7Y | 50.2655

  > Method: B = cumsum(A[, d[, v]])
  > Method: cumsum_(A[, d[, v]])

  >   Overloads the MATLAB/Octave `cumsum` function for `dseries` objects. The cumulated sum cannot be computed if the variables in `dseries` object `A` have NaNs. If a `dates` object `d` is provided as a second argument, then the method computes the cumulated sum with the additional constraint that the variables in the `dseries` object `B` are zero in period `d`. If a single observation `dseries` object `v` is provided as a third argument, the cumulated sum in `B` is such that `B(d)` matches `v` (`dseries` objects `A` and `v` must have the same number of variables).

  >   Example

  >   >     >> ts1 = dseries(ones(10,1));
  >   >     >> ts2 = ts1.cumsum();
  >   >     >> ts2

  >   >     ts2 is a dseries object:

  >   >         | cumsum(Variable_1)
  >   >     1Y  | 1
  >   >     2Y  | 2
  >   >     3Y  | 3
  >   >     4Y  | 4
  >   >     5Y  | 5
  >   >     6Y  | 6
  >   >     7Y  | 7
  >   >     8Y  | 8
  >   >     9Y  | 9
  >   >     10Y | 10

  >   >     >> ts3 = ts1.cumsum(dates('3Y'));
  >   >     >> ts3

  >   >     ts3 is a dseries object:

  >   >         | cumsum(Variable_1)
  >   >     1Y  | -2
  >   >     2Y  | -1
  >   >     3Y  | 0
  >   >     4Y  | 1
  >   >     5Y  | 2
  >   >     6Y  | 3
  >   >     7Y  | 4
  >   >     8Y  | 5
  >   >     9Y  | 6
  >   >     10Y | 7

  >   >     >> ts4 = ts1.cumsum(dates('3Y'),dseries(pi));
  >   >     >> ts4

  >   >     ts4 is a dseries object:

  >   >         | cumsum(Variable_1)
  >   >     1Y  | 1.1416
  >   >     2Y  | 2.1416
  >   >     3Y  | 3.1416
  >   >     4Y  | 4.1416
  >   >     5Y  | 5.1416
  >   >     6Y  | 6.1416
  >   >     7Y  | 7.1416
  >   >     8Y  | 8.1416
  >   >     9Y  | 9.1416
  >   >     10Y | 10.1416

  > Method: B = detrend(A[, m])
  > Method: detrend_(A[, m])

  >   Detrends `dseries` object `A` with a fitted polynomial of order `m`. Default value fir `m` is 0 (time series are detrended by removing the average). Note that each variable is detrended with a different polynomial.

  > Method: B = dgrowth(A)
  > Method: dgrowth_(A)

  >   Computes daily growth rates.

  > Method: B = diff(A)
  > Method: diff_(A)

  >   Returns the first difference of `dseries` object `A`.

  > Method: disp(A)

  >   Overloads the MATLAB/Octave disp function for `dseries` object.

  > Method: display(A)

  >   Overloads the MATLAB/Octave display function for `dseries` object. `display` is the function called by MATLAB to print the content of an object if a semicolon is missing at the end of a MATLAB statement. If the `dseries` object is defined over a too large time span, only the first and last periods will be printed. If the `dseries` object contains too many variables, only the first and last variables will be printed. If all the periods and variables are required, the `disp` method should be used instead.

  > Method: C = eq(A, B)

  >   Overloads the MATLAB/Octave `eq` (equal, `==`) operator. `dseries` objects `A` and `B` must have the same number of observations (say, \(T\)) and variables (\(N\)). The returned argument is a \(T \times N\) matrix of logicals. Element \((i,j)\) of `C` is equal to `true` if and only if observation \(i\) for variable \(j\) in `A` and `B` are the same.

  >   Example

  >   >     >> ts0 = dseries(2*ones(3,1));
  >   >     >> ts1 = dseries([2; 0; 2]);
  >   >     >> ts0==ts1

  >   >     ans =

  >   >        3x1 logical array

  >   >         1
  >   >         0
  >   >         1

  > Method: l = exist(A, varname)

  >   Tests if variable `varname` exists in `dseries` object `A`. Returns `true` iff variable exists in `A`.

  >   Example

  >   >     >> ts = dseries(randn(100,1));
  >   >     >> ts.exist('Variable_1')

  >   >     ans =

  >   >        logical

  >   >         1

  >   >     >> ts.exist('Variable_2')

  >   >     ans =

  >   >        logical

  >   >         0

  > Method: B = exp(A)
  > Method: exp_(A)

  >   Overloads the MATLAB/Octave `exp` function for `dseries` objects.

  >   Example

  >   >     >> ts0 = dseries(rand(10,1));
  >   >     >> ts1 = ts0.exp();

  >   Exemple (in-place modification version)

  >   >     >> ts0 = dseries(rand(3,1))

  >   >     ts0 is a dseries object:

  >   >        | Variable_1
  >   >     1Y | 0.82953
  >   >     2Y | 0.84909
  >   >     3Y | 0.37253

  >   >     >> ts0.exp_();
  >   >     >> ts0

  >   >     ts0 is a dseries object:

  >   >        | Variable_1
  >   >     1Y | 2.2922
  >   >     2Y | 2.3375
  >   >     3Y | 1.4514

  > Method: C = extract(A, B[, ...])

  >   Extracts some variables from a `dseries` object `A` and returns a `dseries` object `C`. The input arguments following `A` are strings representing the variables to be selected in the new `dseries` object `C`. To simplify the creation of sub-objects, the `dseries` class overloads the curly braces (`D = extract (A, B, C)` is equivalent to `D = A{B,C}`) and allows implicit loops (defined between a pair of `@` symbol, see examples below) or MATLAB/Octave’s regular expressions (introduced by square brackets).

  >   Example

  >   > The following selections are equivalent:

  >   >     >> ts0 = dseries(ones(100,10));
  >   >     >> ts1 = ts0{'Variable_1','Variable_2','Variable_3'};
  >   >     >> ts2 = ts0{'Variable_@1,2,3@'};
  >   >     >> ts3 = ts0{'Variable_[1-3]$'};
  >   >     >> isequal(ts1,ts2) && isequal(ts1,ts3)

  >   >     ans =

  >   >        logical

  >   >         1

  >   > It is possible to use up to two implicit loops to select variables:

  >   >     names = {'GDP_1';'GDP_2';'GDP_3'; 'GDP_4'; 'GDP_5'; 'GDP_6'; 'GDP_7'; 'GDP_8'; ...
  >   >         'GDP_9'; 'GDP_10'; 'GDP_11'; 'GDP_12'; ...
  >   >         'HICP_1';'HICP_2';'HICP_3'; 'HICP_4'; 'HICP_5'; 'HICP_6'; 'HICP_7'; 'HICP_8'; ...
  >   >         'HICP_9'; 'HICP_10'; 'HICP_11'; 'HICP_12'};

  >   >     ts0 = dseries(randn(4,24),dates('1973Q1'),names);
  >   >     ts0{'@GDP,HICP@_@1,3,5@'}

  >   >     ans is a dseries object:

  >   >            | GDP_1    | GDP_3     | GDP_5     | HICP_1   | HICP_3   | HICP_5
  >   >     1973Q1 | 1.7906   | -1.6606   | -0.57716  | 0.60963  | -0.52335 | 0.26172
  >   >     1973Q2 | 2.1624   | 3.0125    | 0.52563   | 0.70912  | -1.7158  | 1.7792
  >   >     1973Q3 | -0.81928 | 1.5008    | 1.152     | 0.2798   | 0.88568  | 1.8927
  >   >     1973Q4 | -0.03705 | -0.35899  | 0.85838   | -1.4675  | -2.1666  | -0.62032

  > Method: fill_(name, v)

  >   Assign the value `v` to the variable `name` in a dseries object. If `name` is a character row array, it should correspond to an existing variable within the dseries object. When `v` is a scalar, its value will be applied to all periods uniformly. If `v` is a vector, its length must match the number of observations in the dseries object.You can invoke this method for a batch of variables by providing a 1 by n cell array of character row arrays as the first argument. When “v” is a row vector with n elements, the method will be applied uniformly across all periods. If “v” is a matrix, it must have n columns, and the number of rows should correspond to the number of periods.

  >   Example

  >   >     >> ts = dseries(rand(3,3));
  >   >     >> ts.fill_({'Variable_1', 'Variable_3'}, [1 3]);
  >   >     >> ts

  >   >     ts is a dseries object:

  >   >        | Variable_1 | Variable_2 | Variable_3
  >   >     1Y | 1          | 0.91338    | 3
  >   >     2Y | 1          | 0.63236    | 3
  >   >     3Y | 1          | 0.09754    | 3

  > Method: f = firstdate(A)

  >   Returns the initial period in the `dseries` object `A`.

  > Method: f = firstobservedperiod(A)

  >   Returns the first period where all the variables in `dseries` object `A` are observed (non NaN).

  > Method: B = flip(A)
  > Method: flip_(A)

  >   Flips the rows in the data member (without changing the periods order).

  > Method: f = frequency(B)

  >   Returns the frequency of the variables in `dseries` object `B`.

  >   Example

  >   >     >> ts = dseries(randn(3,2),'1973Q1');
  >   >     >> ts.frequency

  >   >     ans =

  >   >          4

  > Method: l = ge(A, B)
  > Method: l = gt(A, B)

  >   Overloads the `gt` (>) and `ge` (>=) binary operators. Returns a logical array.

  >   Example

  >   >     >> ts = dseries(randn(3,1))

  >   >     ts is a dseries object:

  >   >        | Variable_1
  >   >     1Y | -1.2075
  >   >     2Y | 0.71724
  >   >     3Y | 1.6302

  >   >     >> ts>1

  >   >     ans =

  >   >       3x1 logical array

  >   >        0
  >   >        0
  >   >        1

  >   >     >> ds = dseries(randn(3,1))

  >   >     ds is a dseries object:

  >   >        | Variable_1
  >   >     1Y | 0.48889
  >   >     2Y | 1.0347
  >   >     3Y | 0.72689

  >   >     >> ds>ts

  >   >     ans =

  >   >       3x1 logical array

  >   >        1
  >   >        1
  >   >        0

  > Method: B = hdiff(A)
  > Method: hdiff_(A)

  >   Computes bi-annual differences.

  > Method: B = hgrowth(A)
  > Method: hgrowth_(A)

  >   Computes bi-annual growth rates.

  Method: D = horzcat(A, B[, ...])

    > Overloads the `horzcat` MATLAB/Octave’s method for `dseries` objects. Returns a `dseries` object `D` containing the variables in `dseries` objects passed as inputs: `A, B, ...` If the inputs are not defined on the same time ranges, the method adds NaNs to the variables so that the variables are redefined on the smallest common time range. Note that the names in the `dseries` objects passed as inputs must be different and these objects must have common frequency.

    > Example

    > >     >> ts0 = dseries(rand(5,2),'1950Q1',{'nifnif';'noufnouf'});
    > >     >> ts1 = dseries(rand(7,1),'1950Q3',{'nafnaf'});
    > >     >> ts2 = [ts0, ts1];
    > >     >> ts2

    > >     ts2 is a dseries object:

    > >            | nifnif  | noufnouf | nafnaf
    > >     1950Q1 | 0.17404 | 0.71431  | NaN
    > >     1950Q2 | 0.62741 | 0.90704  | NaN
    > >     1950Q3 | 0.84189 | 0.21854  | 0.83666
    > >     1950Q4 | 0.51008 | 0.87096  | 0.8593
    > >     1951Q1 | 0.16576 | 0.21184  | 0.52338
    > >     1951Q2 | NaN     | NaN      | 0.47736
    > >     1951Q3 | NaN     | NaN      | 0.88988
    > >     1951Q4 | NaN     | NaN      | 0.065076
    > >     1952Q1 | NaN     | NaN      | 0.50946

    Method: B = hpcycle(A[, lambda])
    Method: hpcycle_(A[, lambda])

      Extracts the cycle component from a `dseries` `A` object using the Hodrick and Prescott (1997) filter and returns a `dseries` object, `B`. The default value for `lambda`, the smoothing parameter, is `1600`.

      Example

      >     % Simulate a component model (stochastic trend, deterministic
      >     % trend, and a stationary autoregressive process).
      >     e = 0.2*randn(200,1);
      >     u = randn(200,1);
      >     stochastic_trend = cumsum(e);
      >     deterministic_trend = .1*transpose(1:200);
      >     x = zeros(200,1);
      >     for i=2:200
      >         x(i) = .75*x(i-1) + u(i);
      >     end
      >     y = x + stochastic_trend + deterministic_trend;

      >     % Instantiates time series objects.
      >     ts0 = dseries(y,'1950Q1');
      >     ts1 = dseries(x,'1950Q1'); % stationary component.

      >     % Apply the HP filter.
      >     ts2 = ts0.hpcycle();

      >     % Plot the filtered time series.
      >     plot(ts1(ts2.dates).data,'-k'); % Plot of the stationary component.
      >     hold on
      >     plot(ts2.data,'--r');           % Plot of the filtered y.
      >     hold off
      >     axis tight
      >     id = get(gca,'XTick');
      >     set(gca,'XTickLabel',strings(ts.dates(id)));

    Method: B = hptrend(A[, lambda])
    Method: hptrend_(A[, lambda])

      Extracts the trend component from a `dseries` A object using the Hodrick and Prescott (1997) filter and returns a `dseries` object, `B`. Default value for `lambda`, the smoothing parameter, is `1600`.

      Example

      >     % Using the same generating data process
      >     % as in the previous example:

      >     ts1 = dseries(stochastic_trend + deterministic_trend,'1950Q1');
      >     % Apply the HP filter.
      >     ts2 = ts0.hptrend();

      >     % Plot the filtered time series.
      >     plot(ts1.data,'-k'); % Plot of the nonstationary components.
      >     hold on
      >     plot(ts2.data,'--r');  % Plot of the estimated trend.
      >     hold off
      >     axis tight
      >     id = get(gca,'XTick');
      >     set(gca,'XTickLabel',strings(ts0.dates(id)));

    Method: C = insert(A, B, I)

      Inserts variables contained in `dseries` object `B` in `dseries` object `A` at positions specified by integer scalars in vector `I`, returns augmented `dseries` object `C`. The integer scalars in `I` must take values between `` and `A.length()+1` and refers to `A` ’s column numbers. The `dseries` objects `A` and `B` need not be defined over the same time ranges, but it is assumed that they have common frequency.

      Example

      >     >> ts0 = dseries(ones(2,4),'1950Q1',{'Sly'; 'Gobbo'; 'Sneaky'; 'Stealthy'});
      >     >> ts1 = dseries(pi*ones(2,1),'1950Q1',{'Noddy'});
      >     >> ts2 = ts0.insert(ts1,3)

      >     ts2 is a dseries object:

      >            | Sly | Gobbo | Noddy  | Sneaky | Stealthy
      >     1950Q1 | 1   | 1     | 3.1416 | 1      | 1
      >     1950Q2 | 1   | 1     | 3.1416 | 1      | 1

      >     >> ts3 = dseries([pi*ones(2,1) sqrt(pi)*ones(2,1)],'1950Q1',{'Noddy';'Tessie Bear'});
      >     >> ts4 = ts0.insert(ts1,[3, 4])

      >     ts4 is a dseries object:

      >            | Sly | Gobbo | Noddy  | Sneaky | Tessie Bear | Stealthy
      >     1950Q1 | 1   | 1     | 3.1416 | 1      | 1.7725      | 1
      >     1950Q2 | 1   | 1     | 3.1416 | 1      | 1.7725      | 1

    Method: B = isempty(A)

      Overloads the MATLAB/octave’s `isempty` function. Returns `true` if `dseries` object `A` is empty.

    Method: C = isequal(A, B)

      Overloads the MATLAB/octave’s `isequal` function. Returns `true` if `dseries` objects `A` and `B` are identical.

    Method: C = isinf(A)

      Overloads the MATLAB/octave’s `isinf` function. Returns a logical array, with element `(i,j)` equal to `true` if and only if variable `j` is finite in period `A.dates(i)`.

    Method: C = isnan(A)

      Overloads the MATLAB/octave’s `isnan` function. Returns a logical array, with element `(i,j)` equal to `true` if and only if variable `j` isn’t NaN in period `A.dates(i)`.

    Method: C = isreal(A)

      Overloads the MATLAB/octave’s `isreal` function. Returns a logical array, with element `(i,j)` equal to `true` if and only if variable `j` is real in period `A.dates(i)`.

    Method: B = lag(A[, p])
    Method: lag_(A[, p])

      Returns lagged time series. Default value of integer scalar `p`, the number of lags, is `1`. The dseries class overloads the parentheses, so that ts.lag(p) is equivalent to ts(-p).

      Example

      >     >> ts0 = dseries(transpose(1:4), '1950Q1')

      >     ts0 is a dseries object:

      >            | Variable_1
      >     1950Q1 | 1
      >     1950Q2 | 2
      >     1950Q3 | 3
      >     1950Q4 | 4

      >     >> ts1 = ts0.lag()

      >     ts1 is a dseries object:

      >                | Variable_1
      >         1950Q1 | NaN
      >         1950Q2 | 1
      >         1950Q3 | 2
      >         1950Q4 | 3

      >     >> ts2 = ts0.lag(2)

      >     ts2 is a dseries object:

      >            | Variable_1
      >     1950Q1 | NaN
      >     1950Q2 | NaN
      >     1950Q3 | 1
      >     1950Q4 | 2

      >     % dseries class overloads the parenthesis
      >     % so that ts.lag(p) can be written more
      >     % compactly as ts(-p). For instance:

      >     >> ts0.lag(1)

      >     ans is a dseries object:

      >            | Variable_1
      >     1950Q1 | NaN
      >     1950Q2 | 1
      >     1950Q3 | 2
      >     1950Q4 | 3

      > or alternatively:

      >     >> ts0(-1)

      >     ans is a dseries object:

      >            | Variable_1
      >     1950Q1 | NaN
      >     1950Q2 | 1
      >     1950Q3 | 2
      >     1950Q4 | 3

    Method: l = lastdate(B)

      Retrieves the final period from the `dseries` object `B`.

      Example

      >     >> ts = dseries(randn(3,2),'1973Q1');
      >     >> ts.lastdate()

      >     ans = <dates: 1973Q3>

    Method: f = lastobservedperiod(A)

      Returns the last period in which all variables of the `dseries` object `A` are fully observed (i.e., contain no NaN values).

    Method: f = lastobservedperiods(A)

      Returns the last period without missing observations for each variable in the `dseries` object `A`. The output argument `f` is a structure where each field name corresponds to a variable in `A`, and the content of each field is a singleton `date` object.

    Method: l = le(A, B)
    Method: l = lt(A, B)

      Overloads the `gt` (<) and `ge` (<=) binary operators. Returns a logical array.

      Example

      >     >> ts = dseries(randn(3,1))

      >     ts is a dseries object:

      >        | Variable_1
      >     1Y | -1.2075
      >     2Y | 0.71724
      >     3Y | 1.6302

      >     >> ts<1

      >     ans =

      >       3x1 logical array

      >        1
      >        1
      >        0

      >     >> ds = dseries(randn(3,1))

      >     ds is a dseries object:

      >        | Variable_1
      >     1Y | 0.48889
      >     2Y | 1.0347
      >     3Y | 0.72689

      >     >> ds<ts

      >     ans =

      >       3x1 logical array

      >        0
      >        0
      >        1

    Method: B = lead(A[, p])
    Method: lead_(A[, p])

      Returns a lead time series. The default value for the integer scalar `p`, which represents the number of leads, is `1`. Similar to the `lag` method, the `dseries` class overloads the parentheses, making `ts.lead(p)` equivalent to `ts(p)`.

      Example

      >     >> ts0 = dseries(transpose(1:4),'1950Q1');
      >     >> ts1 = ts0.lead()

      >     ts1 is a dseries object:

      >            | Variable_1
      >     1950Q1 | 2
      >     1950Q2 | 3
      >     1950Q3 | 4
      >     1950Q4 | NaN

      >     >> ts2 = ts0(2)

      >     ts2 is a dseries object:

      >            | Variable_1
      >     1950Q1 | 3
      >     1950Q2 | 4
      >     1950Q3 | NaN
      >     1950Q4 | NaN

      Remark

      The overload of parentheses for `dseries` objects simplifies the creation of new `dseries` instances by enabling the direct copying and pasting of equations defined within the `model` block. For example, if an Euler equation is specified in the `model` block,:

          model;
          ...
          1/C - beta/C(1)*(exp(A(1))*K^(alpha-1)+1-delta) ;
          ...
          end;

      and if variables `, ``A` and `K` are defined as `dseries` objects, then by writing:

          Residuals = 1/C - beta/C(1)*(exp(A(1))*K^(alpha-1)+1-delta) ;

      outside of the `model` block, we create a new `dseries` object, called `Residuals`, for the residuals of the Euler equation (the conditional expectation of the equation defined in the `model` block is zero, but the residuals are non zero).

    Method: B = lineartrend(A)

      Returns a linear trend centered on 0, the length of the trend is given by the size of `dseries` object `A` (the number of periods).

      Example

      >     >> ts = dseries(ones(3,1));
      >     >> ts.lineartrend()

      >     ans =

      >          -1
      >           0
      >           1

    Method: B = log(A)
    Method: log_(A)

      Overloads the MATLAB/Octave `log` function for `dseries` objects.

      Example

      >     >> ts0 = dseries(rand(10,1));
      >     >> ts1 = ts0.log();

    Method: B = mdiff(A)
    Method: mdiff_(A)
    Method: B = mgrowth(A)
    Method: mgrowth_(A)

      Calculates the monthly differences or growth rates of variables in the `dseries` object `A`.

    Method: B = mean(A[, geometric])

      This function overloads the MATLAB/Octave `mean` function specifically for `dseries` objects. It calculates the mean for each variable within the `dseries` object `A`. If the second argument is set to `true`, the geometric mean is calculated; otherwise, the arithmetic mean is computed by default.

    Method: C = merge(A, B[, legacy])

      Merges two `dseries` objects, `A` and `B`, into a new `dseries` object `C`. The objects `A` and `B` must share a common frequency, although they can cover different time ranges. If a variable, such as `x`, exists in both `dseries` objects, the `merge` function will prioritize the definition from the second input, `B`, while retaining the values from `A` for any corresponding periods where `B` has NaN values. This behavior can be altered by setting the optional argument `legacy` to true, in which case the second variable will replace the first, even if it contains NaN values.

      Example

      >     >> ts0 = dseries(rand(3,2),'1950Q1',{'A1';'A2'})

      >     ts0 is a dseries object:

      >            | A1      | A2
      >     1950Q1 | 0.96284 | 0.5363
      >     1950Q2 | 0.25145 | 0.31866
      >     1950Q3 | 0.34447 | 0.4355

      >     >> ts1 = dseries(rand(3,1),'1950Q2',{'A1'})

      >     ts1 is a dseries object:

      >            | A1
      >     1950Q2 | 0.40161
      >     1950Q3 | 0.81763
      >     1950Q4 | 0.97769

      >     >> merge(ts0,ts1)

      >     ans is a dseries object:

      >            | A1      | A2
      >     1950Q1 | 0.96284 | 0.5363
      >     1950Q2 | 0.40161 | 0.31866
      >     1950Q3 | 0.81763 | 0.4355
      >     1950Q4 | 0.97769 | NaN

      >      >> merge(ts1,ts0)

      >      ans is a dseries object:

      >            | A1      | A2
      >     1950Q1 | 0.96284 | 0.5363
      >     1950Q2 | 0.25145 | 0.31866
      >     1950Q3 | 0.34447 | 0.4355
      >     1950Q4 | 0.97769 | NaN

    Method: C = minus(A, B)

      Overloads the MATLAB/Octave `minus` (`-`) operator for `dseries` objects, allowing for element-by-element subtraction. When both `A` and `B` are `dseries` objects, they do not need to be defined over the same time ranges. If `A` and `B` have \(T_A\) and \(T_B\) observations and \(N_A\) and \(N_B\) variables, then \(N_A\) must equal \(N_B\) or \(1\), and \(N_B\) must equal \(N_A\) or \(1\). If \(T_A=T_B\), `isequal(A.init,B.init)` returns `1`, and \(N_A=N_B\), then the `minus` operator will compute for each pair \((t,n)\), where \(1\le t\le T_A\) and \(1\le n\le N_A\), the operation `C.data(t,n)=A.data(t,n)-B.data(t,n)`. If \(N_B\) equals \(1\) and \(N_A>1\), the smaller `dseries` object (`B`) is “broadcasted” across the larger `dseries` (`A`), ensuring compatible shapes for the subtraction of the variable defined in `B` from each variable in `A`. If `B` is a double scalar, the `minus` method will subtract `B` from all observations and variables in `A`. If `B` is a row vector of length \(N_A\), the `minus` method will subtract `B(i)` from all observations of variable `i`, for \(i=1,...,N_A\). If `B` is a column vector of length \(T_A\), the `minus` method will subtract `B` from all the variables.

      Example

      >     >> ts0 = dseries(rand(3,2));
      >     >> ts1 = ts0{'Variable_2'};
      >     >> ts0-ts1

      >     ans is a dseries object:

      >        | Variable_1 | Variable_2
      >     1Y | -0.48853   | 0
      >     2Y | -0.50535   | 0
      >     3Y | -0.32063   | 0

      >     >> ts1

      >     ts1 is a dseries object:

      >        | Variable_2
      >     1Y | 0.703
      >     2Y | 0.75415
      >     3Y | 0.54729

      >     >> ts1-ts1.data(1)

      >     ans is a dseries object:

      >        | Variable_2
      >     1Y | 0
      >     2Y | 0.051148
      >     3Y | -0.15572

      >     >> ts1.data(1)-ts1

      >     ans is a dseries object:

      >        | Variable_2
      >     1Y | 0
      >     2Y | -0.051148
      >     3Y | 0.15572

    Method: C = mpower(A, B)

      Overloads the MATLAB/Octave `mpower` (`^`) operator for `dseries` objects, performing element-wise exponentiation. Given a `dseries` object `A` with `N` variables and `T` observations, if `B` is a real scalar, then `mpower(A,B)` yields a `dseries` object `C` where `C.data(t,n) = A.data(t,n)^B`. If `B` is also a `dseries` object with `N` variables and `T` observations, then `mpower(A,B)` produces a `dseries` object `C` such that `C.data(t,n) = A.data(t,n)^{C.data(t,n)}`.

      Example

      >     >> ts0 = dseries(transpose(1:3));
      >     >> ts1 = ts0^2

      >     ts1 is a dseries object:

      >        | Variable_1
      >     1Y | 1
      >     2Y | 4
      >     3Y | 9

      >     >> ts2 = ts0^ts0

      >     ts2 is a dseries object:

      >        | Variable_1
      >     1Y | 1
      >     2Y | 4
      >     3Y | 27

    Method: C = mrdivide(A, B)

      Overloads the MATLAB/Octave `mrdivide` (`/`) operator for `dseries` objects, enabling element-wise division similar to the `./` operator in MATLAB/Octave. When both `A` and `B` are `dseries` objects, they can have different time ranges. If `A` contains \(T_A\) observations and \(N_A\) variables, and `B` has \(T_B\) observations and \(N_B\) variables, then \(N_A\) must equal \(N_B\) or \(1\), and vice versa. If \(T_A=T_B\) and `isequal(A.init,B.init)` returns `1`, along with \(N_A=N_B\), the `mrdivide` operator calculates for each pair \((t,n)\), where \(1\le t\le T_A\) and \(1\le n\le N_A\), the value of `C.data(t,n)=A.data(t,n)/B.data(t,n)`. If \(N_B\) equals \(1\) and \(N_A>1\), the smaller `dseries` object (`B`) is “broadcast” across the larger one (`A`) to ensure compatible shapes. In this case, the `mrdivide` operator divides each variable in `A` by the variable in `B`, observation by observation. If `B` is a double scalar, then `mrdivide` will divide all observations and variables in `A` by `B`. If `B` is a row vector of length \(N_A\), then `mrdivide` will divide each observation of variable `i` by `B(i)`, for \(i=1,...,N_A\). If `B` is a column vector of length \(T_A\), then `mrdivide` will perform an element-wise division of all variables by `B`.

      Example

      >     >> ts0 = dseries(rand(3,2))

      >     ts0 is a dseries object:

      >        | Variable_1 | Variable_2
      >     1Y | 0.72918    | 0.90307
      >     2Y | 0.93756    | 0.21819
      >     3Y | 0.51725    | 0.87322

      >     >> ts1 = ts0{'Variable_2'};
      >     >> ts0/ts1

      >     ans is a dseries object:

      >        | Variable_1 | Variable_2
      >     1Y | 0.80745    | 1
      >     2Y | 4.2969     | 1
      >     3Y | 0.59235    | 1

    Method: C = mtimes(A, B)

      Overloads the MATLAB/Octave `mtimes` (`*`) operator for `dseries` objects, enabling element-wise multiplication similar to the `.*` operator in MATLAB/Octave. When both `A` and `B` are `dseries` objects, they can have different time ranges. If `A` contains \(T_A\) observations and \(N_A\) variables, and `B` has \(T_B\) observations and \(N_B\) variables, then \(N_A\) must equal \(N_B\) or \(1\), and vice versa. If \(T_A=T_B\) and `isequal(A.init,B.init)` returns `1`, along with \(N_A=N_B\), the `mtimes` operator calculates for each pair \((t,n)\), where \(1\le t\le T_A\) and \(1\le n\le N_A\), the value of `C.data(t,n)=A.data(t,n)*B.data(t,n)`. If \(N_B\) equals \(1\) and \(N_A>1\), the smaller `dseries` object (`B`) is “broadcasted” across the larger one (`A`) to ensure compatible shapes. In this case, the `mtimes` operator multiply each variable in `A` by the variable in `B`, observation by observation. If `B` is a double scalar, then `mtimes` will multiply all observations and variables in `A` by `B`. If `B` is a row vector of length \(N_A\), then `mtimes` will multiply each observation of variable `i` by `B(i)`, for \(i=1,...,N_A\). If `B` is a column vector of length \(T_A\), then `mtimes` will perform an element-wise multiplication of all variables by `B`.

    Method: B = nanmean(A[, geometric])

      Overloads the MATLAB/Octave `nanmean` function for `dseries` objects. Computes the mean of each variable in the `dseries` object `A`, excluding NaN values. If the second argument is `true`, the geometric mean is calculated; otherwise, the default is to report the arithmetic mean.

    Method: B = nanstd(A[, geometric])

      Overloads the MATLAB/Octave `nanstd` function for `dseries` objects. This function calculates the standard deviation for each variable within the `dseries` object `A`, while disregarding any NaN values. If the second argument is set to `true`, the geometric standard deviation will be computed; the default value for the second argument is `false`.

    Method: C = ne(A, B)

      Overloads the MATLAB/Octave `ne` (not equal, `~=`) operator. The `dseries` objects `A` and `B` must contain the same number of observations (denoted as \(T\)) and variables (denoted as \(N\)). The output is a \(T\) by \(N\) matrix consisting of zeros and ones. The element \((i,j)\) of the matrix `C` is equal to `1` if and only if observation \(i\) for variable \(j\) in `A` and `B` are not equal.

      Example

      >     >> ts0 = dseries(2*ones(3,1));
      >     >> ts1 = dseries([2; 0; 2]);
      >     >> ts0~=ts1

      >     ans =

      >       3x1 logical array

      >        0
      >        1
      >        0

    Method: B = nobs(A)

      Returns the number of observations in `dseries` object `A`.

      Example

      >     >> ts0 = dseries(randn(10));
      >     >> ts0.nobs

      >     ans =

      >         10

    Method: B = onesidedhpcycle(A[, lambda[, init]])
    Method: onesidedhpcycle_(A[, lambda[, init]])

      Extracts the cycle component from a `dseries` `A` object using a one-sided HP filter (implemented with a Kalman filter) and returns a `dseries` object, `B`. The default value for `lambda`, the smoothing parameter, is set to `1600`. By default, if `init` is not provided, the initial value is determined from the first two observations.

    Method: B = onesidedhptrend(A[, lambda[, init]])
    Method: onesidedhptrend_(A[, lambda[, init]])

      Extracts the trend component from a `dseries` `A` object using a one-sided HP filter (implemented with a Kalman filter) and returns a `dseries` object, `B`. The default value for `lambda`, the smoothing parameter, is set to `1600`. By default, if `init` is not provided, the initial value is derived from the first two observations.

    Method: h = plot(A)
    Method: h = plot(A, B)
    Method: h = plot(A[, ...])
    Method: h = plot(A, B[, ...])

      Overloads the MATLAB/Octave `plot` function for `dseries` objects. This function returns a MATLAB/Octave plot handle, which can be utilized to modify the properties of the plotted time series. If a single `dseries` object, `A`, is provided as an argument, the plot function will place the corresponding dates on the x-axis. If this `dseries` object contains only one variable, additional arguments can be included to adjust the plot properties, similar to how one would with MATLAB/Octave’s original plot function. However, if the `dseries` object `A` has more than one variable, additional arguments cannot be passed, and modifications to the plotted time series properties must be done using the returned plot handle alongside the MATLAB/Octave `set` function (refer to the example below). When two `dseries` objects, `A` and `B`, are passed as input arguments, the plot function will display the variables in `A` against those in `B` (it is essential that both objects contain the same number of variables; otherwise, an error will occur). Once more, if each object includes only one variable, additional arguments can be utilized to alter the plotted time series properties; otherwise, the MATLAB/Octave `set` command must be employed.

      Example

      > Define a `dseries` object with two variables (named by default `Variable_1` and `Variable_2`):

      >     >> ts = dseries(randn(100,2),'1950Q1');

      > The following command will plot the first variable in `ts`:

      >     >> plot(ts{'Variable_1'},'-k','linewidth',2);

      > The next command will draw all the variables in `ts` on the same figure:

      >     >> h = plot(ts);

      > If one wants to modify the properties of the plotted time series (line style, colours, …), the set function can be used (see MATLAB’s documentation):

      >     >> set(h(1),'-k','linewidth',2);
      >     >> set(h(2),'--r');

      > The following command will plot `Variable_1` against `exp(Variable_1)`:

      >     >> plot(ts{'Variable_1'},ts{'Variable_1'}.exp(),'ok');

      > Again, the properties can also be modified using the returned plot handle and the `set` function:

      >     >> h = plot(ts, ts.exp());
      >     >> set(h(1),'ok');
      >     >> set(h(2),'+r');

    Method: C = plus(A, B)

      Overloads the MATLAB/Octave `plus` (`+`) operator for `dseries` objects, allowing for element-wise addition. When both `A` and `B` are `dseries` objects, they do not need to be defined over the same time ranges. If `A` and `B` are `dseries` objects with \(T_A\) and \(T_B\) observations and \(N_A\) and \(N_B\) variables, then \(N_A\) must be equal to \(N_B\) or \(1\) and \(N_B\) must be equal to \(N_A\) or \(1\). If \(T_A=T_B\), `isequal(A.init,B.init)` returns `1` and \(N_A=N_B\), then the `plus` operator will compute for each pair \((t,n)\), with \(1\le t\le T_A\) and \(1\le n\le N_A\), `C.data(t,n)=A.data(t,n)+B.data(t,n)`. If \(N_B\) is equal to \(1\) and \(N_A>1\), the smaller `dseries` object (`B`) is “broadcasted” across the larger `dseries` (`A`) to ensure compatible shapes, the plus operator will add the variable defined in `B` to each variable in `A`. If `B` is a double scalar, then the method `plus` will add `B` to all the observations/variables in `A`. If `B` is a row vector of length \(N_A\), then the `plus` method will add `B(i)` to all the observations of variable `i`, for \(i=1,\ldots,N_A\). If `B` is a column vector of length \(T_A\), then the `plus` method will add `B` to all the variables.

    Method: C = pop(A[, B])
    Method: pop_(A[, B])

      Removes the variable `B` from the `dseries` object `A`. By default, if the second argument is not specified, the last variable is removed.

      Example

      >     >> ts0 = dseries(ones(3,3));
      >     >> ts1 = ts0.pop('Variable_2');

      >     ts1 is a dseries object:

      >        | Variable_1 | Variable_3
      >     1Y | 1          | 1
      >     2Y | 1          | 1
      >     3Y | 1          | 1

    Method: A = projection(A, info, periods)

      Projects variables in the dseries object `A`. The `info` variable is a \(n \times 3\) cell array, where each row contains essential information for projecting a variable. The first column holds the variable name (as a character array), while the second column indicates the projection method used (also a character array). The possible values for this column are `'Trend'`, `'Constant'`, and `'AR'`. The third column provides quantitative details related to the projection: if the second column is `'Trend'`, the third column specifies the growth factor of the (exponential) trend; if `'Constant'`, it indicates the variable’s level; and if `'AR'`, it denotes the autoregressive parameter. Variables can be projected using an AR(p) model if the third column contains a 1×p vector of doubles. Note that the stationarity of the AR(p) model is not tested. For constant projections, one can use either ‘Trend’ with a growth factor of 1 or ‘AR’ with an autoregressive parameter of one (indicating a random walk). This projection routine solely addresses exponential trends.

      Example

      >     >> data = ones(10,4);
      >     >> ts = dseries(data, '1990Q1', {'A1', 'A2', 'A3', 'A4'});
      >     >> info = {'A1', 'Trend', 1.2; 'A2', 'Constant', 0.0; 'A3', 'AR', .5; 'A4', 'AR', [.4, -.2]};
      >     >> ts.projection(info, 10);

    Method: B = qdiff(A)
    Method: B = qgrowth(A)
    Method: qdiff_(A)
    Method: qgrowth_(A)

      Computes quarterly differences or growth rates.

      Example

      >     >> ts0 = dseries(transpose(1:4),'1950Q1');
      >     >> ts1 = ts0.qdiff()

      >     ts1 is a dseries object:

      >            | Variable_1
      >     1950Q1 | NaN
      >     1950Q2 | 1
      >     1950Q3 | 1
      >     1950Q4 | 1

      >     >> ts0 = dseries(transpose(1:6),'1950M1');
      >     >> ts1 = ts0.qdiff()

      >     ts1 is a dseries object:

      >             | Variable_1
      >     1950M1  | NaN
      >     1950M2  | NaN
      >     1950M3  | NaN
      >     1950M4  | 3
      >     1950M5  | 3
      >     1950M6  | 3

    Method: C = remove(A, B)
    Method: remove_(A, B)

      If `B` is a row character array representing the name of a variable, these methods serve as aliases for the `pop` and `pop_` methods that accept two arguments. They remove the variable `B` from the `dseries` object `A`. To remove multiple variables, you can pass a cell array of row character arrays for `B`.

      Example

      >     >> ts0 = dseries(ones(3,3));
      >     >> ts1 = ts0.remove('Variable_2');

      >     ts1 is a dseries object:

      >        | Variable_1 | Variable_3
      >     1Y | 1          | 1
      >     2Y | 1          | 1
      >     3Y | 1          | 1

      > A more concise syntax is available: `remove(ts, 'Variable_2')`, which is equivalent to `ts{'Variable_2'} = []` (where `[]` can be substituted with any empty object). This alternative syntax proves useful when removing multiple variables. For example:

      >     ts{'Variable_@2,3,4@'} = [];

      > will remove `Variable_2`, `Variable_3` and `Variable_4` from `dseries` object `ts` (if these variables exist). Regular expressions cannot be used but implicit loops can.

    Method: B = rename(A, oldname, newname)
    Method: rename_(A, oldname, newname)

      Renames the variable `oldname` to `newname` in the `dseries` object `A`. This function returns a `dseries` object. If multiple variables need to be renamed, you can provide cell arrays of row character arrays as the second and third arguments.

      Example

      >     >> ts0 = dseries(ones(2,2));
      >     >> ts1 = ts0.rename('Variable_1','Stinkly')

      >     ts1 is a dseries object:

      >        | Stinkly | Variable_2
      >     1Y | 1       | 1
      >     2Y | 1       | 1

    Method: C = rename(A, newname)
    Method: rename_(A, newname)

      Replace the names in `A` with those specified in the cell of row character arrays `newname`. The cell `newname` must contain the same number of elements as there are variables in the `dseries` object `A`.

      Example

      >     >> ts0 = dseries(ones(2,3));
      >     >> ts1 = ts0.rename({'TinkyWinky','Dipsy','LaaLaa'})

      >     ts1 is a dseries object:

      >        | TinkyWinky | Dipsy | LaaLaa
      >     1Y | 1          | 1     | 1
      >     2Y | 1          | 1     | 1

    Method: A = resetops(A, ops)

      Redefine `ops` member.

    Method: A = resetags(A, ops)

      Redefine `tags` member.

    Method: B = round(A[, n])
    Method: round_(A[, n])

      Rounds each value to the nearest decimal or integer. The parameter `n` specifies the precision (number of decimal places), with a default value of 0, indicating that the method will round to the nearest integer by default.

      Example

      >     >> ts = dseries(pi)

      >     ts is a dseries object:

      >        | Variable_1
      >     1Y | 3.1416

      >     >> ts.round_();
      >     >> ts

      >     ts is a dseries object:

      >        | Variable_1
      >     1Y | 3

    Method: save(A, basename[, format])

      Overloads the MATLAB/Octave `save` function to save the `dseries` object `A` to disk. The available formats include `mat` (default, MATLAB binary data file), `m` (MATLAB/Octave script), and `csv` (comma-separated values file). The base name of the file, excluding the extension, is specified by `basename`.

      Example

      >     >> ts0 = dseries(ones(2,2));
      >     >> ts0.save('ts0', 'csv');

      > The last command will create a file ts0.csv with the following content:

      >     ,Variable_1,Variable_2
      >     1Y,               1,               1
      >     2Y,               1,               1

      > To create a MATLAB/Octave script, the following command:

      >     >> ts0.save('ts0','m');

      > will produce a file ts0.m with the following content:

      >     % File created on 14-Nov-2013 12:08:52.

      >     FREQ__ = 1;
      >     INIT__ = ' 1Y';

      >     NAMES__ = {'Variable_1'; 'Variable_2'};
      >     TEX__ = {'Variable_{1}'; 'Variable_{2}'};
      >     OPS__ = {};
      >     TAGS__ = struct();

      >     Variable_1 = [
      >                   1
      >                   1];

      >     Variable_2 = [
      >                   1
      >                   1];

      > The generated (`csv`, `m`, or `mat`) files can be loaded when instantiating a `dseries` object as explained above.

    Method: B = set_names(A, s1, s2, ...)

      Renames the variables in the `dseries` object `A` and returns a new `dseries` object `B` with the updated names `s1`, `s2`, and so forth. The number of input arguments following the first one (the `dseries` object `A`) must be equal to `A.vobs` (the total number of variables in `A`). The name `s1` will correspond to the first variable in `B`, `s2` to the second variable in `B`, and this pattern continues for the remaining variables.

      Example

      >     >> ts0 = dseries(ones(1,3));
      >     >> ts1 = ts0.set_names('Barbibul',[],'Barbouille')

      >     ts1 is a dseries object:

      >        | Barbibul | Variable_2 | Barbouille
      >     1Y | 1        | 1          | 1

    Method: [T, N ] = size(A[, dim])

      Overloads the MATLAB/Octave `size` function to return the number of observations in the `dseries` object `A` (i.e., `A.nobs`) as well as the number of variables (i.e., `A.vobs`). If a second input argument is provided, the `size` function will return the number of observations when `dim=1` or the number of variables when `dim=2`. An error will be issued for any other values of `dim`.

      Example

      >     >> ts0 = dseries(ones(1,3));
      >     >> ts0.size()

      >     ans =

      >          1     3

    Method: B = std(A[, geometric])

      Overloads the MATLAB/Octave `std` function for `dseries` objects. This function returns the standard deviation of each variable within the `dseries` object `A`. If the second argument is set to `true`, the geometric standard deviation is calculated (the default value for the second argument is `false`).

    Method: B = subsample(A, d1, d2)

      Returns a subsample for the period between `d1` and `d2`. While you can achieve the same result by indexing a `dseries` object with a `dates` object, the `subsample` method offers a more straightforward approach for programmatic use.

      Example

      >     >> o = dseries(transpose(1:5));
      >     >> o.subsample(dates('2y'),dates('4y'))

      >     ans is a dseries object:

      >        | Variable_1
      >     2Y | 2
      >     3Y | 3
      >     4Y | 4

    Method: A = tag(A, a[, b, c])

      Adds a tag to a variable in `dseries` object `A`.

      Example

      >     >> ts = dseries(randn(10, 3));
      >     >> tag(ts, 'type');             % Define a tag name.
      >     >> tag(ts, 'type', 'Variable_1', 'Stock');
      >     >> tag(ts, 'type', 'Variable_2', 'Flow');
      >     >> tag(ts, 'type', 'Variable_3', 'Stock');

    Method: B = tex_rename(A, name, newtexname)
    Method: B = tex_rename(A, newtexname)
    Method: tex_rename_(A, name, newtexname)
    Method: tex_rename_(A, newtexname)

      Updates the TeX name of the variable `name` to `newtexname` in the `dseries` object `A`. Returns an updated `dseries` object.

      With just two arguments, `A` and `newtexname`, this function redefines the TeX names of the entries in `A` to those specified in `newtexname`. The `newtexname` argument must be a cell row character arrays containing the same number of entries as there are variables in `A`.

    Method: B = uminus(A)

      Overloads the `uminus` operator (`-`, unary minus) for the `dseries` object.

      Example

      >     >> ts0 = dseries(1)

      >     ts0 is a dseries object:

      >        | Variable_1
      >     1Y | 1

      >     >> ts1 = -ts0

      >     ts1 is a dseries object:

      >        | Variable_1
      >     1Y | -1

    Method: D = vertcat(A, B[, ...])

      Overloads the `vertcat` method in MATLAB/Octave for `dseries` objects. This method facilitates the appending of additional observations to a `dseries` object. It returns a new `dseries` object, `D`, which contains the variables from the input `dseries` objects. All input arguments must be `dseries` objects that share the same variables but are defined over different time ranges.

      Example

      >     >> ts0 = dseries(rand(2,2),'1950Q1',{'nifnif';'noufnouf'});
      >     >> ts1 = dseries(rand(2,2),'1950Q3',{'nifnif';'noufnouf'});
      >     >> ts2 = [ts0; ts1]

      >     ts2 is a dseries object:

      >            | nifnif   | noufnouf
      >     1950Q1 | 0.82558  | 0.31852
      >     1950Q2 | 0.78996  | 0.53406
      >     1950Q3 | 0.089951 | 0.13629
      >     1950Q4 | 0.11171  | 0.67865

    Method: B = vobs(A)

      Returns the count of variables in the `dseries` object `A`.

      Example

      >     >> ts0 = dseries(randn(10,2));
      >     >> ts0.vobs

      >     ans =

      >         2

    Method: B = ydiff(A)
    Method: B = ygrowth(A)
    Method: ydiff_(A)
    Method: ygrowth_(A)

      Calculates annual differences or growth rates.

## 6.3. X-13 ARIMA-SEATS interface

Dynare class: x13

  The x13 class provides a method for each X-13 command as documented in the X-13 ARIMA-SEATS reference manual (x11, automdl, estimate, …). The respective options (see Chapter 7 of U.S. Census Bureau (2020)) can then be passed by key/value pairs. The `x13` class has 22 members:

  Members:

    - y – `dseries` object with a single variable.

    - x – `dseries` object with an arbitrary number of variables (to be used in the REGRESSION block).

    - arima – structure containing the options of the ARIMA model command.

    - automdl – structure containing the options of the ARIMA model selection command.

    - regression – structure containing the options of the Regression command.

    - estimate – structure containing the options of the estimation command.

    - transform – structure containing the options of the transform command.

    - outlier – structure containing the options of the outlier command.

    - forecast – structure containing the options of the forecast command.

    - check – structure containing the options of the check command.

    - x11 – structure containing the options of the X11 command.

    - force – structure containing the options of the force command.

    - history – structure containing the options of the history command.

    - metadata – structure containing the options of the metadata command.

    - identify – structure containing the options of the identify command.

    - pickmdl – structure containing the options of the pickmdl command.

    - seats – structure containing the options of the seats command.

    - slidingspans – structure containing the options of the slidingspans command.

    - spectrum – structure containing the options of the spectrum command.

    - x11regression – structure containing the options of the x11Regression command.

    - results – structure containing the results returned by x13.

    - commands – cell array containing the list of commands.

  All these members are private. The following constructors are available:

  Constructor: x13(y)

    Instantiates an `x13` object with dseries object `y`. The `dseries` object passed as an argument must contain only one variable, the one we need to pass to X-13.

  Constructor: x13(y, x)

    Instantiates an `x13` object with dseries objects `y` and `x`. The first `dseries` object passed as an argument must contain only one variable, the second `dseries` object contains the exogenous variables used by some of the X-13 commands. Both objects must be defined on the same time span.

  The following methods allow to set sequence of X-13 commands, write an .spc file, and run the X-13 binary:

  Method: A = arima(A, key, value[, key, value[, [...]]])

    Interface to the `arima` command, see the X-13 ARIMA-SEATS reference manual. All the options must be passed by key/value pairs.

  Method: A = automdl(A, key, value[, key, value[, [...]]])

    Interface to the `automdl` command, see the X-13 ARIMA-SEATS reference manual. All the options must be passed by key/value pairs.

  Method: A = regression(A, key, value[, key, value[, [...]]])

    Interface to the `regression` command, see the X-13 ARIMA-SEATS reference manual. All the options must be passed by key/value pairs.

  Method: A = estimate(A, key, value[, key, value[, [...]]])

    Interface to the `estimate` command, see the X-13 ARIMA-SEATS reference manual. All the options must be passed by key/value pairs.

  Method: A = transform(A, key, value[, key, value[, [...]]])

    Interface to the `transform` command, see the X-13 ARIMA-SEATS reference manual. All the options must be passed by key/value pairs. For example, the key/value pair `function,log` instructs the use of a multiplicative instead of an additive seasonal pattern, while `function,auto` triggers an automatic selection between the two based on their fit.

  Method: A = outlier(A, key, value[, key, value[, [...]]])

    Interface to the `outlier` command, see the X-13 ARIMA-SEATS reference manual. All the options must be passed by key/value pairs.

  Method: A = forecast(A, key, value[, key, value[, [...]]])

    Interface to the `forecast` command, see the X-13 ARIMA-SEATS reference manual. All the options must be passed by key/value pairs.

  Method: A = check(A, key, value[, key, value[, [...]]])

    Interface to the `check` command, see the X-13 ARIMA-SEATS reference manual. All the options must be passed by key/value pairs.

  Method: A = x11(A, key, value[, key, value[, [...]]])

    Interface to the `x11` command, see the X-13 ARIMA-SEATS reference manual. All the options must be passed by key/value pairs.

  Method: A = force(A, key, value[, key, value[, [...]]])

    Interface to the `force` command, see the X-13 ARIMA-SEATS reference manual. All the options must be passed by key/value pairs.

  Method: A = history(A, key, value[, key, value[, [...]]])

    Interface to the `history` command, see the X-13 ARIMA-SEATS reference manual. All the options must be passed by key/value pairs.

  Method: A = metadata(A, key, value[, key, value[, [...]]])

    Interface to the `metadata` command, see the X-13 ARIMA-SEATS reference manual. All the options must be passed by key/value pairs.

  Method: A = identify(A, key, value[, key, value[, [...]]])

    Interface to the `identify` command, see the X-13 ARIMA-SEATS reference manual. All the options must be passed by key/value pairs.

  Method: A = pickmdl(A, key, value[, key, value[, [...]]])

    Interface to the `pickmdl` command, see the X-13 ARIMA-SEATS reference manual. All the options must be passed by key/value pairs.

  Method: A = seats(A, key, value[, key, value[, [...]]])

    Interface to the `seats` command, see the X-13 ARIMA-SEATS reference manual. All the options must be passed by key/value pairs.

  Method: A = slidingspans(A, key, value[, key, value[, [...]]])

    Interface to the `slidingspans` command, see the X-13 ARIMA-SEATS reference manual. All the options must be passed by key/value pairs.

  Method: A = spectrum(A, key, value[, key, value[, [...]]])

    Interface to the `spectrum` command, see the X-13 ARIMA-SEATS reference manual. All the options must be passed by key/value pairs.

  Method: A = x11regression(A, key, value[, key, value[, [...]]])

    Interface to the `x11regression` command, see the X-13 ARIMA-SEATS reference manual. All the options must be passed by key/value pairs.

  Method: print(A[, basefilename])

    Prints an `.spc` file with all the X-13 commands. The optional second argument is a row char array specifying the name (without extension) of the file.

  Method: run(A)

    Calls the X-13 binary and run the previously defined commands. All the results are stored in the structure `A.results`. When it makes sense these results are saved in `dseries` objects (e.g. for forecasts or filtered variables).

  Method: clean(A)

    Removes the temporary files created by an x13 run that store the intermediate results. This method allows keeping the main folder clean but will also delete potentially important debugging information.

  Example

  > >     >> ts = dseries(rand(100,1),'1999M1');
  > >     >> o = x13(ts);

  > >     >> o.x11('save','(d11)');
  > >     >> o.automdl('savelog','amd','mixed','no');
  > >     >> o.outlier('types','all','save','(fts)');
  > >     >> o.check('maxlag',24,'save','(acf pcf)');
  > >     >> o.estimate('save','(mdl est)');
  > >     >> o.forecast('maxlead',18,'probability',0.95,'save','(fct fvr)');

  > >     >> o.run();

  > The above example shows a run of X13 with various commands an options specified.

  Example

  > >     %   1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960
  > >     y = [112  115  145  171  196  204  242  284  315  340  360  417 ...   % Jan
  > >          118  126  150  180  196  188  233  277  301  318  342  391 ...   % Feb
  > >          132  141  178  193  236  235  267  317  356  362  406  419 ...   % Mar
  > >          129  135  163  181  235  227  269  313  348  348  396  461 ...   % Apr
  > >          121  125  172  183  229  234  270  318  355  363  420  472 ...   % May
  > >          135  149  178  218  243  264  315  374  422  435  472  535 ...   % Jun
  > >          148  170  199  230  264  302  364  413  465  491  548  622 ...   % Jul
  > >          148  170  199  242  272  293  347  405  467  505  559  606 ...   % Aug
  > >          136  158  184  209  237  259  312  355  404  404  463  508 ...   % Sep
  > >          119  133  162  191  211  229  274  306  347  359  407  461 ...   % Oct
  > >          104  114  146  172  180  203  237  271  305  310  362  390 ...   % Nov
  > >          118  140  166  194  201  229  278  306  336  337  405  432 ]'; % Dec

  > >     ts = dseries(y,'1949M1');
  > >     o = x13(ts);
  > >     o.transform('function','auto','savelog','atr');
  > >     o.automdl('savelog','all');
  > >     o.x11('save','(d11 d10)');
  > >     o.run();
  > >     o.clean();

  > >     y_SA=o.results.d11;
  > >     y_seasonal_pattern=o.results.d10;

  > >     figure('Name','Comparison raw data and SAed data');
  > >     plot(ts.dates,log(o.y.data),ts.dates,log(y_SA.data),ts.dates,log(y_seasonal_pattern.data))

  > The above example shows how to remove a seasonal pattern from a time series. `o.transform('function','auto','savelog','atr')` instructs the subsequent `o.automdl()` command to check whether an additional or a multiplicative pattern fits the data better and to save the result. The result is saved in o.results.autotransform, which in the present example indicates that a log transformation, i.e. a multiplicative model was preferred. The `o.automdl('savelog','all')` automatically selects a fitting ARIMA model and saves all relevant output to the .log-file. The `o.x11('save','(d11, d10)')` instructs `x11` to save both the final seasonally adjusted series `d11` and the final seasonal factor `d10` into `dseries` with the respective names in the output structure `o.results`. `o.clean()` removes the temporary files created by `o.run()`. Among these are the `.log`-file storing summary information, the `.err`-file storing information on problems encountered, the `.out`-file storing the raw output, and the .spc-file storing the specification for the x11 run. There may be further files depending on the output requested. The last part of the example reads out the results and plots a comparison of the logged raw data and its log-additive decomposition into a seasonal pattern and the seasonally adjusted series.

## 6.4. Miscellaneous

### 6.4.1. Time aggregation

> A set of functions allows to convert time series to lower frequencies:

> > - `dseries2M` converts daily time series object to monthly time series object.

> > - `dseries2Q` converts daily or monthly time series object to quarterly time series object.

> > - `dseries2S` converts daily, monthly, or quarterly time series object to bi-annual time series object.

> > - `dseries2Y` converts daily, monthly, quarterly, or bi-annual time series object to annual time series object.

> All these routines have two mandatory input arguments: the first one is a `dseries` object, the second one the name (row char array) of the aggregation method. Possible values for the second argument are:

> > - `arithmetic-average` (for growth rates),

> > - `geometric-average` (for growth factors),

> > - `sum` (for flow variables), and

> > - `end-of-period` (for stock variables).

> Example

> >     >> ts = dseries(rand(12,1),'2000M1')

> >     ts is a dseries object:

> >             | Variable_1
> >     2000M1  | 0.55293
> >     2000M2  | 0.14228
> >     2000M3  | 0.38036
> >     2000M4  | 0.39657
> >     2000M5  | 0.57674
> >     2000M6  | 0.019402
> >     2000M7  | 0.57758
> >     2000M8  | 0.9322
> >     2000M9  | 0.10687
> >     2000M10 | 0.73215
> >     2000M11 | 0.97052
> >     2000M12 | 0.60889

> >     >> ds = dseries2Y(ts, 'end-of-period')

> >     ds is a dseries object:

> >           | Variable_1
> >     2000Y | 0.60889

### 6.4.2. Create time series with a univariate model

> It is possible to expand a `dseries` object recursively with the `from` command. For instance to create a `dseries` object containing the simulation of an ARMA(1,1) model:

> >     >> e = dseries(randn(100, 1), '2000Q1', 'e', '\varepsilon');
> >     >> y = dseries(zeros(100, 1), '2000Q1', 'y');
> >     >> from 2000Q2 to 2024Q4 do y(t)=.9*y(t-1)+e(t)-.4*e(t-1);
> >     >> y

> >     y is a dseries object:

> >            | y
> >     2000Q1 | 0
> >     2000Q2 | -0.95221
> >     2000Q3 | -0.6294
> >     2000Q4 | -1.8935
> >     2001Q1 | -1.1536
> >     2001Q2 | -1.5905
> >     2001Q3 | 0.97056
> >     2001Q4 | 1.1409
> >     2002Q1 | -1.9255
> >     2002Q2 | -0.29287
> >            |
> >     2022Q2 | -1.4683
> >     2022Q3 | -1.3758
> >     2022Q4 | -1.2218
> >     2023Q1 | -0.98145
> >     2023Q2 | -0.96542
> >     2023Q3 | -0.23203
> >     2023Q4 | -0.34404
> >     2024Q1 | 1.4606
> >     2024Q2 | 0.901
> >     2024Q3 | 2.4906
> >     2024Q4 | 0.79661

> The expression following the `do` keyword can be any univariate equation, the only constraint is that the model cannot have leads. It can be a static equation, or a very nonlinear backward equation with an arbitrary number of lags. The `from` command must be followed by a range, which is separated from the (recursive) expression to be evaluated by the `do` command.