.. default-domain:: dynare
.. |br| raw:: html
###########
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).
Dates
=====
.. highlight:: matlab
.. _dates in a mod file:
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
1990S2
1990Q4
1990M11
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 secmond 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.
.. _dates-members:
The dates class
---------------
.. class:: dates
:arg freq: equal to 1, 2, 4, 12 or 365 (resp. for annual, bi-annual, quarterly,
monthly, or daily dates).
:arg 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:
.. construct:: dates()
dates(FREQ)
|br| 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);
.. construct:: dates(STRING)
dates(STRING, STRING, ...)
|br| 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).
.. construct:: dates(DATES)
dates(DATES, DATES, ...)
|br| 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.
.. construct:: dates (FREQ, YEAR, SUBPERIOD[, S])
|br| 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 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.
.. datesmethod:: C = append (A, B)
C = append_ (A, B)
|br| 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 =
>> D
D =
>> D.append_('1950Q3')
ans =
.. datesmethod:: B = char (A)
|br| Overloads the MATLAB/Octave ``char`` function. Converts a
``dates`` object into a character array.
*Example*
::
>> A = dates('1950Q1');
> A.char()
ans =
'1950Q1'
.. datesmethod:: C = colon (A, B)
C = colon (A, i, B)
|br| 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 =
>> D = A:2:B
D =
.. datesmethod:: B = copy (A)
|br| Returns a copy of a ``dates`` object.
.. datesmethod:: disp (A)
|br| Overloads the MATLAB/Octave disp function for ``dates`` object.
.. datesmethod:: display (A)
|br| Overloads the MATLAB/Octave display function for ``dates`` object.
*Example*
::
>> disp(B)
B =
>> display(B)
B =
.. datesmethod:: B = double (A)
|br| 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
.. datesmethod:: C = eq (A, B)
|br| 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
.. datesmethod:: C = ge (A, B)
|br| 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
.. datesmethod:: C = gt (A, B)
|br| 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
.. datesmethod:: D = horzcat (A, B, C, ...)
|br| 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 =
.. datesmethod:: C = intersect (A, B)
|br| 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 =
.. datesmethod:: B = isempty (A)
|br| Overloads the MATLAB/Octave ``isempty`` function.
*Example*
::
>> A = dates('1950Q1');
>> A.isempty()
ans =
logical
0
>> B = dates();
>> B.isempty()
ans =
logical
1
.. datesmethod:: C = isequal (A, B)
|br| Overloads the MATLAB/Octave ``isequal`` function.
*Example*
::
>> A = dates('1950Q1');
>> B = dates('1950Q2');
>> isequal(A, B)
ans =
logical
0
.. datesmethod:: C = le (A, B)
|br| 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
.. datesmethod:: B = length (A)
|br| 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
.. datesmethod:: C = lt (A, B)
|br| 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> A = {dates('1950Q2'), dates('1953Q4','1876Q2'), dates('1794Q3')};
>> max(A{:})
ans =
.. datesmethod:: D = min (A, B, C, ...)
|br| 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 =
.. datesmethod:: C = minus (A, B)
|br| 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 =
.. datesmethod:: C = mtimes (A, B)
|br| 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 =
.. datesmethod:: C = ne (A, B)
|br| 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
.. datesmethod:: C = plus (A, B)
|br| 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 =
.. datesmethod:: C = pop (A)
C = pop (A, B)
C = pop_ (A)
C = pop_ (A, B)
|br| 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 =
>> d.pop_(1)
ans =
.. datesmethod:: C = remove (A, B)
C = remove_ (A, B)
|br| 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 =
.. datesmethod:: C = setdiff (A, B)
|br| 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 =
>> setdiff(A, C)
ans =
.. datesmethod:: B = sort (A)
B = sort_ (A)
|br| Sort method for ``dates`` objects. Returns a ``dates`` object
with elements sorted by increasing order.
*Example*
::
>> dd = dates('1945Q3','1938Q4','1789Q3');
>> dd.sort()
ans =
.. datesmethod:: B = strings (A)
|br| 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'}
.. datesmethod:: B = subperiod (A)
|br| 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
.. datesmethod:: B = uminus (A)
|br| 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 =
.. datesmethod:: D = union (A, B, C, ...)
|br| 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 =
.. datesmethod:: B = unique (A)
B = unique_ (A)
|br| 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 =
.. datesmethod:: B = uplus (A)
|br| 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 =
.. datesmethod:: D = vertcat (A, B, C, ...)
|br| 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).
.. datesmethod:: B = year (A)
|br| Returns the year of a date (an integer scalar
between 1 and ``A.freq``).
*Example*
::
>> A = dates('1950Q2');
>> A.subperiod()
ans =
1950
.. _dseries-members:
The dseries class
=================
.. class:: dseries
|br| 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:
.. construct:: dseries ()
dseries (INITIAL_DATE)
|br| Instantiates an empty ``dseries`` object with, if
defined, an initial date given by the single element ``dates``
object *INITIAL_DATE.*
.. construct:: dseries (FILENAME[, INITIAL_DATE])
|br| 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.
.. construct:: dseries (DATA_MATRIX[,INITIAL_DATE[,LIST_OF_NAMES[,TEX_NAMES]]])
dseries (DATA_MATRIX[,RANGE_OF_DATES[,LIST_OF_NAMES[,TEX_NAMES]]])
|br| If the data is not read from a file, it can be provided
via a :math:`T \times N` matrix as the first argument to
``dseries`` ’ constructor, with :math:`T` representing the
number of observations on :math:`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 :math:`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 :math:`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).
.. construct:: 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 :math:`T` observations and ``d`` is a ``dates`` object
with :math:`S> 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
.. dseriesmethod:: [A, B] = align (A, B)
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
.. dseriesmethod:: C = backcast (A, B[, diff])
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.
.. dseriesmethod:: B = baxter_king_filter (A, hf, lf, K)
baxter_king_filter_ (A, hf, lf, K)
|br| 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 :math:`2K+1` points, so
that :math:`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)));
.. dseriesmethod:: B = center (A[, geometric])
center_ (A[, geometric])
|br| 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.
.. dseriesmethod:: C = chain (A, B)
chain_ (A, B)
|br| 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
.. dseriesmethod:: [error_flag, message ] = check (A)
|br| 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.
.. dseriesmethod:: B = copy (A)
|br| 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
.. dseriesmethod:: B = cumprod (A[, d[, v]])
cumprod_ (A[, d[, v]])
|br| 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
.. dseriesmethod:: B = cumsum (A[, d[, v]])
cumsum (A[, d[, v]])
|br| 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
.. dseriesmethod:: B = detrend (A, m)
detrend_ (A, m)
|br| Detrends ``dseries`` object ``A`` with a fitted
polynomial of order ``m``. Note that each variable is
detrended with a different polynomial.
.. dseriesmethod:: B = dgrowth (A)
dgrowth_ (A)
|br| Computes daily growth rates.
.. dseriesmethod:: B = diff (A)
diff_ (A)
|br| Returns the first difference of ``dseries`` object ``A``.
.. datesmethod:: disp (A)
|br| Overloads the MATLAB/Octave disp function for ``dseries`` object.
.. datesmethod:: display (A)
|br| 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.
.. dseriesmethod:: C = eq (A, B)
|br| Overloads the MATLAB/Octave ``eq`` (equal, ``==``)
operator. ``dseries`` objects ``A`` and ``B`` must have the
same number of observations (say, :math:`T`) and variables
(:math:`N`). The returned argument is a :math:`T \times N`
matrix of logicals. Element :math:`(i,j)` of ``C`` is
equal to ``true`` if and only if observation :math:`i` for
variable :math:`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
.. dseriesmethod:: l = exist (A, varname)
|br| 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
.. dseriesmethod:: B = exp (A)
exp_ (A)
|br| Overloads the MATLAB/Octave ``exp`` function for
``dseries`` objects.
*Example*
::
>> ts0 = dseries(rand(10,1));
>> ts1 = ts0.exp();
.. dseriesmethod:: C = extract (A, B[, ...])
|br| 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
.. dseriesmethod:: f = firstdate (A)
|br| Returns the first period in ``dseries`` object ``A``.
.. dseriesmethod:: f = firstobservedperiod (A)
|br| Returns the first period where all the variables in ``dseries`` object ``A`` are observed (non NaN).
.. dseriesmethod:: B = flip (A)
flip_ (A)
|br| Flips the rows in the data member (without changing the
periods order).
.. dseriesmethod:: f = frequency (B)
|br| Returns the frequency of the variables in ``dseries`` object ``B``.
*Example*
::
>> ts = dseries(randn(3,2),'1973Q1');
>> ts.frequency
ans =
4
.. dseriesmethod:: D = horzcat (A, B[, ...])
|br| 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
.. dseriesmethod:: B = hpcycle (A[, lambda])
hpcycle_ (A[, lambda])
|br| 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)));
.. dseriesmethod:: B = hptrend (A[, lambda])
hptrend_ (A[, lambda])
|br| 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)));
.. dseriesmethod:: C = insert (A, B, I)
|br| 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
.. dseriesmethod:: B = isempty (A)
|br| Overloads the MATLAB/octave’s ``isempty`` function. Returns
``true`` if ``dseries`` object ``A`` is empty.
.. dseriesmethod:: C = isequal (A, B)
|br| Overloads the MATLAB/octave’s ``isequal`` function. Returns
``true`` if ``dseries`` objects ``A`` and ``B`` are identical.
.. dseriesmethod:: C = isinf (A)
|br| 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)``.
.. dseriesmethod:: C = isnan (A)
|br| 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)``.
.. dseriesmethod:: C = isreal (A)
|br| 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)``.
.. dseriesmethod:: B = lag (A[, p])
lag_ (A[, p])
|br| Returns lagged time series. Default value of integer scalar ``p``, the number
of lags, is ``1``.
*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
.. dseriesmethod:: l = lastdate (B)
|br| Returns the last period in ``dseries`` object ``B``.
*Example*
::
>> ts = dseries(randn(3,2),'1973Q1');
>> ts.lastdate()
ans =
.. dseriesmethod:: f = lastobservedperiod (A)
|br| Returns the last period where all the variables in ``dseries`` object ``A`` are observed (non NaN).
.. dseriesmethod:: f = lastobservedperiods (A)
|br| Returns for each variable the last period without missing
observations in ``dseries`` object ``A``. Output argument ``f`` is a
structure, each field name is the name of a variable in ``A``, each field
content is a singleton ``date`` object.
.. dseriesmethod:: B = lead (A[, p])
lead_ (A[, p])
|br| Returns lead time series. Default value of integer scalar
``p``, the number of leads, is ``1``. As in the ``lag``
method, the ``dseries`` class overloads the parenthesis so
that ``ts.lead(p)`` is 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 overloading of the parenthesis for ``dseries`` objects,
allows to easily create new ``dseries`` objects by
copying/pasting equations declared in the ``model`` block. For
instance, if an Euler equation is defined 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).
.. dseriesmethod:: B = lineartrend (A)
|br| 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
.. dseriesmethod:: B = log (A)
log_ (A)
|br| Overloads the MATLAB/Octave ``log`` function for
``dseries`` objects.
*Example*
::
>> ts0 = dseries(rand(10,1));
>> ts1 = ts0.log();
.. dseriesmethod:: B = mdiff (A)
mdiff_ (A)
B = mgrowth (A)
mgrowth_ (A)
|br| Computes monthly differences or growth rates of variables in
``dseries`` object ``A``.
.. dseriesmethod:: B = mean (A[, geometric])
|br| Overloads the MATLAB/Octave ``mean`` function for
``dseries`` objects. Returns the mean of each variable in
``dseries`` object ``A``. If the second argument is ``true``
the geometric mean is computed, otherwise (default) the
arithmetic mean is reported.
.. dseriesmethod:: C = merge (A, B[, legacy])
|br| Merges two ``dseries`` objects ``A`` and ``B`` in
``dseries`` object ``C``. Objects ``A`` and ``B`` need to have
common frequency but can be defined on different time
ranges. If a variable, say ``x``, is defined both in
``dseries`` objects ``A`` and ``B``, then the ``merge`` will
select the variable ``x`` as defined in the second input
argument, ``B``, except for the NaN elements in ``B`` if
corresponding elements in ``A`` (ie same periods) are well
defined numbers. This behaviour can be changed by setting the
optional argument ``legacy`` equal to true, in which case the
second variable overwrites the first one even if the second
variable has NaNs.
*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
.. dseriesmethod:: C = minus (A, B)
|br| Overloads the MATLAB/Octave ``minus`` (``-``) operator
for ``dseries`` objects, element by element subtraction. If
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 :math:`T_A` and :math:`T_B`
observations and :math:`N_A` and :math:`N_B` variables, then
:math:`N_A` must be equal to :math:`N_B` or :math:`1` and
:math:`N_B` must be equal to :math:`N_A` or :math:`1`. If
:math:`T_A=T_B`, ``isequal(A.init,B.init)`` returns ``1`` and
:math:`N_A=N_B`, then the ``minus`` operator will compute for
each couple :math:`(t,n)`, with :math:`1\le t\le T_A` and
:math:`1\le n\le N_A`,
``C.data(t,n)=A.data(t,n)-B.data(t,n)``. If :math:`N_B` is
equal to :math:`1` and :math:`N_A>1`, the smaller ``dseries``
object (``B``) is “broadcast” across the larger ``dseries``
(``A``) so that they have compatible shapes, the ``minus``
operator will subtract the variable defined in ``B`` from each
variable in ``A``. If ``B`` is a double scalar, then the
method ``minus`` will subtract ``B`` from all the
observations/variables in ``A``. If ``B`` is a row vector of
length :math:`N_A`, then the ``minus`` method will subtract
``B(i)`` from all the observations of variable ``i``, for
:math:`i=1,...,N_A`. If ``B`` is a column vector of length
:math:`T_A`, then 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
.. dseriesmethod:: C = mpower (A, B)
|br| Overloads the MATLAB/Octave ``mpower`` (``^``) operator for ``dseries``
objects and computes element-by-element power. ``A`` is a
``dseries`` object with ``N`` variables and ``T``
observations. If ``B`` is a real scalar, then ``mpower(A,B)``
returns a ``dseries`` object ``C`` with
``C.data(t,n)=A.data(t,n)^C``. If ``B`` is a ``dseries``
object with ``N`` variables and ``T`` observations then
``mpower(A,B)`` returns a ``dseries`` object ``C`` with
``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
.. dseriesmethod:: C = mrdivide (A, B)
|br| Overloads the MATLAB/Octave ``mrdivide`` (``/``) operator for
``dseries`` objects, element by element division (like the
``./`` MATLAB/Octave operator). If 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 :math:`T_A` and :math:`T_B` observations and :math:`N_A`
and :math:`N_B` variables, then :math:`N_A` must be equal to
:math:`N_B` or :math:`1` and :math:`N_B` must be equal to
:math:`N_A` or :math:`1`. If :math:`T_A=T_B`,
``isequal(A.init,B.init)`` returns ``1`` and :math:`N_A=N_B`,
then the ``mrdivide`` operator will compute for each couple
:math:`(t,n)`, with :math:`1\le t\le T_A` and :math:`1\le n\le
N_A`, ``C.data(t,n)=A.data(t,n)/B.data(t,n)``. If :math:`N_B`
is equal to :math:`1` and :math:`N_A>1`, the smaller
``dseries`` object (``B``) is “broadcast” across the larger
``dseries`` (``A``) so that they have compatible shapes. In
this case the ``mrdivide`` operator will divide each variable
defined in A by the variable in B, observation per
observation. If B is a double scalar, then ``mrdivide`` will
divide all the observations/variables in ``A`` by ``B``. If
``B`` is a row vector of length :math:`N_A`, then ``mrdivide``
will divide all the observations of variable ``i`` by
``B(i)``, for :math:`i=1,...,N_A`. If ``B`` is a column vector
of length :math:`T_A`, then ``mrdivide`` will perform a
division of all the variables by ``B``, element by element.
*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
.. dseriesmethod:: C = mtimes (A, B)
|br| Overloads the MATLAB/Octave ``mtimes`` (``*``) operator
for ``dseries`` objects and the Hadammard product (the .*
MATLAB/Octave operator). If 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 :math:`T_A` and :math:`_B` observations and :math:`N_A`
and :math:`N_B` variables, then :math:`N_A` must be equal to
:math:`N_B` or :math:`1` and :math:`N_B` must be equal to
:math:`N_A` or :math:`1`. If :math:`T_A=T_B`,
``isequal(A.init,B.init)`` returns ``1`` and :math:`N_A=N_B`,
then the ``mtimes`` operator will compute for each couple
:math:`(t,n)`, with :math:`1\le t\le T_A` and :math:`1\le n\le
N_A`, ``C.data(t,n)=A.data(t,n)*B.data(t,n)``. If :math:`N_B`
is equal to :math:`1` and :math:`N_A>1`, the smaller
``dseries`` object (``B``) is “broadcast” across the larger
``dseries`` (``A``) so that they have compatible shapes,
``mtimes`` operator will multiply each variable defined in
``A`` by the variable in ``B``, observation per
observation. If ``B`` is a double scalar, then the method
``mtimes`` will multiply all the observations/variables in
``A`` by ``B``. If ``B`` is a row vector of length
:math:`N_A`, then the ``mtimes`` method will multiply all the
observations of variable ``i`` by ``B(i)``, for
:math:`i=1,...,N_A`. If ``B`` is a column vector of length
:math:`T_A`, then the ``mtimes`` method will perform a
multiplication of all the variables by ``B``, element by
element.
.. dseriesmethod:: B = nanmean (A[, geometric])
|br| Overloads the MATLAB/Octave ``nanmean`` function for
``dseries`` objects. Returns the mean of each variable in
``dseries`` object ``A`` ignoring the NaN values. If the
second argument is ``true`` the geometric mean is computed,
otherwise (default) the arithmetic mean is reported.
.. dseriesmethod:: B = nanstd (A[, geometric])
|br| Overloads the MATLAB/Octave ``nanstd`` function for
``dseries`` objects. Returns the standard deviation of each
variable in ``dseries`` object ``A`` ignoring the NaN
values. If the second argument is ``true`` the geometric std
is computed, default value of the second argument is
``false``.
.. dseriesmethod:: C = ne (A, B)
|br| Overloads the MATLAB/Octave ``ne`` (not equal, ``~=``)
operator. ``dseries`` objects ``A`` and ``B`` must have the
same number of observations (say, :math:`T`) and variables
(:math:`N`). The returned argument is a :math:`T` by :math:`N`
matrix of zeros and ones. Element :math:`(i,j)` of ``C`` is
equal to ``1`` if and only if observation :math:`i` for
variable :math:`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
.. dseriesmethod:: B = nobs (A)
|br| Returns the number of observations in ``dseries`` object
``A``.
*Example*
::
>> ts0 = dseries(randn(10));
>> ts0.nobs
ans =
10
.. dseriesmethod:: B = onesidedhpcycle (A[, lambda[, init]])
onesidedhpcycle_ (A[, lambda[, init]])
|br| Extracts the cycle component from a ``dseries`` ``A``
object using a one sided HP filter (with a Kalman filter) and
returns a ``dseries`` object, ``B``. The default value for
``lambda``, the smoothing parameter, is ``1600``. By default,
if ``ìnit`` is not provided, the initial value is based on the
first two observations.
.. dseriesmethod:: B = onesidedhptrend (A[, lambda[, init]])
onesidedhptrend_ (A[, lambda[, init]])
|br| Extracts the trend component from a ``dseries`` ``A``
object using a one sided HP filter (with a Kalman filter) and
returns a ``dseries`` object, ``B``. The default value for
``lambda``, the smoothing parameter, is ``1600``. By default,
if ``ìnit`` is not provided, the initial value is based on the
first two observations.
.. dseriesmethod:: h = plot (A)
h = plot (A, B)
h = plot (A[, ...])
h = plot (A, B[, ...])
|br| Overloads MATLAB/Octave’s ``plot`` function for
``dseries`` objects. Returns a MATLAB/Octave plot handle, that
can be used to modify the properties of the plotted time
series. If only one ``dseries`` object, ``A``, is passed as
argument, then the plot function will put the associated dates
on the x-abscissa. If this ``dseries`` object contains only
one variable, additional arguments can be passed to modify the
properties of the plot (as one would do with the
MATLAB/Octave’s version of the plot function). If ``dseries``
object ``A`` contains more than one variable, it is not
possible to pass these additional arguments and the properties
of the plotted time series must be modified using the returned
plot handle and the MATLAB/Octave ``set`` function (see
example below). If two ``dseries`` objects, ``A`` and ``B``,
are passed as input arguments, the plot function will plot the
variables in ``A`` against the variables in ``B`` (the number
of variables in each object must be the same otherwise an
error is issued). Again, if each object contains only one
variable, additional arguments can be passed to modify the
properties of the plotted time series, otherwise the
MATLAB/Octave ``set`` command has to be used.
*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');
.. dseriesmethod:: C = plus (A, B)
|br| Overloads the MATLAB/Octave ``plus`` (``+``) operator for
``dseries`` objects, element by element addition. If 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 :math:`T_A` and :math:`T_B`
observations and :math:`N_A` and :math:`N_B` variables, then
:math:`N_A` must be equal to :math:`N_B` or :math:`1` and
:math:`N_B` must be equal to :math:`N_A` or :math:`1`. If
:math:`T_A=T_B`, ``isequal(A.init,B.init)`` returns ``1`` and
:math:`N_A=N_B`, then the ``plus`` operator will compute for
each couple :math:`(t,n)`, with :math:`1\le t\le T_A` and
:math:`1\le n\le N_A`,
``C.data(t,n)=A.data(t,n)+B.data(t,n)``. If :math:`N_B` is
equal to :math:`1` and :math:`N_A>1`, the smaller ``dseries``
object (``B``) is “broadcast” across the larger ``dseries``
(``A``) so that they have 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 :math:`N_A`, then the ``plus``
method will add ``B(i)`` to all the observations of variable
``i``, for :math:`i=1,...,N_A`. If ``B`` is a column vector of
length :math:`T_A`, then the ``plus`` method will add ``B`` to
all the variables.
.. dseriesmethod:: C = pop (A[, B])
pop_ (A[, B])
|br| Removes variable ``B`` from ``dseries`` object ``A``. By
default, if the second argument is not provided, 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
.. dseriesmethod:: A = projection (A, info, periods)
|br| Projects variables in dseries object ``A``. ``info`` is
is a :math:`n \times 3` cell array. Each row provides
informations necessary to project a variable. The first column
contains the name of variable (row char array). the second
column contains the name of the method used to project the
associated variable (row char array), possible values are
``'Trend'``, ``'Constant'``, and ``'AR'``. Last column
provides quantitative information about the projection. If the
second column value is ``'Trend'``, the third column value is
the growth factor of the (exponential) trend. If the second
column value is ``'Constant'``, the third column value is the
level of the variable. If the second column value is ``'AR'``,
the third column value is the autoregressive parameter. The
variables can be projected with an AR(p) model, if the third
column contains a 1×p vector of doubles. The stationarity of
the AR(p) model is not tested. The case of the constant
projection, using the last value of the variable, is covered
with 'Trend' and a growth factor equal to 1, or 'AR' with an
autoregressive parameter equal to one (random walk). This
projection routine only deals with 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);
.. dseriesmethod:: B = qdiff (A)
B = qgrowth (A)
qdiff_ (A)
qgrowth_ (A)
|br| 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
.. dseriesmethod:: C = remove (A, B)
remove_ (A, B)
|br| If ``B`` is a row char array, the name of a variable, these methods
are aliases for ``pop`` and ``pop_`` methods with two arguments. They
remove variable ``B`` from ``dseries`` object ``A``. To remove more than
one variable, one can pass a cell of row char 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 shorter syntax is available: ``remove(ts,'Variable_2')``
is equivalent to ``ts{'Variable_2'} = []`` (``[]`` can be
replaced by any empty object). This alternative syntax is
useful if more than one variable has to be removed. For
instance::
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.
.. dseriesmethod:: B = rename (A, oldname, newname)
rename_ (A, oldname, newname)
|br| Rename variable ``oldname`` to ``newname`` in ``dseries``
object ``A``. Returns a ``dseries`` object. If more than one
variable needs to be renamed, it is possible to pass cells of
char arrays as 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
.. dseriesmethod:: C = rename (A, newname)
rename_ (A, newname)
|br| Replace the names in ``A`` with those passed in the cell
string array ``newname``. ``newname`` must have the same
number of elements as ``dseries`` object ``A`` has
variables. Returns a ``dseries`` object.
*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
.. dseriesmethod:: A = resetops (A, ops)
|br| Redefine ``ops`` member.
.. dseriesmethod:: A = resetags (A, ops)
|br| Redefine ``tags`` member.
.. dseriesmethod:: B = round (A[, n])
round_ (A[, n])
|br| Rounds to the nearest decimal or integer. ``n`` is the
precision parameter (number of decimals), default value is 0
meaning that that by default the method rounds to the nearest
integer.
*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
.. dseriesmethod:: save (A, basename[, format])
|br| Overloads the MATLAB/Octave ``save`` function and saves
``dseries`` object ``A`` to disk. Possible formats are ``mat``
(this is the default), ``m`` (MATLAB/Octave script), and
``csv`` (MATLAB binary data file). The name of the file
without 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.
.. dseriesmethod:: B = set_names(A, s1, s2, ...)
|br| Renames variables in ``dseries`` object ``A`` and returns
a ``dseries`` object ``B`` with new names ``s1``, ``s2``,
... The number of input arguments after the first one
(``dseries`` object ``A``) must be equal to ``A.vobs`` (the
number of variables in ``A``). ``s1`` will be the name of the
first variable in ``B``, ``s2`` the name of the second
variable in ``B``, and so on.
*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
.. dseriesmethod:: [T, N ] = size(A[, dim])
Overloads the MATLAB/Octave’s ``size`` function. Returns the
number of observations in ``dseries`` object ``A``
(i.e. ``A.nobs``) and the number of variables
(i.e. ``A.vobs``). If a second input argument is passed, the
``size`` function returns the number of observations if
``dim=1`` or the number of variables if ``dim=2`` (for all
other values of ``dim`` an error is issued).
*Example*
::
>> ts0 = dseries(ones(1,3));
>> ts0.size()
ans =
1 3
.. dseriesmethod:: B = std (A[, geometric])
|br| Overloads the MATLAB/Octave ``std`` function for
``dseries`` objects. Returns the standard deviation of each
variable in ``dseries`` object ``A``. If the second argument
is ``true`` the geometric standard deviation is computed
(default value of the second argument is ``false``).
.. dseriesmethod:: B = subsample (A, d1, d2)
|br| Returns a subsample, for periods between ``dates`` ``d1``
and ``d2``. The same can be achieved by indexing a
``dseries`` object with a ``dates`` object, but the
``subsample`` method is easier to use programmatically.
*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
.. dseriesmethod:: A = tag (A, a[, b, c])
|br| Add 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');
.. dseriesmethod:: B = tex_rename (A, name, newtexname)
B = tex_rename (A, newtexname)
tex_rename_ (A, name, newtexname)
tex_rename_ (A, newtexname)
|br| Redefines the tex name of variable ``name`` to
``newtexname`` in ``dseries`` object ``A``. Returns a
``dseries`` object.
With only two arguments ``A`` and ``newtexname``, it redefines
the tex names of the ``A`` to those contained in
``newtexname``. Here, ``newtexname`` is a cell string array
with the same number of entries as variables in ``A``.
.. dseriesmethod:: B = uminus(A)
|br| Overloads ``uminus`` (``-``, unary minus) for ``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
.. dseriesmethod:: D = vertcat (A, B[, ...])
|br| Overloads the ``vertcat`` MATLAB/Octave method for
``dseries`` objects. This method is used to append more
observations to a ``dseries`` object. Returns a ``dseries``
object ``D`` containing the variables in ``dseries`` objects
passed as inputs. All the input arguments must be ``dseries``
objects with the same variables defined on 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
.. dseriesmethod:: B = vobs (A)
|br| Returns the number of variables in ``dseries`` object
``A``.
*Example*
::
>> ts0 = dseries(randn(10,2));
>> ts0.vobs
ans =
2
.. dseriesmethod:: B = ydiff (A)
B = ygrowth (A)
ydiff_ (A)
ygrowth_ (A)
|br| Computes yearly differences or growth rates.
.. _x13-members:
X-13 ARIMA-SEATS interface
==========================
.. class:: x13
|br| 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:
:arg y: ``dseries`` object with a single variable.
:arg x: ``dseries`` object with an arbitrary number of variables (to be used in the REGRESSION block).
:arg arima: structure containing the options of the ARIMA model command.
:arg automdl: structure containing the options of the ARIMA model selection command.
:arg regression: structure containing the options of the Regression command.
:arg estimate: structure containing the options of the estimation command.
:arg transform: structure containing the options of the transform command.
:arg outlier: structure containing the options of the outlier command.
:arg forecast: structure containing the options of the forecast command.
:arg check: structure containing the options of the check command.
:arg x11: structure containing the options of the X11 command.
:arg force: structure containing the options of the force command.
:arg history: structure containing the options of the history command.
:arg metadata: structure containing the options of the metadata command.
:arg identify: structure containing the options of the identify command.
:arg pickmdl: structure containing the options of the pickmdl command.
:arg seats: structure containing the options of the seats command.
:arg slidingspans: structure containing the options of the slidingspans command.
:arg spectrum: structure containing the options of the spectrum command.
:arg x11regression: structure containing the options of the x11Regression command.
:arg results: structure containing the results returned by x13.
:arg commands: cell array containing the list of commands.
All these members are private. The following constructors are available:
.. construct:: x13 (y)
|br| 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.
.. construct:: x13 (y, x)
|br| 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:
.. x13method:: 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.
.. x13method:: 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.
.. x13method:: 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.
.. x13method:: 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.
.. x13method:: 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.
.. x13method:: 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.
.. x13method:: 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.
.. x13method:: 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.
.. x13method:: 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.
.. x13method:: 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.
.. x13method:: 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.
.. x13method:: 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.
.. x13method:: 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.
.. x13method:: 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.
.. x13method:: 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.
.. x13method:: 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.
.. x13method:: 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.
.. x13method:: 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.
.. x13method:: 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.
.. x13method:: 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).
.. x13method:: 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.
Miscellaneous
=============
Time aggregation
----------------
|br| 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.
|br| 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
Create time series with a univariate model
------------------------------------------
|br| 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.