(available currently only in the Matlab and the Scilab versions)
OPTIONS:
- DR_ALGO = [0,1]
- Specify the algorithm used for computing the quadratic approximation of the decision rules:
- 0: uses a "pure" perturbation approach as in Schmitt-Grohe and Uribe (2002) (default)
- 1: moves the point around which the Taylor expansion is computed toward the means of the distribution as in Collard and Juillard (2001)
- AR = Integer
- Order of autocorrelation coefficients to compute and to print (default = 5)
- NOCORR
- Doesn't print the correlation matrix (default = PRINT)
- DROP = Integer
- Number of points dropped at the beginning of simulation before computing the summary statistics (default = 100)
- IRF = Integer
- Number of periods on which to compute the IRFs (default = 40)
- NOFUNCTIONS
- Doesn't print the coefficients of the approximated solution
- LINEAR
- Indicates that the original model is linear
- NOMOMENTS
- Doesn't print moments of the endogenous variables
- ORDER = [1,2]
- Order of Taylor approximation (default = 2)
- REPLIC = Integer
- Number of simulated series used to compute the IRFs (default = 1, if order = 1, and 50 otherwise)
When a LIST OF VARIABLES is specified, results are displayed only for these variables.
stoch_simul computes a Taylor approximation of the decision and transition functions for the model, impulse repsonse functions and various descriptive statistics (moments, variance decomposition, correlation and autocorrelation coefficients). For correlated shocks, the variance decomposition is computed as in the VAR literature through a Cholesky decomposition of the covariance matrix of the exogenous variables. When the shocks are correlated, the variance decomposition depends upon the order of the variables in the varexo command.
The covariance matrix of the shocks is specified either with the shocks command or with the Sigma_e command.
Example 1
shocks;
var e;
stderr 0.0348;
end;
stoch_simul;
performs the simulation of the 2nd order approximation of a model with a single stochastic shock, e, with a standard error of 0.0348.
Example 2
stoch_simul(linear,irf=60) y k;
performs the simulation of a linear model and displays impulse response functions on 60 periods for variables y and k.
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