Commit 2916aa75 by michel

### manual entry + test for conditional variance decomposistion

`git-svn-id: https://www.dynare.org/svn/dynare/trunk@3112 ac1d8469-bf42-47a9-8791-bf33cf982152`
parent bd3ae5a1
 ... ... @@ -1769,6 +1769,14 @@ The simulated endogenous variables are available in global matrix oo_.e Use the Anderson-Moore Algorithm (AIM) to compute the decision rules, instead of using Dynare's default method based on a generalized Schur decomposition. This option is only valid for first order approximation. See AIM website for more details on the algorithm. = INTEGER = [INTEGER1:INTEGER2] = [INTEGER1 INTEGER2 ...] Computes a conditional variance decomposition for the specified period(s). Conditional variances are given by var(yt+k|t). For period 1, the conditional variance decomposition provides the decomposition of the effects of shocks upon impact. ... ...
 // example 1 from Collard's guide to Dynare var y, c, k, a, h, b; varexo e,u; parameters beta, rho, alpha, delta, theta, psi, tau, phi; alpha = 0.36; rho = 0.95; tau = 0.025; beta = 0.99; delta = 0.025; psi = 0; theta = 2.95; phi = 0.1; model; c*theta*h^(1+psi)=(1-alpha)*y; k = beta*(((exp(b)*c)/(exp(b(+1))*c(+1))) *(exp(b(+1))*alpha*y(+1)+(1-delta)*k)); y = exp(a)*(k(-1)^alpha)*(h^(1-alpha)); k = exp(b)*(y-c)+(1-delta)*k(-1); a = rho*a(-1)+tau*b(-1) + e; b = tau*a(-1)+rho*b(-1) + u; end; initval; y = 1.08068253095672; c = 0.80359242014163; h = 0.29175631001732; k = 5; a = 0; b = 0; e = 0; u = 0; end; shocks; var e; stderr 0.009; var u; stderr 0.009; //var e, u = phi*0.009*0.009; end; stoch_simul(conditional_variance_decomposition = 100,irf=0); stoch_simul(conditional_variance_decomposition = [1 2 3 5 10 100],irf=0) a y k;
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