Commit 2916aa75 authored by michel's avatar michel
Browse files

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 <varname>oo_.e
<listitem><para>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 <ulink url="http://www.federalreserve.gov/Pubs/oss/oss4/aimindex.html">AIM website</ulink> for more details on the algorithm.</para>
</listitem>
</varlistentry>
<varlistentry id="conditional_variance_decomposition">
<term><option>conditional_variance_decomposition</option> = <replaceable>INTEGER</replaceable></term></varlistentry>
<varlistentry>
<term><option>conditional_variance_decomposition</option> = [<replaceable>INTEGER1</replaceable>:<replaceable>INTEGER2</replaceable>]</term></varlistentry>
<varlistentry>
<term><option>conditional_variance_decomposition</option> = [<replaceable>INTEGER1</replaceable> <replaceable>INTEGER2</replaceable> ...]</term>
<listitem><para>Computes a conditional variance decomposition for the specified period(s). Conditional variances are given by var(y<subscript>t+k</subscript>|t). For period 1, the conditional variance decomposition provides the decomposition of the effects of shocks upon impact.</para></listitem>
</varlistentry>
</variablelist>
</refsect1>
......
// 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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