manual entry + test for conditional variance decomposistion
git-svn-id: https://www.dynare.org/svn/dynare/trunk@3112 ac1d8469-bf42-47a9-8791-bf33cf982152time-shift
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@ -1769,6 +1769,14 @@ The simulated endogenous variables are available in global matrix <varname>oo_.e
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<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>
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</listitem>
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</varlistentry>
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<varlistentry id="conditional_variance_decomposition">
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<term><option>conditional_variance_decomposition</option> = <replaceable>INTEGER</replaceable></term></varlistentry>
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<varlistentry>
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<term><option>conditional_variance_decomposition</option> = [<replaceable>INTEGER1</replaceable>:<replaceable>INTEGER2</replaceable>]</term></varlistentry>
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<varlistentry>
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<term><option>conditional_variance_decomposition</option> = [<replaceable>INTEGER1</replaceable> <replaceable>INTEGER2</replaceable> ...]</term>
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<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>
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</varlistentry>
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</variablelist>
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</refsect1>
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@ -0,0 +1,46 @@
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// example 1 from Collard's guide to Dynare
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var y, c, k, a, h, b;
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varexo e,u;
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parameters beta, rho, alpha, delta, theta, psi, tau, phi;
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alpha = 0.36;
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rho = 0.95;
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tau = 0.025;
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beta = 0.99;
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delta = 0.025;
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psi = 0;
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theta = 2.95;
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phi = 0.1;
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model;
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c*theta*h^(1+psi)=(1-alpha)*y;
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k = beta*(((exp(b)*c)/(exp(b(+1))*c(+1)))
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*(exp(b(+1))*alpha*y(+1)+(1-delta)*k));
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y = exp(a)*(k(-1)^alpha)*(h^(1-alpha));
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k = exp(b)*(y-c)+(1-delta)*k(-1);
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a = rho*a(-1)+tau*b(-1) + e;
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b = tau*a(-1)+rho*b(-1) + u;
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end;
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initval;
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y = 1.08068253095672;
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c = 0.80359242014163;
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h = 0.29175631001732;
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k = 5;
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a = 0;
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b = 0;
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e = 0;
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u = 0;
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end;
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shocks;
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var e; stderr 0.009;
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var u; stderr 0.009;
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//var e, u = phi*0.009*0.009;
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end;
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stoch_simul(conditional_variance_decomposition = 100,irf=0);
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stoch_simul(conditional_variance_decomposition = [1 2 3 5 10 100],irf=0) a y k;
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