Update userguide .mod files: reconcile text with updated .mod files
Houtan Bastani
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@ -225,7 +225,7 @@ end;}\\
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We add the following commands to ask Dynare to run a basic estimation of our model:\\
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\\
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\texttt{estimation(datafile=fsdat,nobs=192,loglinear,mh\_replic=2000,\\
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mode\_compute=4,mh\_nblocks=2,mh\_drop=0.45,mh\_jscale=0.65);}\\
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mode\_compute=6,mh\_nblocks=2,mh\_drop=0.45,mh\_jscale=0.65);}\\
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\textsf{\textbf{NOTE!}} As mentioned earlier, we need to instruct Dynare to log-linearize our model, since it contains non-linear equations in non-stationary variables. A simple linearization would fail as these variables do not have a steady state. Fortunately, taking the log of the equations involving non-stationary variables does the job of linearizing them.\\
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@ -234,19 +234,19 @@ We have seen each part of the .mod separately; it's now time to get a picture of
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\\
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\texttt{var m P c e W R k d n l Y\_obs P\_obs y dA; \\
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varexo e\_a e\_m;\\
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\\
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parameters alp, bet, gam, mst, rho, psi, del;
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parameters alp, bet, gam, mst, rho, psi, del;\\
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\\
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model;\\
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dA = exp(gam+e\_a);\\
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log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e\_m;\\
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-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k\textasciicircum (alp-1)\\
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*n(+1)\textasciicircum (1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;\\
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-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))\\
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*k\textasciicircum (alp-1)*n(+1)\textasciicircum (1-alp)+(1-del)\\
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*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;\\
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W = l/n;\\
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-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;\\
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R = P*(1-alp)*exp(-alp*(gam+e\_a))*k(-1)\textasciicircum alp*n\textasciicircum (-alp)/W;\\
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1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e\_a))*k(-1)\textasciicircum alp*n\textasciicircum (1-alp)/(m*l*c(+1)*P(+1)) = 0;\\
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c+k = exp(-alp*(gam+e\_a))*k(-1)\textasciicircum alp*n\textasciicircum (1-alp)+(1-del)*exp(-(gam+e\_a))*k(-1);\\
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1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e\_a))*k(-1)\textasciicircum alp*n\textasciicircum (1-alp)/\\(m*l*c(+1)*P(+1)) = 0;\\
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c+k = exp(-alp*(gam+e\_a))*k(-1)\textasciicircum alp*n\textasciicircum (1-alp)+(1-del)\\*exp(-(gam+e\_a))*k(-1);\\
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P*c = m;\\
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m-1+d = l;\\
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e = exp(e\_a);\\
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@ -262,7 +262,7 @@ P\_obs (log(mst)-gam);\\
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Y\_obs (gam);\\
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end;\\
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\\
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unit\_root\_vars = P\_obs Y\_obs;\\
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unit\_root\_vars P\_obs Y\_obs;\\
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\\
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initval;\\
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k = 6;\\
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@ -298,7 +298,7 @@ stderr e\_m, inv\_gamma\_pdf, 0.008862, inf;\\
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end;\\
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\\
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estimation(datafile=fsdat,nobs=192,loglinear,mh\_replic=2000,\\
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mode\_compute=4,mh\_nblocks=2,mh\_drop=0.45,mh\_jscale=0.65);}\\
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mode\_compute=6,mh\_nblocks=2,mh\_drop=0.45,mh\_jscale=0.65);}\\
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\\
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\subsection{Summing it up}
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@ -35,7 +35,7 @@ end;}\\
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\section{Declaring observable variables}
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This should not come as a surprise. Dynare must know which variables are observable for the estimation procedure. \textsf{\textbf{NOTE!}} These variables must be available in the data file, as explained in section \ref{sec:estimate} below. For the moment, we write:\\
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\\
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\texttt{varobs Y;}\\
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\texttt{varobs y;}\\
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\section{Specifying the steady state} \label{sec:ssest}
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Before Dynare estimates a model, it first linearizes it around a steady state. Thus, a steady state must exist for the model and although Dynare can calculate it, we must give it a hand by declaring approximate values for the steady state. This is just as explained in details and according to the same syntax outlined in chapter \ref{ch:solbase}, covering the \texttt{initval}, \texttt{steady} and \texttt{check} commands. In fact, as this chapter uses the same model as that outlined in chapter \ref{ch:solbase}, the steady state block will look exactly the same.\\
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@ -138,7 +138,8 @@ displayed). Actually seeing if the various blocks of Metropolis-Hastings runs co
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Finally, coming back to our example, we could choose a standard option:\\
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\\
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\texttt{estimation(datafile=simuldataRBC,nobs=200,first\_obs=500,\\
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mh\_replic=2000,mh\_nblocks=2,mh\_drop=0.45,mh\_jscale=0.8); }\\
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mh\_replic=2000,mh\_nblocks=2,mh\_drop=0.45,mh\_jscale=0.8,\\
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mode\_compute=6); }\\
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This ends our description of the .mod file.
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@ -147,7 +148,6 @@ To summarize and to get a complete perspective on our work so far, here is the c
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\\
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\texttt{var y c k i l y\_l w r z;\\
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varexo e;\\
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\\
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parameters beta psi delta alpha rho epsilon;\\
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\\
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model;\\
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@ -162,7 +162,7 @@ model;\\
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z = rho*z(-1)+e;\\
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end;\\
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\\
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varobs Y;\\
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varobs y;\\
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\\
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initval;\\
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k = 9;\\
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@ -175,7 +175,6 @@ initval;\\
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end;\\
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\\
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steady;\\
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\\
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check;\\
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\\
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estimated\_params;\\
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@ -189,7 +188,8 @@ stderr e, inv\_gamma\_pdf, 0.01, inf;\\
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end;\\
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\\
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estimation(datafile=simuldataRBC,nobs=200,first\_obs=500,\\
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mh\_replic=2000,mh\_nblocks=2,mh\_drop=0.45,mh\_jscale=0.8); }
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mh\_replic=2000,mh\_nblocks=2,mh\_drop=0.45,mh\_jscale=0.8,\\
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mode\_compute=6); }
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\\
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@ -87,8 +87,9 @@ So that you can gain experience by manipulating the entire model, here is the co
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\\
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\\
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\texttt{var y, c, k, a, h, b;\\
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varexo e,u;\\
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varexo e, u;\\
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parameters beta, rho, alpha, delta, theta, psi, tau;\\
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\\
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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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@ -96,6 +97,7 @@ 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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\\
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phi = 0.1;\\
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\\
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model;\\
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@ -452,13 +452,14 @@ For completion's sake, and for the pleasure of seeing our work bear its fruits,
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\texttt{var y c k i l y\_l w r z;\\
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varexo e;\\
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parameters beta psi delta alpha rho sigma epsilon;\\
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parameters beta psi delta alpha rho gamma sigma epsilon;\\
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\\
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alpha = 0.33;\\
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beta = 0.99;\\
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delta = 0.023;\\
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psi = 1.75;\\
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rho = 0.95; \\
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sigma = (0.007\/(1-alpha));\\
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rho = 0.95;\\
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sigma = (0.007/(1-alpha));\\
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epsilon = 10;\\
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\\
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model;\\
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@ -475,23 +476,22 @@ end;\\
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\\
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initval;\\
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k = 9;\\
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c = 0.7;\\
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c = 0.76;\\
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l = 0.3;\\
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w = 2.0;\\
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r = 0;\\
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z = 0; \\
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w = 2.07;\\
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r = 0.03;\\
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z = 0;\\
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e = 0;\\
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end;\\
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\\
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steady;\\
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\\
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check;\\
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\\
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shocks;\\
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var e = sigma\textasciicircum 2;\\
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end;\\
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\\
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stoch\_simul(periods=2100);}\\
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stoch\_simul(periods=2100);}
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\subsection{The deterministic model (case of temporary shock)}
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@ -502,7 +502,7 @@ alpha = 0.33;\\
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beta = 0.99;\\
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delta = 0.023;\\
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psi = 1.75;\\
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sigma = (0.007\/(1-alpha));\\
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sigma = (0.007/(1-alpha));\\
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epsilon = 10;\\
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\\
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model;\\
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@ -530,7 +530,7 @@ steady;\\
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check;\\
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\\
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shocks;\\
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var z;
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var z;\\
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periods 1:9;\\
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values 0.1;\\
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end;\\
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