diff --git a/doc/manual.xml b/doc/manual.xml
index 56aab51e85c057837cc05f4e55dc4eab7ed58bc7..74e44d1d63209b42f85afbe5e66a27ec623d94b7 100644
--- a/doc/manual.xml
+++ b/doc/manual.xml
@@ -3,11 +3,11 @@
<book>
<bookinfo>
<title>Dynare Manual</title>
- <subtitle>Version 4.0.3 (draft)</subtitle>
+ <subtitle>Version 4.0.3.1 (draft)</subtitle>
<author>
<firstname>Stéphane</firstname><surname>Adjemian</surname>
<affiliation><orgname>Université du Mans et CEPREMAP</orgname></affiliation>
- <email>stephane.adjemian@gmail.com</email>
+ <email>stephane.adjemian@ens.fr</email>
<address><street>142 rue du Chevaleret</street><postcode>75013</postcode><city>Paris</city><country>France</country></address>
</author>
<author>
@@ -415,7 +415,7 @@ In the description of Dynare commands, the following conventions are observed:
<arg rep="repeat"><arg>,</arg>
<replaceable>VARIABLE_NAME</replaceable>
</arg>
- <arg choice="plain">;</arg>
+ ;
</cmdsynopsis>
</refsynopsisdiv>
@@ -454,7 +454,7 @@ var c gnp q1 q2;
<arg rep="repeat"><arg>,</arg>
<replaceable>VARIABLE_NAME</replaceable>
</arg>
- <arg choice="plain">;</arg>
+ ;
</cmdsynopsis>
</refsynopsisdiv>
@@ -496,7 +496,7 @@ varexo m gov;
<arg rep="repeat"><arg>,</arg>
<replaceable>VARIABLE_NAME</replaceable>
</arg>
- <arg choice="plain">;</arg>
+ ;
</cmdsynopsis>
</refsynopsisdiv>
@@ -538,7 +538,7 @@ varexo_det tau;
<arg rep="repeat"><arg>,</arg>
<replaceable>PARAMETER_NAME</replaceable>
</arg>
- <arg choice="plain">;</arg>
+ ;
</cmdsynopsis>
</refsynopsisdiv>
@@ -667,13 +667,13 @@ A = 1-alpha*beta;
<arg>(<replaceable>OPTION</replaceable><arg rep="repeat">, <replaceable>OPTION</replaceable></arg>)</arg>
;
<sbr/>
- <arg rep="repeat">
+ <arg choice="plain" rep="repeat">
<group>
<arg choice="plain"><replaceable>MODEL_EXPRESSION</replaceable> = <replaceable>MODEL_EXPRESSION</replaceable> ;</arg>
<arg choice="plain"><replaceable>MODEL_EXPRESSION</replaceable> ;</arg>
<arg choice="plain"># <replaceable>VARIABLE_NAME</replaceable> = <replaceable>MODEL_EXPRESSION</replaceable> ;</arg>
</group>
- </arg>
+ </arg><sbr/>
<command>end</command>;
</cmdsynopsis>
</refsynopsisdiv>
@@ -800,7 +800,7 @@ For models with lags on more than one period, the command <xref linkend='histval
<command>initval</command>;<sbr/>
<arg rep="repeat" choice="plain">
<replaceable>VARIABLE_NAME</replaceable> = <replaceable>EXPRESSION</replaceable> ;
- </arg>
+ </arg><sbr/>
<command>end</command>;
</cmdsynopsis>
</refsynopsisdiv>
@@ -885,7 +885,7 @@ steady;
<command>endval</command>;<sbr/>
<arg rep="repeat" choice="plain">
<replaceable>VARIABLE_NAME</replaceable> = <replaceable>EXPRESSION</replaceable> ;
- </arg>
+ </arg><sbr/>
<command>end</command>;
</cmdsynopsis>
</refsynopsisdiv>
@@ -947,7 +947,7 @@ The initial equilibrium is computed by <xref linkend='steady'/> for <literal>x=1
<command>histval</command>;<sbr/>
<arg rep="repeat" choice="plain">
<replaceable>VARIABLE_NAME</replaceable> = <replaceable>EXPRESSION</replaceable> ;
- </arg>
+ </arg><sbr/>
<command>end</command>;
</cmdsynopsis>
</refsynopsisdiv>
@@ -1038,7 +1038,7 @@ If the variance of an exogenous variable is set to zero, this variable will appe
<synopfragmentref linkend="sto_shock">STOCHASTIC SHOCK STATEMENT</synopfragmentref>
</arg>
</group>
- </arg>
+ </arg><sbr/>
<command>end</command>;
<synopfragment id="det_shock">
@@ -1164,7 +1164,7 @@ forecast;
<synopfragmentref linkend="sto_mshock">STOCHASTIC SHOCK STATEMENT</synopfragmentref>
</arg>
</group>
- </arg>
+ </arg><sbr/>
<command>end</command>;
<synopfragment id="det_mshock">
@@ -1337,7 +1337,7 @@ Dynare has special commands for the computation of the static equilibrium of the
<listitem><para><literal>3</literal>: Chris Sims' solver</para></listitem>
<listitem><para><literal>4</literal>: similar to value <literal>2</literal>, except that it deals differently with nearly singular Jacobian</para></listitem>
</itemizedlist>
- Default value is 2.
+ Default value is <literal>2</literal>.
</para></listitem>
</varlistentry>
<varlistentry>
@@ -1394,7 +1394,7 @@ See <xref linkend='initval'/> and <xref linkend='endval'/>.
<refsynopsisdiv>
<cmdsynopsis>
<command>homotopy_setup</command>;<sbr/>
- <arg rep="repeat"><replaceable>VARIABLE_NAME</replaceable>, <replaceable>EXPRESSION</replaceable><arg>, <replaceable>EXPRESSION</replaceable></arg>;</arg>
+ <arg choice="plain" rep="repeat"><replaceable>VARIABLE_NAME</replaceable>, <replaceable>EXPRESSION</replaceable><arg>, <replaceable>EXPRESSION</replaceable></arg>;</arg><sbr/>
<command>end</command>;
</cmdsynopsis>
</refsynopsisdiv>
@@ -1465,11 +1465,11 @@ A necessary condition for the uniqueness of a stable equilibrium in the neighbor
<variablelist spacing='compact'>
<varlistentry>
<term><option>periods</option> = <replaceable>INTEGER</replaceable></term>
- <listitem><para>Number of periods of the forecast. Default: 40</para></listitem>
+ <listitem><para>Number of periods of the forecast. Default: <literal>40</literal></para></listitem>
</varlistentry>
<varlistentry>
<term><option>conf_sig</option> = <replaceable>DOUBLE</replaceable></term>
- <listitem><para>Level of significance for confidence interval. Default: 0.90</para></listitem>
+ <listitem><para>Level of significance for confidence interval. Default: <literal>0.90</literal></para></listitem>
</varlistentry>
</variablelist>
</refsect1>
@@ -1570,12 +1570,12 @@ The simulated endogenous variables are available in global matrix <varname>oo_.e
<refsect1><title>Options</title>
<variablelist spacing='compact'>
<varlistentry>
- <term><anchor id="ar" xreflabel="ar"/> <option>ar</option> = <replaceable>INTEGER</replaceable></term>
- <listitem><para>Order of autocorrelation coefficients to compute and to print. Default: 5</para></listitem>
+ <term><anchor id="ar" xreflabel="ar"/><option>ar</option> = <replaceable>INTEGER</replaceable></term>
+ <listitem><para>Order of autocorrelation coefficients to compute and to print. Default: <literal>5</literal></para></listitem>
</varlistentry>
<varlistentry>
<term><option>drop</option> = <replaceable>INTEGER</replaceable></term>
- <listitem><para>Number of points dropped at the beginning of simulation before computing the summary statistics. Default: 100</para></listitem>
+ <listitem><para>Number of points dropped at the beginning of simulation before computing the summary statistics. Default: <literal>100</literal></para></listitem>
</varlistentry>
<varlistentry>
<term><option>hp_filter</option> = <replaceable>INTEGER</replaceable></term>
@@ -1583,11 +1583,11 @@ The simulated endogenous variables are available in global matrix <varname>oo_.e
</varlistentry>
<varlistentry>
<term><option>hp_ngrid</option> = <replaceable>INTEGER</replaceable></term>
- <listitem><para>Number of points in the grid for the discrete Inverse Fast Fourier Transform used in the HP filter computation. It may be necessary to increase it for highly autocorrelated processes. Default: 512</para></listitem>
+ <listitem><para>Number of points in the grid for the discrete Inverse Fast Fourier Transform used in the HP filter computation. It may be necessary to increase it for highly autocorrelated processes. Default: <literal>512</literal></para></listitem>
</varlistentry>
<varlistentry>
<term><option>irf</option> = <replaceable>INTEGER</replaceable></term>
- <listitem><para>Number of periods on which to compute the IRFs. Setting <option>irf</option>=0, suppresses the plotting of IRF's. Default: 40</para></listitem>
+ <listitem><para>Number of periods on which to compute the IRFs. Setting <option>irf</option>=0, suppresses the plotting of IRF's. Default: <literal>40</literal></para></listitem>
</varlistentry>
<varlistentry>
<term><option>relative_irf</option></term>
@@ -1611,7 +1611,7 @@ The simulated endogenous variables are available in global matrix <varname>oo_.e
</varlistentry>
<varlistentry>
<term><option>nograph</option></term>
- <listitem><para>...</para></listitem>
+ <listitem><para>Doesn't do the graphs. Useful for loops</para></listitem>
</varlistentry>
<varlistentry>
<term><option>noprint</option></term>
@@ -1619,23 +1619,23 @@ The simulated endogenous variables are available in global matrix <varname>oo_.e
</varlistentry>
<varlistentry>
<term><option>print</option></term>
- <listitem><para>...</para></listitem>
+ <listitem><para>Print results (opposite of the above)</para></listitem>
</varlistentry>
<varlistentry>
<term><option>order = <replaceable>INTEGER</replaceable></option></term>
- <listitem><para>Order of Taylor approximation. Acceptable values are 1 and 2. Default: 2</para></listitem>
+ <listitem><para>Order of Taylor approximation. Acceptable values are <literal>1</literal> and <literal>2</literal>. Default: <literal>2</literal></para></listitem>
</varlistentry>
<varlistentry>
<term><option>periods</option> = <replaceable>INTEGER</replaceable></term>
- <listitem><para>Specifies the number of periods to use in simulations. If <option>order</option>=1, no simulation is necessary to compute theoretical moments and IRFs. A number of periods larger than one triggers automatically option <option>simul</option>. Default: 0</para></listitem>
+ <listitem><para>Specifies the number of periods to use in simulations. If <option>order</option>=<literal>1</literal>, no simulation is necessary to compute theoretical moments and IRFs. A number of periods larger than one triggers automatically option <option>simul</option>. Default: <literal>0</literal></para></listitem>
</varlistentry>
<varlistentry>
<term><option>qz_criterium</option> = <replaceable>DOUBLE</replaceable></term>
- <listitem><para>Value used to split stable from unstable eigenvalues in reordering the Generalized Schur decomposition used for solving 1<superscript>st</superscript> order problems. Default: 1.000001</para></listitem>
+ <listitem><para>Value used to split stable from unstable eigenvalues in reordering the Generalized Schur decomposition used for solving 1<superscript>st</superscript> order problems. Default: <literal>1.000001</literal></para></listitem>
</varlistentry>
<varlistentry>
<term><option>replic</option> = <replaceable>INTEGER</replaceable></term>
- <listitem><para>Number of simulated series used to compute the IRFs. Default: 1 if <option>order</option>=1, and 50 otherwise</para></listitem>
+ <listitem><para>Number of simulated series used to compute the IRFs. Default: <literal>1</literal> if <option>order</option>=<literal>1</literal>, and <literal>50</literal> otherwise</para></listitem>
</varlistentry>
<varlistentry>
<term><option>simul</option></term>
@@ -1647,7 +1647,7 @@ The simulated endogenous variables are available in global matrix <varname>oo_.e
</varlistentry>
<varlistentry>
<term><option>simul_algo</option> = <replaceable>INTEGER</replaceable></term>
- <listitem><para>...</para></listitem>
+ <listitem><para>Obsolete. Use only the default = 0</para></listitem>
</varlistentry>
<varlistentry>
<term><option>solve_algo</option> = <replaceable>INTEGER</replaceable></term>
@@ -1711,32 +1711,32 @@ where ys is the steady state value of y, yh<subscript>t</subscript>=y<subscript>
<listitem><para>the coefficients of the decision rules are stored in global structure <varname>oo_.dr</varname>. Here is the correspondance with the symbols used in the above description of the decision rules:
<itemizedlist><title>Decision rule coefficients</title>
-<listitem><para><varname>ys</varname>: <varname>oo_.dr.ys</varname>. The vector rows correspond to variables in alphabetical order of the variable names.</para></listitem>
+<listitem><para><varname>ys</varname>: <varname>oo_.dr.ys</varname>. The vector rows correspond to variables in the declaration order of the variable names.</para></listitem>
<listitem><para>Δ<superscript>2</superscript>: <varname>oo_.dr.ghs2</varname>. The vector rows correspond to re-ordered variables (see below).</para></listitem>
<listitem><para><varname>A</varname>: <varname>oo_.dr.ghx</varname>. The matrix rows correspond to re-ordered variables. The matrix columns correspond to state variables (see below).</para></listitem>
-<listitem><para><varname>B</varname>: <varname>oo_.dr.ghu</varname>. The matrix rows correspond to re-ordered variables (see below). The matrix columns correspond to exogenous variables in alphabetical order.</para></listitem>
+<listitem><para><varname>B</varname>: <varname>oo_.dr.ghu</varname>. The matrix rows correspond to re-ordered variables (see below). The matrix columns correspond to exogenous variables in declaration order.</para></listitem>
<listitem><para><varname>C</varname>: <varname>oo_.dr.ghxx</varname>. The matrix rows correspond to re-ordered variables. The matrix columns correspond to the Kronecker product of the vector of state variables (see below).</para></listitem>
-<listitem><para><varname>D</varname>: <varname>oo_.dr.ghuu</varname>. The matrix rows correspond to re-ordered variables (see below). The matrix columns correspond to the Kronecker product of exogenous variables in alphabetical order.</para></listitem>
-<listitem><para><varname>E</varname>: <varname>oo_.dr.ghxu</varname>. The matrix rows correspond to re-ordered variables. The matrix columns correspond to the Kronecker product of the vector of state variables (see below) by the vector of exogenous variables in alphabetical order.</para></listitem>
+<listitem><para><varname>D</varname>: <varname>oo_.dr.ghuu</varname>. The matrix rows correspond to re-ordered variables (see below). The matrix columns correspond to the Kronecker product of exogenous variables in declaration order.</para></listitem>
+<listitem><para><varname>E</varname>: <varname>oo_.dr.ghxu</varname>. The matrix rows correspond to re-ordered variables. The matrix columns correspond to the Kronecker product of the vector of state variables (see below) by the vector of exogenous variables in declaration order.</para></listitem>
</itemizedlist>
-When reordered, the variables are stored in the following order: static variables, purely predetermined variables (variables that appear only at the current and lagged periods in the model), variables that are both predetermined and forward-looking (variables that appear at the current, future and lagged periods in the model), purely forward-looking variables (variables that appear only at the current and future periods in the model). In each category, the variables are arranged alphabetically.</para>
+When reordered, the variables are stored in the following order: static variables, purely predetermined variables (variables that appear only at the current and lagged periods in the model), variables that are both predetermined and forward-looking (variables that appear at the current, future and lagged periods in the model), purely forward-looking variables (variables that appear only at the current and future periods in the model). In each category, the variables are arranged in declaration order.</para>
<para>
The state variables of the model are purely predetermined variables and variables that are both predetermined and forward-looking. They are ordered in that order. When there are lags on more than one period, the state variables are ordered first according to their lag: first variables from the previous period, then variables from two periods before and so on. Note also that when a variable appears in the model at a lag larger than one period, it is automatically included at all inferior lags.
</para>
</listitem>
-<listitem><para>The mean of the endogenous variables is available in the vector <varname>oo_.mean</varname>. The variables are arranged in alphabetical order.
+<listitem><para>The mean of the endogenous variables is available in the vector <varname>oo_.mean</varname>. The variables are arranged in declaration order.
</para></listitem>
-<listitem><para>The matrix of variance-covariance of the endogenous variables in the matrix <varname>oo_.var</varname>. The variables are arranged in alphabetical order.</para></listitem>
+<listitem><para>The matrix of variance-covariance of the endogenous variables in the matrix <varname>oo_.var</varname>. The variables are arranged in declaration order.</para></listitem>
<listitem><para>The matrix of autocorrelation of the endogenous variables are made available in cell array <varname>oo_.autocorr</varname>. The element number of the matrix in the cell array corresponds to the order of autocorrelation. The option <option>ar</option> specifies the number of autocorrelation matrices available.
</para></listitem>
<listitem>
<para>
Simulated variables, when they have been computed, are available in <trademark class="registered">Matlab</trademark>
-vectors with the same name as the endogenous variables.</para>
+vectors with the same name as the endogenous variables. They are also available in the <varname>oo_.endo_simul</varname> matrix. The series are arranged by row, in declaration order of the variable names</para>
</listitem>
<listitem>
<para>
- Impulse responses, when they have been computed, are available in <trademark class="registered">Matlab</trademark> vectors with the following naming convention <varname><replaceable>VARIABLE_NAME</replaceable>_<replaceable>SHOCK_NAME</replaceable></varname>.
+ Impulse responses, when they have been computed, are available in <trademark class="registered">Matlab</trademark> vectors with the following naming convention <varname><replaceable>VARIABLE_NAME</replaceable>_<replaceable>SHOCK_NAME</replaceable></varname>. They are also available in <varname>oo_.irfs</varname>.
</para>
<informalexample>
<para>Example:
@@ -1794,11 +1794,10 @@ Note that in order to avoid stochastic singularity, you must have at least as ma
<listitem><para><xref linkend='estimated_params'/></para></listitem>
<listitem><para><xref linkend='estimated_params_init'/></para></listitem>
<listitem><para><xref linkend='estimated_params_bounds'/></para></listitem>
-<listitem><para><xref linkend='estimated_params_init'/></para></listitem>
<listitem><para><xref linkend='estimation'/></para></listitem>
<listitem><para><xref linkend='prior_analysis'/></para></listitem>
<listitem><para><xref linkend='posterior_analysis'/></para></listitem>
-<listitem><para><xref linkend='unit_root_vars'/></para></listitem>
+<listitem><para><xref linkend='unit_root_vars'/> (deprecated)</para></listitem>
</itemizedlist>
<refentry id="varobs">
@@ -1815,12 +1814,9 @@ Note that in order to avoid stochastic singularity, you must have at least as ma
<cmdsynopsis>
<command>varobs</command>
<arg choice="plain" rep="repeat">
- <replaceable>VARIABLE_NAME</replaceable>
- </arg>
- <arg rep="repeat">
- <replaceable>VARIABLE_NAME</replaceable>
+ <replaceable>VARIABLE_NAME</replaceable>
</arg>
- <arg choice="plain">;</arg>
+ ;
</cmdsynopsis>
</refsynopsisdiv>
@@ -1832,9 +1828,9 @@ Note that in order to avoid stochastic singularity, you must have at least as ma
<refsect1><title>Example</title>
<informalexample>
- <programlisting>
- varobs C y rr;
- </programlisting>
+<programlisting>
+varobs C y rr;
+</programlisting>
</informalexample>
</refsect1>
@@ -1852,16 +1848,11 @@ Note that in order to avoid stochastic singularity, you must have at least as ma
<refsynopsisdiv>
<cmdsynopsis>
- <command>observation_trends;</command><sbr/>
- <arg choice="plain">
- <replaceable>VARIABLE_NAME</replaceable>
- </arg>
- <arg choice="plain">
- (<replaceable>EXPRESSION</replaceable>);
+ <command>observation_trends</command>;<sbr/>
+ <arg choice="plain" rep="repeat">
+ <replaceable>VARIABLE_NAME</replaceable>(<replaceable>EXPRESSION</replaceable>);
</arg><sbr/>
- <arg choice="plain">
- end;
- </arg>
+ <command>end</command>;
</cmdsynopsis>
</refsynopsisdiv>
@@ -1873,12 +1864,12 @@ Note that in order to avoid stochastic singularity, you must have at least as ma
<refsect1><title>Example</title>
<informalexample>
- <programlisting>
- observation_trends;
- Y (eta);
- P (mu/eta);
- end;
- </programlisting>
+<programlisting>
+observation_trends;
+Y (eta);
+P (mu/eta);
+end;
+</programlisting>
</informalexample>
</refsect1>
@@ -1895,100 +1886,163 @@ Note that in order to avoid stochastic singularity, you must have at least as ma
</refnamediv>
<refsynopsisdiv>
- <para>Syntax I (maximum likelihood estimation)</para>
- <cmdsynopsis>
- <command>estimated_params;</command><sbr/>
- <group choice="req">
- <arg choice="plain">
- stderr <replaceable>VARIABLE_NAME</replaceable>
- </arg>
- <arg choice="plain">
- corr <replaceable>VARIABLE_NAME_1, VARIABLE_NAME_2</replaceable>
- </arg>
- <arg choice="plain">
- <replaceable>PARAMETER_NAME</replaceable>
- </arg>
- </group>
- <arg choice="plain">
- <replaceable>, INITIAL_VALUE</replaceable>
- </arg>
- <arg choice="opt">
- <replaceable>, LOWER_BOUND</replaceable>
- </arg>
- <arg choice="opt">
- <replaceable>, UPPER_BOUND</replaceable>
- </arg>
- <arg choice="plain">;</arg><sbr/>
- <arg choice='plain'>...</arg><sbr/>
- <arg choice="plain">end;</arg>
- </cmdsynopsis>
- <para>Syntax II (Bayesian estimation)</para>
- <cmdsynopsis>
- <command>estimated_params;</command><sbr/>
- <group choice="req">
- <arg choice="plain">
- stderr <replaceable>VARIABLE_NAME</replaceable>
- </arg>
- <arg choice="plain">
- corr <replaceable>VARIABLE_NAME_1, VARIABLE_NAME_2</replaceable>
- </arg>
- <arg choice="plain">
- <replaceable>PARAMETER_NAME</replaceable>
- </arg>
- </group>
- <arg choice="plain">
- <replaceable>, PRIOR_SHAPE</replaceable>
- </arg>
- <arg choice="plain">
- <replaceable>, PRIOR_MEAN</replaceable>
- </arg>
- <arg choice="plain">
- <replaceable>, PRIOR_STANDARD_ERROR</replaceable>
- </arg>
- <arg choice="opt">
- <replaceable>, PRIOR_3RD_PARAMETER</replaceable>
- </arg>
- <arg choice="opt">
- <replaceable>, PRIOR_4TH_PARAMETER</replaceable>
- </arg>
- <arg choice="opt">
- <replaceable>, SCALE_PARAMETER</replaceable>
- </arg>
- <arg choice="plain">;</arg>
- <sbr/>
- <arg choice="plain">...</arg><sbr/>
- <arg choice="plain">end;</arg>
- </cmdsynopsis>
+ <refsect2><title>Syntax I (Maximum likelihood estimation)</title>
+ <cmdsynopsis>
+ <command>estimated_params</command>;<sbr/>
+ <arg choice="plain" rep="repeat">
+ <group choice="req">
+ <arg choice="plain">
+ <option>stderr</option> <replaceable>VARIABLE_NAME</replaceable>
+ </arg>
+ <arg choice="plain">
+ <option>corr</option> <replaceable>VARIABLE_NAME_1</replaceable>, <replaceable>VARIABLE_NAME_2</replaceable>
+ </arg>
+ <arg choice="plain">
+ <replaceable>PARAMETER_NAME</replaceable>
+ </arg>
+ </group>
+ ,
+ <arg choice="plain">
+ <replaceable>INITIAL_VALUE</replaceable>
+ </arg>
+ <arg>
+ , <replaceable>LOWER_BOUND</replaceable>
+ , <replaceable>UPPER_BOUND</replaceable>
+ </arg>
+ ;
+ </arg><sbr/>
+ <command>end</command>;
+ </cmdsynopsis>
+ </refsect2>
+ <refsect2>
+ <title>Syntax II (Bayesian estimation)</title>
+ <cmdsynopsis>
+ <command>estimated_params</command>;<sbr/>
+ <arg choice="plain" rep="repeat">
+ <group choice="req">
+ <arg choice="plain">
+ <option>stderr</option> <replaceable>VARIABLE_NAME</replaceable>
+ </arg>
+ <arg choice="plain">
+ <option>corr</option> <replaceable>VARIABLE_NAME_1</replaceable>, <replaceable>VARIABLE_NAME_2</replaceable>
+ </arg>
+ <arg choice="plain">
+ <replaceable>PARAMETER_NAME</replaceable>
+ </arg>
+ </group>
+ <arg>
+ , <replaceable>INITIAL_VALUE</replaceable>
+ <arg>
+ , <replaceable>LOWER_BOUND</replaceable>
+ , <replaceable>UPPER_BOUND</replaceable>
+ </arg>
+ </arg>
+
+ <arg choice="plain">
+ , <synopfragmentref linkend="prior_shape"><replaceable>PRIOR_SHAPE</replaceable></synopfragmentref>
+ </arg>
+ <arg choice="plain">
+ , <replaceable>PRIOR_MEAN</replaceable>
+ </arg>
+ <arg choice="plain">
+ , <replaceable>PRIOR_STANDARD_ERROR</replaceable>
+ </arg>
+ <arg>
+ , <replaceable>PRIOR_3RD_PARAMETER</replaceable>
+ <arg>
+ , <replaceable>PRIOR_4TH_PARAMETER</replaceable>
+ <arg>
+ , <replaceable>SCALE_PARAMETER</replaceable>
+ </arg>
+ </arg>
+ </arg>
+ ;
+ </arg><sbr/>
+ <command>end</command>;
+
+ <synopfragment id="prior_shape">
+ <group choice="plain">
+ <arg choice="plain"><option>beta_pdf</option></arg>
+ <arg choice="plain"><option>gamma_pdf</option></arg>
+ <arg choice="plain"><option>normal_pdf</option></arg>
+ <arg choice="plain"><option>uniform_pdf</option></arg>
+ <arg choice="plain"><option>inv_gamma_pdf</option></arg>
+ <arg choice="plain"><option>inv_gamma1_pdf</option></arg>
+ <arg choice="plain"><option>inv_gamma2_pdf</option></arg>
+ </group>
+ </synopfragment>
+ </cmdsynopsis>
+ </refsect2>
</refsynopsisdiv>
<refsect1><title>Description</title>
<para>
- The <command>estimated_params;....end;</command> block lists all parameters to be estimated and specifies bounds and priors as necessary.
+ The <command>estimated_params</command> block lists all parameters to be estimated and specifies bounds and priors as necessary.
</para>
</refsect1>
<refsect1><title>Estimated parameter specification</title>
<para>
Each line corresponds to an estimated parameter and follows this syntax:
-<itemizedlist spacing='compact'>
- <listitem><para><command>stderr</command> is a keyword indicating that the standard error of the exogenous variable, <replaceable>VARIABLE_NAME</replaceable>, or of the observation error associated with endogenous observed variable, <replaceable>VARIABLE_NAME</replaceable>, is to be estimated</para></listitem>
- <listitem><para><command>corr</command> is a keyword indicating that the correlation between the exogenous variables, <replaceable>VARIABLE_NAME_1</replaceable> and <replaceable>VARIABLE_NAME_2</replaceable>, or the correlation of the observation errors associated with endogenous observed variables, <replaceable>VARIABLE_NAME_1</replaceable> and <replaceable>VARIABLE_NAME_2</replaceable>, is to be estimated</para></listitem>
- <listitem><para> <replaceable>PARAMETER_NAME</replaceable> is the name of a model parameter to be estimated</para></listitem>
-<listitem><para> <replaceable>INITIAL_VALUE</replaceable> specifies a starting value for maximum likelihood estimation</para></listitem>
-<listitem><para> <replaceable>LOWER_BOUND</replaceable> specifies a lower bound for the parameter value in maximum likelihood estimation</para></listitem>
-<listitem><para> <replaceable>UPPER_BOUND</replaceable> specifies an upper bound for the parameter value in maximum likelihood estimation</para></listitem>
- <listitem><para> <replaceable>PRIOR_SHAPE</replaceable> is prior density among <command>beta_pdf</command>, <command>gamma_pdf</command>, <command>normal_pdf</command>, <command>inv_gamma_pdf</command>, <command>inv_gamma1_pdf</command>, <command>inv_gamma2_pdf</command>, <command>uniform_pdf</command></para></listitem>
- <listitem><para> <replaceable>PRIOR_MEAN</replaceable> is the mean of the prior distribution</para></listitem>
- <listitem><para> <replaceable>PRIOR_STANDARD_ERROR</replaceable> is the standard error of the prior distribution</para></listitem>
- <listitem><para> <replaceable>PRIOR_3RD_PARAMETER</replaceable> is a third parameter of the prior used for generalized beta distribution, generalized gamma and for the uniform distribution (default 0)</para></listitem>
- <listitem><para> <replaceable>PRIOR_4TH_PARAMETER</replaceable> is a fourth parameter of the prior used for generalized beta distribution, generalized gamma and for the uniform distribution (default 1)</para></listitem>
- <listitem><para> <replaceable>SCALE_PARAMETER</replaceable> is the scale parameter to be used for the jump distribution of the Metropolis-Hasting algorithm</para></listitem>
-</itemizedlist>
+<variablelist>
+ <varlistentry>
+ <term><option>stderr</option> <replaceable>VARIABLE_NAME</replaceable></term>
+ <listitem><para>Indicates that the standard error of the exogenous variable <replaceable>VARIABLE_NAME</replaceable>, or of the observation error associated with endogenous observed variable <replaceable>VARIABLE_NAME</replaceable>, is to be estimated</para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>corr</option> <replaceable>VARIABLE_NAME_1</replaceable>, <replaceable>VARIABLE_NAME_2</replaceable></term>
+ <listitem><para>Indicates that the correlation between the exogenous variables <replaceable>VARIABLE_NAME_1</replaceable> and <replaceable>VARIABLE_NAME_2</replaceable>, or the correlation of the observation errors associated with endogenous observed variables <replaceable>VARIABLE_NAME_1</replaceable> and <replaceable>VARIABLE_NAME_2</replaceable>, is to be estimated</para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><replaceable>PARAMETER_NAME</replaceable></term>
+ <listitem><para>The name of a model parameter to be estimated</para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><replaceable>INITIAL_VALUE</replaceable></term>
+ <listitem><para>Specifies a starting value for maximum likelihood estimation</para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><replaceable>LOWER_BOUND</replaceable></term>
+ <listitem><para>Specifies a lower bound for the parameter value in maximum likelihood estimation</para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><replaceable>UPPER_BOUND</replaceable></term>
+ <listitem><para>Specifies an upper bound for the parameter value in maximum likelihood estimation</para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><replaceable>PRIOR_SHAPE</replaceable></term>
+ <listitem><para>A keyword specifying the shape of the prior density. See the <link linkend="prior_shape">list of possible values</link>. Note that <option>inv_gamma_pdf</option> is equivalent to <option>inv_gamma1_pdf</option></para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><replaceable>PRIOR_MEAN</replaceable></term>
+ <listitem><para>The mean of the prior distribution</para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><replaceable>PRIOR_STANDARD_ERROR</replaceable></term>
+ <listitem><para>The standard error of the prior distribution</para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><replaceable>PRIOR_3RD_PARAMETER</replaceable></term>
+ <listitem><para>A third parameter of the prior used for generalized beta distribution, generalized gamma and for the uniform distribution. Default: <literal>0</literal></para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><replaceable>PRIOR_4TH_PARAMETER</replaceable></term>
+ <listitem><para>A fourth parameter of the prior used for generalized beta distribution, generalized gamma and for the uniform distribution. Default: <literal>1</literal></para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><replaceable>SCALE_PARAMETER</replaceable></term>
+ <listitem><para>The scale parameter to be used for the jump distribution of the Metropolis-Hasting algorithm</para></listitem>
+ </varlistentry>
+</variablelist>
+
+<note>
+<para><replaceable>INITIAL_VALUE</replaceable>, <replaceable>LOWER_BOUND</replaceable>, <replaceable>UPPER_BOUND</replaceable>, <replaceable>PRIOR_MEAN</replaceable>, <replaceable>PRIOR_STANDARD_ERROR</replaceable>, <replaceable>PRIOR_3RD_PARAMETER</replaceable>, <replaceable>PRIOR_4TH_PARAMETER</replaceable> and <replaceable>SCALE_PARAMETER</replaceable> must be positive or negative <replaceable>INTEGER</replaceable> or <replaceable>DOUBLE</replaceable>. Some of them can be empty, in which Dynare will select a default value depending on the context and the prior shape.</para>
+</note>
-<note><para> At minimum, one must specify the name of the parameter and an initial guess. That will trigger unconstrained maximum likelihood estimation.
+<note><para>At minimum, one must specify the name of the parameter and an initial guess. That will trigger unconstrained maximum likelihood estimation.
</para></note>
-<note><para> As one uses options more towards the end of the list, all previous options must be filled: if you want to specify <replaceable>jscale</replaceable>, you must specify <replaceable>prior_p3</replaceable> and <replaceable>prior_p4</replaceable>. Use default values, if these parameters don't apply.
+<note><para>As one uses options more towards the end of the list, all previous options must be filled: for example, if you want to specify <replaceable>SCALE_PARAMETER</replaceable>, you must specify <replaceable>PRIOR_3RD_PARAMETER</replaceable> and <replaceable>PRIOR_4TH_PARAMETER</replaceable>. Use empty values, if these parameters don't apply.
</para></note>
</para>
</refsect1>
@@ -1999,24 +2053,24 @@ Sometimes, it is desirable to estimate a transformation of a parameter appearing
</para>
<para>
-In such a case, it is possible to declare the parameter to be estimated in the <xref linkend="parameters"/> statement and to define the transformation at the top of the <xref linkend="model"/> section, as a <trademark class="registered">Matlab</trademark> expression. The first character of the line must be a pound sign (#).
+In such a case, it is possible to declare the parameter to be estimated in the <xref linkend="parameters"/> statement and to define the transformation, using a pound sign (#) expression (see <xref linkend="model"/>).
</para>
</refsect1>
<refsect1><title>Example</title>
-<informalexample>
- <programlisting>
- parameters bet;
-
- model;
- # sig = 1/bet;
- c = sig*c(+1)*mpk;
- end;
-
- estimated_params;
- bet,normal_pdf,1,0.05;
- end;
- </programlisting>
+ <informalexample>
+<programlisting>
+parameters bet;
+
+model;
+# sig = 1/bet;
+c = sig*c(+1)*mpk;
+end;
+
+estimated_params;
+bet, normal_pdf, 1, 0.05;
+end;
+</programlisting>
</informalexample>
</refsect1>
@@ -2036,43 +2090,36 @@ In such a case, it is possible to declare the parameter to be estimated in the <
<refsynopsisdiv>
<cmdsynopsis>
- <command>estimated_params_init;</command><sbr/>
- <group choice="req">
- <arg choice="plain">
- stderr <replaceable>VARIABLE_NAME</replaceable>
- </arg>
- <arg choice="plain">
- corr <replaceable>VARIABLE_NAME_1, VARIABLE_NAME_2</replaceable>
- </arg>
- <arg choice="plain">
- <replaceable>PARAMETER_NAME</replaceable>
- </arg>
- </group>
- <arg choice="plain">
- <replaceable>, INITIAL_VALUE</replaceable>
- </arg>
- <arg choice="plain">;</arg><sbr/>
- <arg choice='plain'>...</arg><sbr/>
- <arg choice="plain">end;</arg>
+ <command>estimated_params_init</command>;<sbr/>
+ <arg choice="plain" rep="repeat">
+ <group choice="req">
+ <arg choice="plain">
+ <option>stderr</option> <replaceable>VARIABLE_NAME</replaceable>
+ </arg>
+ <arg choice="plain">
+ <option>corr</option> <replaceable>VARIABLE_NAME_1</replaceable>, <replaceable>VARIABLE_NAME_2</replaceable>
+ </arg>
+ <arg choice="plain">
+ <replaceable>PARAMETER_NAME</replaceable>
+ </arg>
+ </group>
+ ,
+ <arg choice="plain">
+ <replaceable>INITIAL_VALUE</replaceable>
+ </arg>
+ ;
+ </arg><sbr/>
+ <command>end</command>;
</cmdsynopsis>
</refsynopsisdiv>
<refsect1><title>Description</title>
-<para>
- The <command>estimated_params_init;....end;</command> block declares numerical initial values for the optimizer when these ones are different from the prior mean
+<para>The <command>estimated_params_init</command> block declares numerical initial values for the optimizer when these ones are different from the prior mean.
</para>
</refsect1>
<refsect1><title>Estimated parameter initial value specification</title>
-<para>
-Each line corresponds to an estimated parameter and follows this syntax:
-<itemizedlist spacing='compact'>
- <listitem><para><command>stderr</command> is a keyword indicating that the standard error of the exogenous variable, <replaceable>VARIABLE_NAME</replaceable>, or of the observation error associated with endogenous observed variable, <replaceable>VARIABLE_NAME</replaceable>, is to be estimated</para></listitem>
- <listitem><para><command>corr</command> is a keyword indicating that the correlation between the exogenous variables, <replaceable>VARIABLE_NAME_1</replaceable> and <replaceable>VARIABLE_NAME_2</replaceable>, or the correlation of the observation errors associated with endogenous observed variables, <replaceable>VARIABLE_NAME_1</replaceable> and <replaceable>VARIABLE_NAME_2</replaceable>, is to be estimated</para></listitem>
- <listitem><para> <replaceable>PARAMETER_NAME</replaceable> is the name of a model parameter to be estimated</para></listitem>
-<listitem><para> <replaceable>INITIAL_VALUE</replaceable> specifies a starting value for maximum likelihood estimation</para></listitem>
-</itemizedlist>
-</para>
+<para>See <xref linkend="estimated_params" /> for the meaning and syntax of the various components.</para>
</refsect1>
</refentry>
@@ -2089,48 +2136,41 @@ Each line corresponds to an estimated parameter and follows this syntax:
<refsynopsisdiv>
<cmdsynopsis>
- <command>estimated_params_bounds;</command><sbr/>
- <group choice="req">
- <arg choice="plain">
- stderr <replaceable>VARIABLE_NAME</replaceable>
- </arg>
- <arg choice="plain">
- corr <replaceable>VARIABLE_NAME_1, VARIABLE_NAME_2</replaceable>
- </arg>
- <arg choice="plain">
- <replaceable>PARAMETER_NAME</replaceable>
- </arg>
- </group>
- <arg choice="plain">
- <replaceable>, LOWER_BOUND</replaceable>
- </arg>
- <arg choice="plain">
- <replaceable>, UPPER_BOUND</replaceable>
- </arg>
- <arg choice="plain">;</arg><sbr/>
- <arg choice='plain'>...</arg><sbr/>
- <arg choice="plain">end;</arg>
+ <command>estimated_params_bounds</command>;<sbr/>
+ <arg choice="plain" rep="repeat">
+ <group choice="req">
+ <arg choice="plain">
+ <option>stderr</option> <replaceable>VARIABLE_NAME</replaceable>
+ </arg>
+ <arg choice="plain">
+ <option>corr</option> <replaceable>VARIABLE_NAME_1</replaceable>, <replaceable>VARIABLE_NAME_2</replaceable>
+ </arg>
+ <arg choice="plain">
+ <replaceable>PARAMETER_NAME</replaceable>
+ </arg>
+ </group>
+ ,
+ <arg choice="plain">
+ <replaceable>LOWER_BOUND</replaceable>
+ </arg>
+ ,
+ <arg choice="plain">
+ <replaceable>UPPER_BOUND</replaceable>
+ </arg>
+ ;
+ </arg><sbr/>
+ <command>end</command>;
</cmdsynopsis>
</refsynopsisdiv>
<refsect1><title>Description</title>
-<para>
- The <command>estimated_params;....end;</command> block lists all parameter to be estimated and specifies bounds and priors when required.
-</para>
+<para>The <command>estimated_params_bounds</command> block declares lower and upper bounds for parameters in maximum likelihood estimation.</para>
</refsect1>
-<refsect1><title>Estimated parameter specification</title>
-<para>
-Each line corresponds to an estimated parameter and follows this syntax:
-<itemizedlist spacing='compact'>
- <listitem><para><command>stderr</command> is a keyword indicating that the standard error of the exogenous variable, <replaceable>VARIABLE_NAME</replaceable>, or of the observation error associated with endogenous observed variable, <replaceable>VARIABLE_NAME</replaceable>, is to be estimated</para></listitem>
- <listitem><para><command>corr</command> is a keyword indicating that the correlation between the exogenous variables, <replaceable>VARIABLE_NAME_1</replaceable> and <replaceable>VARIABLE_NAME_2</replaceable>, or the correlation of the observation errors associated with endogenous observed variables, <replaceable>VARIABLE_NAME_1</replaceable> and <replaceable>VARIABLE_NAME_2</replaceable>, is to be estimated</para></listitem>
- <listitem><para> <replaceable>PARAMETER_NAME</replaceable> is the name of a model parameter to be estimated</para></listitem>
-<listitem><para> <replaceable>LOWER_BOUND</replaceable> specifies a lower bound for the parameter value in maximum likelihood estimation</para></listitem>
-<listitem><para> <replaceable>UPPER_BOUND</replaceable> specifies an upper bound for the parameter value in maximum likelihood estimation</para></listitem>
-</itemizedlist>
-</para>
+<refsect1><title>Estimated parameter bounds specification</title>
+<para>See <xref linkend="estimated_params" /> for the meaning and syntax of the various components.</para>
</refsect1>
+
</refentry>
<refentry id="estimation">
@@ -2140,74 +2180,209 @@ Each line corresponds to an estimated parameter and follows this syntax:
<refnamediv>
<refname>estimation</refname>
- <refpurpose>computes estimation.</refpurpose>
+ <refpurpose>computes estimation</refpurpose>
</refnamediv>
<refsynopsisdiv>
<cmdsynopsis>
<command>estimation</command>
- <arg>(OPTIONS)</arg>
- <arg choice='plain'>;</arg>
+ <arg>(<replaceable>OPTION</replaceable><arg rep="repeat">, <replaceable>OPTION</replaceable></arg>)</arg>
+ <arg rep="repeat"><replaceable>VARIABLE_NAME</replaceable></arg>;
</cmdsynopsis>
</refsynopsisdiv>
-<refsect1><title>OPTIONS</title>
-<itemizedlist spacing='compact'>
-<listitem><para> <command>datafile</command> =
- <replaceable>FILENAME</replaceable>: the datafile (a .m file, a .mat file or a .xls file)</para></listitem>
-<listitem><para><command>xls_sheet</command> = <replaceable>NAME</replaceable>: the name of the sheet with the data in an Excel file</para></listitem>
-<listitem><para><command>xls_range</command> = <replaceable>RANGE</replaceable>: the range with the data in an Excel file</para></listitem>
-<listitem><para><command>nobs</command> = <replaceable>INTEGER</replaceable>: the number of observations to be used (default: all observations in the file)</para>
-<para><command>nobs</command> = ([<replaceable>INTEGER_1</replaceable>:<replaceable>INTEGER_2</replaceable>]): runs a recursive estimation and forecast for samples of size ranging of <varname>INTEGER_1</varname> to <varname>INTEGER_2</varname>. Option <varname>FORECAST</varname> must also be specified.</para>
-</listitem>
-<listitem><para> <command>first_obs</command> = <replaceable>INTEGER</replaceable>: the number of the first observation to be used (default = 1)</para></listitem>
-<listitem><para> <command>prefilter</command> = 1: the estimation procedure demeans the data (default=0, no prefiltering)</para></listitem>
-<listitem><para> <command>presample</command> = <replaceable>INTEGER</replaceable>: the number of observations to be skipped before evaluating the likelihood (default = 0)</para></listitem>
-<listitem><para> <command>loglinear</command>: computes a log--linear approximation of the model instead of a linear (default) approximation. The data must correspond to the definition of the variables used in the modelx.</para></listitem>
-<listitem><para> <command>nograph</command>: no graphs should be plotted</para></listitem>
-<listitem><para> <command>lik_init</command>: <replaceable>INTEGER</replaceable>: type of initialization of Kalman filter.
-<itemizedlist spacing='compact'>
- <listitem><para>1 (default): for stationary models, the initial matrix of variance of the error of forecast is set equal to the unconditional variance of the state variables.</para></listitem>
- <listitem><para>2: for nonstationary models: a wide prior is used with an initial matrix of variance of the error of forecast diagonal with 10 on the diagonal.</para></listitem>
-</itemizedlist>
-</para></listitem>
-<listitem><anchor id="conf_sig" xreflabel="conf_sig"/><para><command>conf_sig</command> = <replaceable>{INTEGER | DOUBLE}</replaceable>: the level for the confidence intervals reported in the results (default = 0.90)</para></listitem>
-<listitem><anchor id="mh_replic" xreflabel="mh_replic"/><para> <command>mh_replic</command> = <replaceable>INTEGER</replaceable>: number of replication for Metropolis Hasting algorithm. For the time being, mh_replic should be larger than 1200 (default = 20000.)</para></listitem>
-<listitem><para> <command>mh_nblocks</command> = <replaceable>INTEGER</replaceable>: number of paralletl chains for Metropolis Hasting algorithm (default = 2).</para></listitem>
-<listitem><para> <command>mh_drop</command> = <replaceable>DOUBLE</replaceable>: the fraction of initially generated parameter vectors to be dropped before using posterior simulations (default = 0.5)</para></listitem>
-<listitem><para> <command>mh_jscale</command> = <replaceable>DOUBLE</replaceable>: the scale to be used for the jumping distribution in MH algorithm. The default value is rarely satisfactory. This option must be tune to obtain, ideally, an accpetation rate of 25% in the Metropolis-Hastings algorithm (default = 0.2).</para></listitem>
-<listitem><para><command>mh_init_scale</command>=<replaceable>DOUBLE</replaceable>: the scale to be used for drawing the initial value of the Metropolis-Hastings chain (default=2*mh_scale).</para>
-</listitem>
-<listitem><anchor id="mh_recover" xreflabel="mh_recover"/><para><command>mh_recover</command> attempts to recover a Metropolis simulation that crashed prematurely. Shouldn't be used together with <link linkend="load_mh_file">load_mh_file</link></para></listitem>
-<listitem><para><command>mode_file</command>=<replaceable>FILENAME</replaceable>: name of the file containing previous value for the mode. When computing the mode, Dynare stores the mode (<varname>xparam1</varname>) and the hessian (<varname>hh</varname>) in a file called <filename><replaceable>MODEL NAME</replaceable>_mode</filename>.</para></listitem>
-<listitem><para><command>mode_compute</command>=<replaceable>INTEGER</replaceable>: specifies the optimizer for the mode computation.
-<itemizedlist spacing='compact'>
- <listitem><para>0: the mode isn't computed. mode_file must be specified</para></listitem>
- <listitem><para>1: uses <trademark class="registered">Matlab</trademark> <command>fmincon</command>.</para></listitem>
- <listitem><para>2: uses Lester Ingber's Adaptive Simulated Annealing.</para></listitem>
- <listitem><para>3: uses <trademark class="registered">Matlab</trademark> <command>fminunc</command>.</para></listitem>
- <listitem><para>4 (default): uses Chris Sim's <command>csminwel</command>.</para></listitem>
-</itemizedlist></para></listitem>
-<listitem><para><command>mode_check</command>: when <command>mode_check</command> is set, Dynare plots the posterior density for values around the computed mode for each estimated parameter in turn. This is helpful to diagnose problems with the optimizer.</para></listitem>
-<listitem><para><command>prior_trunc</command>=<replaceable>DOUBLE</replaceable>: probability of extreme values of the prior density that is ignored when computing bounds for the parameters (default=1e-32).</para></listitem>
-<listitem><anchor id="load_mh_file" xreflabel="load_mh_file"/><para><command>load_mh_file</command>: when <command>load_mh_file</command> is declared, Dynare adds to previous Metropolis-Hastings simulations instead of starting from scratch. Shouldn't be used together with <link linkend="mh_recover">mh_recover</link>.</para></listitem>
-<listitem><para><command>optim</command>=(<replaceable>fmincon options</replaceable>): can be used to set options for fmincon, the optimizing function of <trademark class="registered">Matlab</trademark> Optimizaiton toolbox. Use <trademark class="registered">Matlab</trademark> syntax for these options</para>
-<para> (default: ('display','iter','LargeScale','off','MaxFunEvals',100000,'TolFun',1e-8,'TolX',1e-6))</para></listitem>
-<listitem>
- <para>
- <command>nodiagnostic</command>: doesn't compute the convergence diagnostics for Metropolis (default: diagnostics are computed and displayed).
- </para>
-</listitem>
-<listitem><para><anchor id="bayesian_irf" xreflabel="bayesian_irf"/><command>bayesian_irf</command> triggers the computation of the posterior distribution of IRFs. The length of the IRFs are controlled by the <command>irf</command> option</para></listitem>
-<listitem><para><anchor id="moments_varendo" xreflabel="moments_varendo"/><command>moments_varendo</command> triggers the computation of the posterior distribution of the theoretical moments of the endogenous variables</para></listitem>
-<listitem><para><anchor id="filtered_vars" xreflabel="filtered_vars"/><command>filtered_vars</command> triggers the computation of the posterior distribution of filtered endogenous variables and shocks</para></listitem>
-<listitem><anchor id="smoother" xreflabel="smoother"/><para><command>smoother</command> triggers the computation of the posterior distribution of smoothered endogenous variables and shocks</para></listitem>
-<listitem><para><anchor id="forecast_opt" xreflabel="forecast"/><command>forecast = </command><replaceable>INTEGER</replaceable> computes the posterior distribution of a forecast on <replaceable>INTEGER</replaceable> periods after the end of the sample used in estimation</para></listitem>
-<listitem><para><command>tex</command> requests the printing of results and graphs in TeX tables and graphics that can be later directly included in Latex files (not yet implemented)</para></listitem>
-<listitem><para>All options for <xref linkend="stoch_simul"/></para></listitem>
-</itemizedlist>
+<refsect1><title>Options</title>
+<variablelist>
+ <varlistentry>
+ <term><option>datafile</option> = <replaceable>FILENAME</replaceable></term>
+ <listitem><para>The datafile (a <filename class="extension">.m</filename> file, a <filename class="extension">.mat</filename> file or a <filename class="extension">.xls</filename> file)</para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>xls_sheet</option> = <replaceable>NAME</replaceable></term>
+ <listitem><para>The name of the sheet with the data in an Excel file</para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>xls_range</option> = <replaceable>RANGE</replaceable></term>
+ <listitem><para>The range with the data in an Excel file</para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>nobs</option> = <replaceable>INTEGER</replaceable></term>
+ <listitem><para>The number of observations to be used. Default: all observations in the file</para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>nobs</option> = [<replaceable>INTEGER_1</replaceable>:<replaceable>INTEGER_2</replaceable>]</term>
+ <listitem><para>Runs a recursive estimation and forecast for samples of size ranging of <replaceable>INTEGER_1</replaceable> to <replaceable>INTEGER_2</replaceable>. Option <option>forecast</option> must also be specified</para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>first_obs</option> = <replaceable>INTEGER</replaceable></term>
+ <listitem><para>The number of the first observation to be used. Default: <literal>1</literal></para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>prefilter</option> = <replaceable>INTEGER</replaceable></term>
+ <listitem><para>A value of <literal>1</literal> means that the estimation procedure will demean the data. Default: <literal>0</literal>, <foreignphrase>i.e.</foreignphrase> no prefiltering</para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>presample</option> = <replaceable>INTEGER</replaceable></term>
+ <listitem><para>The number of observations to be skipped before evaluating the likelihood. Default: <literal>0</literal></para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>loglinear</option></term>
+ <listitem><para>Computes a log--linear approximation of the model instead of a linear approximation. The data must correspond to the definition of the variables used in the model. Default: computes a linear approximation</para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>nograph</option></term>
+ <listitem><para>No graphs should be plotted</para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>lik_init</option> = <replaceable>INTEGER</replaceable></term>
+ <listitem><para>Type of initialization of Kalman filter:
+ <itemizedlist>
+ <listitem><para><literal>1</literal>: for stationary models, the initial matrix of variance of the error of forecast is set equal to the unconditional variance of the state variables</para></listitem>
+ <listitem><para><literal>2</literal>: for nonstationary models: a wide prior is used with an initial matrix of variance of the error of forecast diagonal with 10 on the diagonal</para></listitem>
+ </itemizedlist>
+ Default value is <literal>1</literal>.
+ </para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>lik_algo</option> = <replaceable>INTEGER</replaceable></term>
+ <listitem><para>...</para></listitem>
+ </varlistentry>
+ <varlistentry id="conf_sig" xreflabel="conf_sig">
+ <term><option>conf_sig</option> = <replaceable>DOUBLE</replaceable></term>
+ <listitem><para>The level for the confidence intervals reported in the results. Default: <literal>0.90</literal></para></listitem>
+ </varlistentry>
+ <varlistentry id="mh_replic" xreflabel="mh_replic">
+ <term><option>mh_replic</option> = <replaceable>INTEGER</replaceable></term>
+ <listitem><para>Number of replications for Metropolis-Hastings algorithm. For the time being, <option>mh_replic</option> should be larger than <literal>1200</literal>. Default: <literal>20000</literal></para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>mh_nblocks</option> = <replaceable>INTEGER</replaceable></term>
+ <listitem><para>Number of parallel chains for Metropolis-Hastings algorithm. Default: <literal>2</literal></para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>mh_drop</option> = <replaceable>DOUBLE</replaceable></term>
+ <listitem><para>The fraction of initially generated parameter vectors to be dropped before using posterior simulations. Default: <literal>0.5</literal></para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>mh_jscale</option> = <replaceable>DOUBLE</replaceable></term>
+ <listitem><para>The scale to be used for the jumping distribution in Metropolis-Hastings algorithm. The default value is rarely satisfactory. This option must be tuned to obtain, ideally, an acceptation rate of 25% in the Metropolis-Hastings algorithm. Default: <literal>0.2</literal></para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>mh_init_scale</option> = <replaceable>DOUBLE</replaceable></term>
+ <listitem><para>The scale to be used for drawing the initial value of the Metropolis-Hastings chain. Default: 2*<option>mh_scale</option></para></listitem>
+ </varlistentry>
+ <varlistentry id="mh_recover" xreflabel="mh_recover">
+ <term><option>mh_recover</option></term>
+ <listitem><para>Attempts to recover a Metropolis-Hastings simulation that crashed prematurely. Shouldn't be used together with <link linkend="load_mh_file"><option>load_mh_file</option></link></para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>mh_mode</option> = <replaceable>INTEGER</replaceable></term>
+ <listitem><para>...</para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>mode_file</option> = <replaceable>FILENAME</replaceable></term>
+ <listitem><para>Name of the file containing previous value for the mode. When computing the mode, Dynare stores the mode (<varname>xparam1</varname>) and the hessian (<varname>hh</varname>) in a file called <filename><replaceable>MODEL_FILENAME</replaceable>_mode.mat</filename></para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>mode_compute</option> = <replaceable>INTEGER</replaceable></term>
+ <listitem><para>Specifies the optimizer for the mode computation:
+ <itemizedlist>
+ <listitem><para><literal>0</literal>: the mode isn't computed. mode_file must be specified</para></listitem>
+ <listitem><para><literal>1</literal>: uses <trademark class="registered">Matlab</trademark>'s <command>fmincon</command></para></listitem>
+ <listitem><para><literal>2</literal>: value no longer used</para></listitem>
+ <listitem><para><literal>3</literal>: uses <trademark class="registered">Matlab</trademark>'s <command>fminunc</command></para></listitem>
+ <listitem><para><literal>4</literal>: uses Chris Sim's <command>csminwel</command></para></listitem>
+ <listitem><para><literal>5</literal>: uses a routine by Marco Ratto</para></listitem>
+ <listitem><para><literal>6</literal>: uses a simulated annealing-like algorithm</para></listitem>
+ <listitem><para><literal>7</literal>: uses <trademark class="registered">Matlab</trademark>'s <command>fminsearch</command> (a simplex based routine)</para></listitem>
+ </itemizedlist>
+ Default value is <literal>4</literal>.
+ </para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>mode_check</option></term>
+ <listitem><para>Tells Dynare to plot the posterior density for values around the computed mode for each estimated parameter in turn. This is helpful to diagnose problems with the optimizer</para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>prior_trunc</option> = <replaceable>DOUBLE</replaceable></term>
+ <listitem><para>Probability of extreme values of the prior density that is ignored when computing bounds for the parameters. Default: <literal>1e-32</literal></para></listitem>
+ </varlistentry>
+ <varlistentry id="load_mh_file" xreflabel="load_mh_file">
+ <term><option>load_mh_file</option></term>
+ <listitem><para>Tells Dynare to add to previous Metropolis-Hastings simulations instead of starting from scratch. Shouldn't be used together with <link linkend="mh_recover">mh_recover</link></para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>optim</option> = (<replaceable>fmincon options</replaceable>)</term>
+ <listitem><para>Can be used to set options for <command>fmincon</command>, the optimizing function of <trademark class="registered">Matlab</trademark> Optimizaiton toolbox. Use <trademark class="registered">Matlab</trademark>'s syntax for these options. Default: <literal>('display','iter','LargeScale','off','MaxFunEvals',100000,'TolFun',1e-8,'TolX',1e-6)</literal></para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>nodiagnostic</option></term>
+ <listitem><para>Doesn't compute the convergence diagnostics for Metropolis-Hastings. Default: diagnostics are computed and displayed</para></listitem>
+ </varlistentry>
+ <varlistentry id="bayesian_irf" xreflabel="bayesian_irf">
+ <term><option>bayesian_irf</option></term>
+ <listitem><para>Triggers the computation of the posterior distribution of IRFs. The length of the IRFs are controlled by the <option>irf</option> option</para></listitem>
+ </varlistentry>
+ <varlistentry id="moments_varendo" xreflabel="moments_varendo">
+ <term><option>moments_varendo</option></term>
+ <listitem><para>Triggers the computation of the posterior distribution of the theoretical moments of the endogenous variables</para></listitem>
+ </varlistentry>
+ <varlistentry id="filtered_vars" xreflabel="filtered_vars">
+ <term><option>filtered_vars</option></term>
+ <listitem><para>Triggers the computation of the posterior distribution of filtered endogenous variables and shocks</para></listitem>
+ </varlistentry>
+ <varlistentry id="smoother" xreflabel="smoother">
+ <term><option>smoother</option></term>
+ <listitem><para>Triggers the computation of the posterior distribution of smoothered endogenous variables and shocks</para></listitem>
+ </varlistentry>
+ <varlistentry id="forecast_opt" xreflabel="forecast">
+ <term><option>forecast</option> = <replaceable>INTEGER</replaceable></term>
+ <listitem><para>Computes the posterior distribution of a forecast on <replaceable>INTEGER</replaceable> periods after the end of the sample used in estimation</para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>tex</option></term>
+ <listitem><para>Requests the printing of results and graphs in TeX tables and graphics that can be later directly included in LaTeX files (not yet implemented)</para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>kalman_algo</option> = <replaceable>INTEGER</replaceable></term>
+ <listitem><para>...</para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>kalman_tol</option> = <replaceable>INTEGER</replaceable></term>
+ <listitem><para>...</para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>filter_step_ahead</option> = [<replaceable>INTEGER_1</replaceable>:<replaceable>INTEGER_2</replaceable>]</term>
+ <listitem><para>...</para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>constant</option></term>
+ <listitem><para>...</para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>noconstant</option></term>
+ <listitem><para>...</para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>diffuse_filter</option></term>
+ <listitem><para>...</para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>solve_algo</option> = <replaceable>INTEGER</replaceable></term>
+ <listitem><para>See <xref linkend="steady"/></para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>order</option> = <replaceable>INTEGER</replaceable></term>
+ <listitem><para>See <xref linkend="stoch_simul"/></para></listitem>
+ </varlistentry>
+ <varlistentry>
+ <term><option>irf</option> = <replaceable>INTEGER</replaceable></term>
+ <listitem><para>See <xref linkend="stoch_simul"/></para></listitem>
+ </varlistentry>
+</variablelist>
+
-<note><para> If no <command>mh_jscale</command> parameter is used in estimated_params, the procedure uses <command>mh_jscale</command> for all parameters. If <command>mh_jscale</command> option isn't set, the procedure uses 0.2 for all parameters.
+<note><para> If no <option>mh_jscale</option> parameter is used in estimated_params, the procedure uses <option>mh_jscale</option> for all parameters. If <option>mh_jscale</option> option isn't set, the procedure uses <literal>0.2</literal> for all parameters.
</para></note>
</refsect1>
@@ -2225,7 +2400,7 @@ Each line corresponds to an estimated parameter and follows this syntax:
<refsect1><title>Output</title>
<para>After running <command>estimation</command>, the parameters and the variance matrix of the shocks are set to the mode for maximum likelihood estimation or posterior mode computation without Metropolis iterations.
</para>
-<para>After <command>estimation</command> with Metropolis iterations (option <command>mh_replic</command> > 0 or option <command>load_mh_file</command> set) the parameters and the variance matrix of the shocks are set to the posterior mean.</para>
+<para>After <command>estimation</command> with Metropolis iterations (option <option>mh_replic</option> > 0 or option <option>load_mh_file</option> set) the parameters and the variance matrix of the shocks are set to the posterior mean.</para>
<para>Depending on the options, <command>estimation</command> stores results in the following fields of structure <varname>oo_</varname>:
<table orient="land"><title>Content of <varname>oo_</varname></title><tgroup cols='6'>
@@ -2388,7 +2563,7 @@ oo_.posterior_hpdsup.measurement_errors_corr.gdp_conso
<refsect1><title>Description</title>
<para>
-<command>unit_root_vars</command> is used to declare unit-root variables of a model so that a diffuse prior can be used in the initialization of the Kalman filter for these variables only. For stationary variables, the unconditional covariance matrix of these variables is used for initialization. The algorithm to compute a true diffuse prior is taken from <xref linkend="durbin-koopman:2001"/> and <xref linkend="koopman-durbin:2003"/>.
+<command>unit_root_vars</command> is now deprecated and will result in no action, It was used to declare unit-root variables of a model so that a diffuse prior can be used in the initialization of the Kalman filter for these variables only. For stationary variables, the unconditional covariance matrix of these variables is used for initialization. The algorithm to compute a true diffuse prior is taken from <xref linkend="durbin-koopman:2001"/> and <xref linkend="koopman-durbin:2003"/>.
</para>
<para>When <command>unit_root_vars</command> is used the <command>lik_init</command> option of <xref linkend="estimation"/> has no effect.
@@ -2562,7 +2737,7 @@ This problem is solved using a numerical optimizer.
<refnamediv>
<refname>ramsey_policy</refname>
- <refpurpose>computes the first order approximation of the policy that maximizes the policy maker objective function (see <xref linkend="planner_objective"/>) submitted to the constraints provided by the equilibrium path of the economy</refpurpose>
+ <refpurpose>computes the first order approximation of the policy that maximizes the policy maker objective function (see <xref linkend="planner_objective"/>) submitted to the constraints provided by the equilibrium path of the economy. See <ulink url="http://www.dynare.org/DynareWiki/OptimalPolicy">http://www.dynare.org/DynareWiki/OptimalPolicy</ulink> for more information.</refpurpose>
</refnamediv>
</refentry>
@@ -2577,7 +2752,7 @@ This problem is solved using a numerical optimizer.
<refnamediv>
<refname>dynare_sensitivity</refname>
- <refpurpose>interface to the global sensitivity analysis (GSA) toolbox developed by the Joint Research Center of the European Commission</refpurpose>
+ <refpurpose>interface to the global sensitivity analysis (GSA) toolbox developed by the Joint Research Center of the European Commission. The GSA toolbox needs to be downloaded separately from the JRC web site (<ulink url="http://eemc.jrc.ec.europa.eu/Software-DYNARE.htm">http://eemc.jrc.ec.europa.eu/Software-DYNARE.htm</ulink>)</refpurpose>
</refnamediv>
</refentry>