(more) tweaks and updates for example scripts in Guide

doc/tex/dpanel.tex

@@ -332,11 +332,11 @@ \subsection{Regressors}
   dpanel 1 ; y const
 \end{code}
 In this case the default \texttt{dpanel} behavior, which agrees with
-David Roodman's \texttt{xtabond2} for Stata \citep{Roodman2009}, is
-to drop the constant (since differencing reduces it to nothing but
-zeros). However, for compatibility with the DPD package for Ox, you
-can give the option \verb|--dpdstyle|, in which case the constant is
-retained (equivalent to including a linear trend in
+David Roodman's \texttt{xtabond2} for Stata \citep{Roodman2009}, is to
+drop the constant (since differencing reduces it to nothing but
+zeros). However, for compatibility with the \app{DPD} package for
+\app{Ox}, you can give the option \verb|--dpdstyle|, in which case the
+constant is retained (equivalent to including a linear trend in
 equation~\ref{eq:dpd-def}).  A similar point applies to the
 period-specific dummy variables which can be added in \texttt{dpanel}
 via the \verb|--time-dummies| option: in the differences-only case
@@ -539,24 +539,29 @@ \section{Replication of DPD results}
 
 The examples are based on the program files \texttt{abest1.ox},
 \texttt{abest3.ox} and \texttt{bbest1.ox}. These are included in the
-DPD package, along with the Arellano--Bond database files
+\app{DPD} package, along with the Arellano--Bond database files
 \texttt{abdata.bn7} and \texttt{abdata.in7}.\footnote{See
-  \url{http://www.doornik.com/download.html}.} The
-Arellano--Bond data are also provided with gretl, in the file
-\texttt{abdata.gdt}. In the following we do not show the output from
-DPD or gretl; it is somewhat voluminous, and is easily generated by
-the user. As of this writing the results from Ox/DPD and gretl are
-identical in all relevant respects for all of the examples
-shown.\footnote{To be specific, this is using Ox Console version 5.10,
-  version 1.24 of the DPD package, and gretl built from CVS as of
-  2010-10-23, all on Linux.}
-
-A complete Ox/DPD program to generate the results of interest takes
-this general form:
-
-\begin{code}
+  \url{http://www.doornik.com/download.html}.} The Arellano--Bond data
+are also provided with gretl, in the file \texttt{abdata.gdt}. In the
+following we do not show the output from DPD or gretl; it is somewhat
+voluminous, and is easily generated by the user. As of this writing
+the results from Ox/DPD and gretl are identical in all relevant
+respects for all of the examples shown.\footnote{To be specific, this
+  was last tested using Ox Console version 9.10, version 1.28 of the
+  DPD package, and gretl built from git as of 2024-02-12, all on
+  Linux.}
+
+An Ox/DPD program to generate the results of interest takes the
+general form shown in Listing~\ref{dpd-skeleton}. In the examples
+below we take this template for granted and show just the
+model-specific code.
+
+\begin{script}
+  \caption{Template for Ox/DPD program}
+  \label{dpd-skeleton}
+\begin{scode}
 #include <oxstd.h>
-#import <packages/dpd/dpd>
+#import <packages/DPD/dpd>
 
 main()
 {
@@ -565,12 +570,13 @@ \section{Replication of DPD results}
     dpd.Load("abdata.in7");
     dpd.SetYear("YEAR");
 
-    // model-specific code here
+    // model-specific code goes here
 
     delete dpd;
 }
-\end{code}
-%
+\end{scode}
+\end{script}
+
 In the examples below we take this template for granted and show just
 the model-specific code.
 
@@ -595,17 +601,17 @@ \subsection{Example 1}
 
 \begin{code}
 dpd.SetOptions(FALSE); // don't use robust standard errors
-dpd.Select(Y_VAR, {"n", 0, 2});
-dpd.Select(X_VAR, {"w", 0, 1, "k", 0, 0, "ys", 0, 1});
-dpd.Select(I_VAR, {"w", 0, 1, "k", 0, 0, "ys", 0, 1});
+dpd.Select(DPD::Y_VAR, {"n", 0, 2});
+dpd.Select(DPD::X_VAR, {"w", 0, 1, "k", 0, 0, "ys", 0, 1});
+dpd.Select(DPD::I_VAR, {"w", 0, 1, "k", 0, 0, "ys", 0, 1});
 
 dpd.Gmm("n", 2, 99);
-dpd.SetDummies(D_CONSTANT + D_TIME);
+dpd.SetDummies(DPD::D_CONSTANT + DPD::D_TIME);
 
 print("\n\n***** Arellano & Bond (1991), Table 4 (b)");
-dpd.SetMethod(M_1STEP);
+dpd.SetMethod(DPD::M_1STEP);
 dpd.Estimate();
-dpd.SetMethod(M_2STEP);
+dpd.SetMethod(DPD::M_2STEP);
 dpd.Estimate();
 \end{code}
 
@@ -631,8 +637,8 @@ \subsection{Example 1}
 
 \subsection{Example 2}
 
-The DPD file \texttt{abest3.ox} contains a variant of the above that
-differs with regard to the choice of instruments: the variables
+The \app{DPD} file \texttt{abest3.ox} contains a variant of the above
+that differs with regard to the choice of instruments: the variables
 \texttt{w} and \texttt{k} are now treated as predetermined, and are
 instrumented GMM-style using the second and third lags of their
 levels. This approximates column (c) of Table 4 in
@@ -642,18 +648,18 @@ \subsection{Example 2}
 DPD and gretl with 2-step estimation:
 
 \begin{code}
-dpd.Select(Y_VAR, {"n", 0, 2});
-dpd.Select(X_VAR, {"w", 0, 1, "k", 0, 0, "ys", 0, 1});
-dpd.Select(I_VAR, {"ys", 0, 1});
-dpd.SetDummies(D_CONSTANT + D_TIME);
+dpd.Select(DPD::Y_VAR, {"n", 0, 2});
+dpd.Select(DPD::X_VAR, {"w", 0, 1, "k", 0, 0, "ys", 0, 1});
+dpd.Select(DPD::I_VAR, {"ys", 0, 1});
+dpd.SetDummies(DPD::D_CONSTANT + DPD::D_TIME);
 
 dpd.Gmm("n", 2, 99);
 dpd.Gmm("w", 2, 3);
 dpd.Gmm("k", 2, 3);
 
 print("\n***** Arellano & Bond (1991), Table 4 (c)\n");
 print("        (but using different instruments!!)\n");
-dpd.SetMethod(M_2STEP);
+dpd.SetMethod(DPD::M_2STEP);
 dpd.Estimate();
 \end{code}
 
@@ -674,31 +680,32 @@ \subsection{Example 2}
 
 \subsection{Example 3}
 
-Our third example replicates the DPD output from \texttt{bbest1.ox}:
-this uses the same dataset as the previous examples but the model
-specifications are based on \cite{blundell-bond98}, and involve
-comparison of the GMM-DIF and GMM-SYS (``system'') estimators. The
-basic specification is slightly simplified in that the variable
-\texttt{ys} is not used and only one lag of the dependent variable
-appears as a regressor. The Ox/DPD code is:
+Our third example replicates the \app{DPD} output from
+\texttt{bbest1.ox}: this uses the same dataset as the previous
+examples but the model specifications are based on
+\cite{blundell-bond98}, and involve comparison of the GMM-DIF and
+GMM-SYS (``system'') estimators. The basic specification is slightly
+simplified in that the variable \texttt{ys} is not used and only one
+lag of the dependent variable appears as a regressor. The Ox/DPD code
+is:
 
 \begin{code}
-dpd.Select(Y_VAR, {"n", 0, 1});
-dpd.Select(X_VAR, {"w", 0, 1, "k", 0, 1});
-dpd.SetDummies(D_CONSTANT + D_TIME);
+dpd.Select(DPD::Y_VAR, {"n", 0, 1});
+dpd.Select(DPD::X_VAR, {"w", 0, 1, "k", 0, 1});
+dpd.SetDummies(DPD::D_CONSTANT + DPD::D_TIME);
 
 print("\n\n***** Blundell & Bond (1998), Table 4: 1976-86 GMM-DIF");
 dpd.Gmm("n", 2, 99);
 dpd.Gmm("w", 2, 99);
 dpd.Gmm("k", 2, 99);
-dpd.SetMethod(M_2STEP);
+dpd.SetMethod(DPD::M_2STEP);
 dpd.Estimate();
 
 print("\n\n***** Blundell & Bond (1998), Table 4: 1976-86 GMM-SYS");
 dpd.GmmLevel("n", 1, 1);
 dpd.GmmLevel("w", 1, 1);
 dpd.GmmLevel("k", 1, 1);
-dpd.SetMethod(M_2STEP);
+dpd.SetMethod(DPD::M_2STEP);
 dpd.Estimate();
 \end{code}
 
@@ -832,9 +839,12 @@ \section{Cross-country growth example}
 values dataset containing the data as transformed in the gretl script
 shown above, using a missing-value code appropriate to the target
 program.) For reference, the commands used with Stata are reproduced
-below:
-%
-\begin{code}
+in Listing~\ref{xtabond2-code}.
+
+\begin{script}[htbp]
+  \caption{Stata commands for GDP growth example}
+  \label{xtabond2-code}
+\begin{scode}
 #delimit ;
 insheet using CEL.csv
 tsset unit time;
@@ -844,7 +854,8 @@ \section{Cross-country growth example}
   gmm(lngd, lag(2 99)) rob nolev twostep;
 xtabond2 ly L.ly linv lngd, gmm(L.ly, lag(1 99)) gmm(linv, lag(2 99)) 
   gmm(lngd, lag(2 99)) rob nocons twostep;
-\end{code}
+\end{scode}
+\end{script}
 
 For the GMM-DIF model all three programs find 382 usable observations
 and 30 instruments, and yield identical parameter estimates and

doc/tex/functions.tex

@@ -846,6 +846,8 @@ \subsection{Overloading}
    endif
 end function
 
+set seed 12345
+
 # call 1: x and y are scalars
 eval x_plus_b_y(10, 3, 2)
 

doc/tex/gretlOx.tex

@@ -112,12 +112,13 @@ \section{\app{Ox} support in gretl}
 \section{Illustration: replication of DPD model}
 \label{sec:dpd-replication}
 
-Listing~\ref{script:Ox-DPD} shows a more ambitious case.  This script
+Listing~\ref{ex:ox-dpd} shows a more ambitious case.  This script
 replicates one of the dynamic panel data models in
 \cite{arellano-bond91}, first using gretl and then using \app{Ox}; we
 then check the relative differences between the parameter estimates
 produced by the two programs (which turn out to be reassuringly
-small).
+small). This was last tested using version 9.10 of \app{Ox console}
+for Linux.
 
 Unlike the previous example, in this case we pass the dataset from
 gretl to \app{Ox} as a CSV file in order to preserve the
@@ -131,30 +132,29 @@ \section{Illustration: replication of DPD model}
 gretl side.  The \verb|gretl_export| function takes two arguments, a
 matrix and a file name.  The file is written into the user's dotdir,
 from where it can be picked up using \texttt{mread}.  The final
-portion of the output from Listing~\ref{script:Ox-DPD} is shown below:
+portion of the output from Listing~\ref{ex:ox-dpd} is shown below:
 %
 \begin{code}
 ? matrix oxparm = mread("oxparm.mat", 1)
 Generated matrix oxparm
 ? eval abs((parm - oxparm) ./ oxparm)
-  1.4578e-13 
-  3.5642e-13 
-  5.0672e-15 
-  1.6091e-13 
-  8.9808e-15 
-  2.0450e-14 
-  1.0218e-13 
-  2.1048e-13 
-  9.5898e-15 
-  1.8658e-14 
-  2.1852e-14 
-  2.9451e-13 
-  1.9398e-13 
+  1.1094e-12 
+  1.6314e-12 
+  1.7885e-13 
+  1.1949e-12 
+  1.8999e-13 
+  1.1359e-13 
+  6.5339e-13 
+  3.1943e-12 
+  1.2108e-12 
+  3.7335e-13 
+  1.7468e-12 
+  3.9536e-12 
+  2.0980e-12
 \end{code}
 
 \begin{script}[htbp]
-  \caption{Estimation of dynamic panel data model via gretl and \app{Ox}}
-  \label{script:Ox-DPD}
+  \scriptinfo{ox-dpd}{Estimation of dynamic panel data model via gretl and \app{Ox}}
 \begin{scode}
 open abdata.gdt
 
@@ -169,20 +169,20 @@ \section{Illustration: replication of DPD model}
 # Replicate using the Ox DPD package
 foreign language=Ox
 #include <oxstd.h>
-#import <packages/dpd/dpd>
+#import <packages/DPD/dpd>
 
 main ()
 {
     decl dpd = new DPD();
     dpd.Load("@dotdir/abdata.csv"); 
     dpd.SetYear("YEAR");
 
-    dpd.Select(Y_VAR, {"n", 0, 2});
-    dpd.Select(X_VAR, {"w", 0, 1, "k", 0, 0, "ys", 0, 1});
-    dpd.Select(I_VAR, {"w", 0, 1, "k", 0, 0, "ys", 0, 1});
+    dpd.Select(DPD::Y_VAR, {"n", 0, 2});
+    dpd.Select(DPD::X_VAR, {"w", 0, 1, "k", 0, 0, "ys", 0, 1});
+    dpd.Select(DPD::I_VAR, {"w", 0, 1, "k", 0, 0, "ys", 0, 1});
 
     dpd.Gmm("n", 2, 99);  // GMM-type instrument
-    dpd.SetDummies(D_CONSTANT + D_TIME);
+    dpd.SetDummies(DPD::D_CONSTANT + DPD::D_TIME);
     dpd.SetTest(2, 2); // Sargan, AR 1-2 tests
     dpd.Estimate();    // 1-step estimation
     decl parm = dpd.GetPar();

doc/tex/realtime.tex

@@ -423,6 +423,11 @@ \subsection{An illustration}
 The script that produced this Figure is shown in full in
 Listing~\ref{ex:realtime-revisions}.
 
+\tip{To replicate the examples in Listings \ref{ex:realtime-revisions}
+  and \ref{ex:revision-history} below you'll need the Alfred file
+  \texttt{INDPRO.txt}, which is available as
+  \url{https://gretl.sf.net/gretldata/INDPRO.txt}.}
+
 \begin{figure}[htbp]
   \centering
 \includegraphics{figures/realtime}

doc/tex/tsfilter.tex

@@ -378,6 +378,7 @@ \section{The discrete Fourier transform}
 \begin{scode}
 set verbose off
 nulldata 50
+set seed 76543
 # generate an AR(1) process
 series e = normal()
 series x = 0