Use of "fcast" with panel data is now enabled (to varying degrees) for pooled OLS, fixed effects, random effects and dpanel.

Caveat: It's not clear what we should do if the model specification includes time dummies. At present we impute an out-of-sample time effect equal to the mean of the in-sample time effects. This is obviously debatable; feel free to debate it. Perhaps we should ban such prediction altogether.

Now to out-of-sample in the cross-sectional dimension. What are we going to do with individual effects, for the fixed- and random-effects estimators? For now, we set the predicted individual effect to the global constant (for fixed effects) or to its expected value of zero (for random effects). This is obviously another debatable point, analogous to the out-of-sample time effect case.

BTW, I see that Stata is very restrictive in this department. With "predict" after "xtreg" you can't get an out of sample forecast even in the time dimension when no time dummies are present -- other than plain "xb" which omits individual effects, whether fixed or random.

On Tue, 26 Feb 2019, Sven Schreiber wrote: > Am 26.02.2019 um 20:06 schrieb Allin Cottrell: >> BTW, I see that Stata is very restrictive in this department. With >> "predict" after "xtreg" you can't get an out of sample forecast even >> in the time dimension when no time dummies are present -- other than >> plain "xb" which omits individual effects, whether fixed or random. > > Well for small-T (standard micro panel assumption) the unit effects > are not well estimated, are they? That might be the reason they ban > it. (And probably a good reason.)

But whatever Stata's up to, it's worth assessing whether individual fixed effects estimated with small T are actually any use in forecasting out of sample in the time dimension. I show below a Monte Carlo experiment, comparing out-of-sample MSE for forecasts computed with and without individual effects. (In the "without" case I use the global constant, as in Stata's "xb" forecast for fixed effects.) I simulate 100 individuals observed over T periods, T-1 of which are used in estimation leaving the last for an out-of-sample forecast, with T = 4 as baseline. Executive summary: As Sven suggests, if T is small enough (and the fixed effects are moderate in size, even if statistically significant at the usual level on a joint test), then the estimated effects are just noise so far as forecasting is concerned; the forecast MSE tends to be smaller without them. But if T is a bit larger (even just 5 as opposed to the 4 in the script), or if the scale of the fixed effects passes a certain threshold[*], then inclusion of the estimated fixed effects enhances the forecast accuracy quite substantially.

> fcast --out-of-sample --quiet

On Tue, 26 Feb 2019, Sven Schreiber wrote: > Am 26.02.2019 um 20:06 schrieb Allin Cottrell: >> BTW, I see that Stata is very restrictive in this department. With >> "predict" after "xtreg" you can't get an out of sample forecast even in the >> time dimension when no time dummies are present -- other than plain "xb" >> which omits individual effects, whether fixed or random. > > Well for small-T (standard micro panel assumption) the unit > effects are not well estimated, are they? That might be the reason > they ban it. (And probably a good reason.) Interesting point. I doubt that's the rationale in Stata's case, since the "ban" is not conditional on the time-series length of the panel. I suspect it's because Stata doesn't really make a distinction between out-of-sample in the cross-sectional dimension and out-of-sample in the time dimension: it's all just in or out of sample. But whatever Stata's up to, it's worth assessing whether individual fixed effects estimated with small T are actually any use in forecasting out of sample in the time dimension. I show below a Monte Carlo experiment, comparing out-of-sample MSE for forecasts computed with and without individual effects. (In the "without" case I use the global constant, as in Stata's "xb" forecast for fixed effects.) I simulate 100 individuals observed over T periods, T-1 of which are used in estimation leaving the last for an out-of-sample forecast, with T = 4 as baseline. Executive summary: As Sven suggests, if T is small enough (and the fixed effects are moderate in size, even if statistically significant at the usual level on a joint test), then the estimated effects are just noise so far as forecasting is concerned; the forecast MSE tends to be smaller without them. But if T is a bit larger (even just 5 as opposed to the 4 in the script), or if the scale of the fixed effects passes a certain threshold[*], then inclusion of the estimated fixed effects enhances the forecast accuracy quite substantially. [*] in the script below, with fixed effects simulated by drawings from U(0,1) * fe_scale, an fe_scale value of 2.5 is sufficient to make inclusion of the estimated fixed clearly beneficial, even with a scanty T = 4. <hansl> set verbose off # set seed 87564427 T = 4 N = 100 NT = N*T nulldata NT --preserve setobs T 1:1 --stacked-time-series genr time series x = normal() list X = 0 x series u = normal() # generate scaled uniform random fixed effect fe_scale = 2.5 matrix am = fe_scale * muniform(N,1) series a = pexpand(am) K = 10000 matrix MSE = zeros(K, 2) matrix fepv = zeros(K, 1) loop i=1..K -q series u = normal() series y = 5 + x + u + a smpl time < T --restrict panel y 0 x --quiet fepv[i] = $model.fixed_effects_F.pvalue matrix b = $coeff fcast --out-of-sample --quiet smpl time == T --restrict --replace series fca = $fcast series fc0 = lincomb(X, b) MSE[i,1] = sum((y - fca)^2) / $nobs MSE[i,2] = sum((y - fc0)^2) / $nobs smpl full endloop printf "Forecast MSEs\n" means = meanc(MSE) sds = sdc(MSE) printf "Including fixed effects: mean %g, sd %g\n", means[1], sds[1] printf "Excluding fixed effects: mean %g, sd %g\n", means[2], sds[2] printf "memo: average p-value for fixed effects: %g\n", meanc(fepv) </hansl> Allin _______________________________________________ Gretl-users mailing list Gretl-users(a)lists.wfu.edu http://lists.wfu.edu/mailman/listinfo/gretl-users

From git://git.code.sf.net/p/gretl/git

> fcast --out-of-sample --quiet

> fcast --out-of-sample --quiet

Am 03.03.2019 um 08:22 schrieb Clive Nicholas: > clive@clivubu:~/gretl-git$ git pull ... > > Then: > > gretl version 2019b-git But you have rebuilt in-between (and also done make install)? (Sorry if this is a redundant question but it wasn't obvious from your "log".)

clive@clivubu:~$ ls -l `which gretlcli` -rwxr-xr-x 1 root root 127640 Feb 12 00:52 /usr/local/bin/gretlcli clive@clivubu:~$ ls -l `which gretl_x11` -rwxr-xr-x 1 root root 6172512 Feb 12 00:52 /usr/local/bin/gretl_x11 Make of that what you will.

Am 03.03.2019 um 08:22 schrieb Clive Nicholas: > clive@clivubu:~/gretl-git$ git pull ... > > Then: > > gretl version 2019b-git But you have rebuilt in-between (and also done make install)? (Sorry if this is a redundant question but it wasn't obvious from your "log".) cheers sven _______________________________________________ Gretl-users mailing list Gretl-users(a)lists.wfu.edu http://lists.wfu.edu/mailman/listinfo/gretl-users