I am trying to use panel data techniques as a forecasting tools, although little research is dedicated to it. Generally, forecasting in a traditional sense is somehow related to time-series econometric. However, Baltaigi (2008) notes various benefits with panel data forecasts, so I want to fallow this avenue as it suits my data-setting better.

I have a balanced panel for 93 countries, 2000-2024 period (few years obviously are predictions) for some variables, but 2000-2018 for other variables including my dependent variable. it is not a huge sample for GMM , but i apply it (just to try it. in a more perfect world I should have larger N and smaller T). These are my results.

I am aware that the model could be improved, following Roodman 2009 (especially related to Sargan Hansen test, but let's ignore it). Then following forecast command which in stata help also states the following

I apply forecast following the steps suggested. I want to get out of sample forecast for 2020 - 2024 period

why do I get this message in the 4th step? I change the beginning year to 2019 but that does not help.

Does anyone have experience with foresting with panel?

Thanks!

Reference:

Roodman, D. (2009). How to do xtabond2: An introduction to difference and system GMM in Stata.

Baltagi, B. H. (2008). Forecasting with panel data.

I have a balanced panel for 93 countries, 2000-2024 period (few years obviously are predictions) for some variables, but 2000-2018 for other variables including my dependent variable. it is not a huge sample for GMM , but i apply it (just to try it. in a more perfect world I should have larger N and smaller T). These are my results.

Code:

Favoring space over speed. To switch, type or click on mata: mata set matafavor speed, perm. Warning: Number of instruments may be large relative to number of observations. Warning: Two-step estimated covariance matrix of moments is singular. Using a generalized inverse to calculate optimal weighting matrix for two-step estimation. Difference-in-Sargan/Hansen statistics may be negative. Dynamic panel-data estimation, two-step system GMM ------------------------------------------------------------------------------ Group variable: country3 Number of obs = 1763 Time variable : year Number of groups = 93 Number of instruments = 98 Obs per group: min = 18 F(30, 92) = 1243.86 avg = 18.96 Prob > F = 0.000 max = 19 ------------------------------------------------------------------------------ | Corrected lny | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- ly | L1. | .374036 .0687893 5.44 0.000 .2374146 .5106574 | lng | .6740826 .1040717 6.48 0.000 .4673872 .880778 eu | -.3168764 .1618267 -1.96 0.053 -.6382782 .0045253 O... | .007962 .0034658 2.30 0.024 .0010786 .0148454 lne | .0054246 .0267454 0.20 0.840 -.0476942 .0585433 | year | 2000 | 0 (empty) 2001 | -3.293952 .8267903 -3.98 0.000 -4.936029 -1.651875 2002 | -3.275765 .8331387 -3.93 0.000 -4.930451 -1.62108 2003 | -3.051059 .8255846 -3.70 0.000 -4.690741 -1.411377 2004 | -2.963923 .8386126 -3.53 0.001 -4.62948 -1.298366 2005 | -2.991701 .8578211 -3.49 0.001 -4.695408 -1.287995 2006 | -2.742058 .8313055 -3.30 0.001 -4.393103 -1.091014 2007 | -2.619339 .8606872 -3.04 0.003 -4.328738 -.9099403 2008 | -2.739219 .8337378 -3.29 0.001 -4.395095 -1.083344 2009 | -3.107684 .8435374 -3.68 0.000 -4.783022 -1.432346 2010 | -2.925138 .8636608 -3.39 0.001 -4.640443 -1.209834 2011 | -2.81268 .8808837 -3.19 0.002 -4.562191 -1.063169 2012 | -2.915922 .8721009 -3.34 0.001 -4.64799 -1.183854 2013 | -3.02399 .8874551 -3.41 0.001 -4.786553 -1.261428 2014 | -3.044528 .8826193 -3.45 0.001 -4.797486 -1.29157 2015 | -3.103196 .8773961 -3.54 0.001 -4.84578 -1.360611 2016 | -3.004841 .8713689 -3.45 0.001 -4.735455 -1.274228 2017 | -3.010357 .8898926 -3.38 0.001 -4.777761 -1.242954 2018 | -3.065278 .8937723 -3.43 0.001 -4.840387 -1.290169 2019 | -3.582186 .8513228 -4.21 0.000 -5.272986 -1.891385 2020 | 0 (omitted) 2021 | 0 (omitted) 2022 | 0 (omitted) 2023 | 0 (omitted) 2024 | 0 (omitted) | _cons | 0 (omitted) ------------------------------------------------------------------------------ Instruments for first differences equation Standard D.(O eu lne 2000b.year 2001.year 2002.year 2003.year 2004.year 2005.year 2006.year 2007.year 2008.year 2009.year 2010.year 2011.year 2012.year 2013.year 2014.year 2015.year 2016.year 2017.year 2018.year 2019.year 2020.year 2021.year 2022.year 2023.year 2024.year) GMM-type (missing=0, separate instruments for each period unless collapsed) L(1/3).lng L(1/3).L.lny collapsed Instruments for levels equation Standard open3 eu lnexc 2000b.year 2001.year 2002.year 2003.year 2004.year 2005.year 2006.year 2007.year 2008.year 2009.year 2010.year 2011.year 2012.year 2013.year 2014.year 2015.year 2016.year 2017.year 2018.year 2019.year 2020.year 2021.year 2022.year 2023.year 2024.year _cons GMM-type (missing=0, separate instruments for each period unless collapsed) D.lng D.L.lny collapsed ------------------------------------------------------------------------------ Arellano-Bond test for AR(1) in first differences: z = -3.60 Pr > z = 0.000 Arellano-Bond test for AR(2) in first differences: z = 1.61 Pr > z = 0.108 ------------------------------------------------------------------------------ Sargan test of overid. restrictions: chi2(67) = 152.25 Prob > chi2 = 0.000 (Not robust, but not weakened by many instruments.) Hansen test of overid. restrictions: chi2(67) = 81.44 Prob > chi2 = 0.110 (Robust, but weakened by many instruments.) Difference-in-Hansen tests of exogeneity of instrument subsets: GMM instruments for levels Hansen test excluding group: chi2(47) = 59.47 Prob > chi2 = 0.105 Difference (null H = exogenous): chi2(20) = 21.97 Prob > chi2 = 0.342 gmm(L.lnfdi, collapse lag(1 3)) Hansen test excluding group: chi2(63) = 75.13 Prob > chi2 = 0.141 Difference (null H = exogenous): chi2(4) = 6.31 Prob > chi2 = 0.177 iv(O eu lne 2000b.year 2001.year 2002.year 2003.year 2004.year 2005.year 2006.year 2007.ye ar 2008.year 2009.year 2010.year 2011.year 2012.year 2013.year 2014.year 2015.year 2016.year 2017.year 2018.year 2019.year 2020.year 2021.year 2022.year 2023.year 2024.year) Hansen test excluding group: chi2(46) = 63.23 Prob > chi2 = 0.047 Difference (null H = exogenous): chi2(21) = 18.21 Prob > chi2 = 0.636

forecast works with both time-series and panel datasets. time-series datasets may not contain any gaps, and panel datasets must be strongly balanced.

Code:

estimates store spec1 //first step forecast create spec1forecast, replace //second step Forecast model spec1forecast started forecast estimates spec1 //3rd step forecast will use the default type of prediction for xtabond2. Verify this is appropriate; see xtabond2 postestimation. Use the predict() option with forecast estimates to override the default. Added estimation results from xtabond2. Forecast model spec1forecast now contains 1 endogenous variable. forecast solve, prefix(f_) begin(year(2000)) end (year(2024)) //step begin(year(2019)) out of range Time variable year runs from 2000 through 2024. r(459);

Does anyone have experience with foresting with panel?

Thanks!

Reference:

Roodman, D. (2009). How to do xtabond2: An introduction to difference and system GMM in Stata.

*The stata journal*,*9*(1), 86-136.Baltagi, B. H. (2008). Forecasting with panel data.

*Journal of forecasting*,*27*(2), 153-173.
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