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ASYMPTOTICALLY EFFICIENT MODEL SELECTION FOR PANEL DATA FORECASTING

Published online by Cambridge University Press:  30 October 2018

Ryan Greenaway-McGrevy
Ryan Greenaway-McGrevy*
Affiliation:
The University of Auckland
*
*Address correspondence to Ryan Greenaway-McGrevy, Department of Economics, The University of Auckland, Auckland, New Zealand; e-mail: r.mcgrevy@auckland.ac.nz.
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Abstract

This article develops new model selection methods for forecasting panel data using a set of least squares (LS) vector autoregressions. Model selection is based on minimizing the estimated quadratic forecast risk among candidate models. We provide conditions under which the selection criterion is asymptotically efficient in the sense of Shibata (1980) as n (cross sections) and T (time series) approach infinity. Relative to extant selection criteria, this criterion places a heavier penalty on model dimensionality in order to account for the effects of parameterized forms of cross sectional heterogeneity (such as fixed effects) on forecast loss. We also extend the analysis to bias-corrected least squares, showing that significant reductions in forecast risk can be achieved.

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Type
ARTICLES
Information
Econometric Theory , Volume 35 , Issue 4 , August 2019 , pp. 842 - 899
Copyright
Copyright © Cambridge University Press 2018 

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Footnotes

The author thanks Yong Bao, Peter C.B. Phillips, Donggyu Sul, and seminar participants at the University of Auckland, the 23rd NZESG meeting, and the 22nd Midwest Econometrics Group meeting for their comments. This work was supported in part by the Marsden Fund Council from Government funding, administered by the Royal Society of New Zealand under grant No. 16-UOA-239.

References

REFERENCES

Akaike, H. (1970) Statistical predictor identification. Annals of the Institute of Statistical Mathematics 22, 203–419.10.1007/BF02506337CrossRefGoogle Scholar
Alvarez, J. & Arellano, M. (2003) The time series and cross-section asymptotics of dynamic panel data estimators. Econometrica 71, 1121–1159.10.1111/1468-0262.00441CrossRefGoogle Scholar
Baillie, R.T. & Baltagi, B.H. (1999) Prediction from the regression model with one-way error components. In Hsiao, C., Lahiri, K., Lee, L.F., & Pesaran, H. (eds.), Analysis of Panels and Limited Dependent Variable Models, pp. 255–267. Cambridge University Press.10.1017/CBO9780511493140.012CrossRefGoogle Scholar
Baltagi, B.H. (2008) Forecasting with panel data. Journal of Forecasting 27, 153–173.10.1002/for.1047CrossRefGoogle Scholar
Baltagi, B.H., Bresson, G., & Pirotte, A. (2002) Comparison of forecast performance for homogeneous, heterogeneous and shrinkage estimators: Some empirical evidence from US electricity and natural-gas consumption. Economics Letters 76, 375–382.10.1016/S0165-1765(02)00065-4CrossRefGoogle Scholar
Baltagi, B.H. & Griffin, J.M. (1997) Pooled estimators vs. their heterogeneous counterparts in the context of dynamic demand for gasoline. Journal of Econometrics 77, 303–327.10.1016/S0304-4076(96)01802-7CrossRefGoogle Scholar
Baltagi, B.H., Griffin, J.M., & Xiong, W. (2000) To pool or not to pool: Homogeneous versus heterogeneous estimators applied to cigarette demand. Review of Economics and Statistics 82, 117–126.10.1162/003465300558551CrossRefGoogle Scholar
Baltagi, B.H. & Li, Q. (1992) Prediction in the one-way error component model with serial correlation. Journal of Forecasting 11, 561–567.10.1002/for.3980110605CrossRefGoogle Scholar
Bhansali, R.J. (1996) Asymptotically efficient autoregressive model selection for multistep prediction. Annals of the Institute of Statistical Mathematics 48, 577–602.10.1007/BF00050857CrossRefGoogle Scholar
Bhansali, R.J. (1997) Direct autoregressive predictors for multistep prediction: Order selection and performance relative to plug-in predictors. Statistica Sinica 7, 425–449.Google Scholar
Brillinger, D.R. (1981) Time Series: Data Analysis and Theory. Holden-Day.Google Scholar
Brucker, H. & Siliverstovs, B. (2006) On the estimation and forecasting of international migration: How relevant is heterogeneity across countries? Empirical Economics 31, 735–754.10.1007/s00181-005-0049-yCrossRefGoogle Scholar
Chudik, A., Pesaran, M.H., & Tosetti, E. (2011) Weak and strong cross-section dependence and estimation of large panels. The Econometrics Journal, 1, 45–90.10.1111/j.1368-423X.2010.00330.xCrossRefGoogle Scholar
Driver, C., Imai, K., Temple, P., & Urga, A. (2004) The effect of uncertainty on UK investment authorisation: Homogeneous vs. heterogeneous estimators. Empirical Economics 29, 115–128.10.1007/s00181-003-0192-2CrossRefGoogle Scholar
Eisinberg, A. & Fedele, G. (2007) Discrete orthogonal polynomials on equidistant nodes. International Mathematical Forum 21, 1007–102010.12988/imf.2007.07087CrossRefGoogle Scholar
Findley, D.F. (1984) On some ambiguities associated with the fitting of ARMA models to time series. Journal of Time Series Analysis 5, 213–225.10.1111/j.1467-9892.1984.tb00388.xCrossRefGoogle Scholar
Findley, D.F. & Wei, C.Z. (1993) Moment bounds for deriving time series CLT’s and model selection procedures. Statistica Sinica 3, 453–480.Google Scholar
Findley, D.F. & Wei, C.Z. (2002) AIC, overfitting principles, and boundedness of moments of inverse matrices for vector autoregressions and related models. Journal of Multivariate Analysis 83, 415–450.10.1006/jmva.2001.2063CrossRefGoogle Scholar
Fuller, W.A. & Hasza, D.P. (1981) Properties of predictors for autoregressive time series. Journal of American Statistical Association 76, 155–161.10.1080/01621459.1981.10477622CrossRefGoogle Scholar
Gavin, W.T. & Theodorou, A.T. (2005) A common model approach to macroeconomics: Using panel data to reduce sampling error. Journal of Forecasting 24, 203–219.10.1002/for.954CrossRefGoogle Scholar
Greenaway-McGrevy, R. (2013) Multistep prediction of panel autoregressive processes. Econometric Theory 29, 699–73410.1017/S0266466612000679CrossRefGoogle Scholar
Greenaway-McGrevy, R. (2015) Evaluating panel data forecasts under independent realization. Journal of Multivariate Analysis 136, 108–125.10.1016/j.jmva.2015.01.004CrossRefGoogle Scholar
Hahn, J. & Kuersteiner, G. (2002) Asymptotically unbiased inference for a dynamic panel model with fixed effects when both n and T are large. Econometrica 70, 1639–1657.10.1111/1468-0262.00344CrossRefGoogle Scholar
Hahn, J. & Kuersteiner, G. (2011) Bias reduction for dynamic nonlinear panel models with fixed effects. Econometric Theory 27, 1152–1191.10.1017/S0266466611000028CrossRefGoogle Scholar
Han, C., Phillips, P.C.B., & Sul, D. (2017) Lag length selection in panel autoregression. Econometric Reviews 36, 225–240.10.1080/07474938.2015.1114313CrossRefGoogle Scholar
Hansen, C.B. (2007) Asymptotic properties of a robust variance matrix estimator for panel data when T is large. Journal of Econometrics 141, 597–620.10.1016/j.jeconom.2006.10.009CrossRefGoogle Scholar
Hoogstrate, A.J., Palm, F.C., & Pfann, G.A. (2000) Pooling in dynamic panel-data models: An application to forecasting GDP growth rates. Journal of Business and Economic Statistics 18, 274–283.Google Scholar
Ing, C.K. (2003) Multistep prediction in autoregressive processes. Econometric Theory 19, 254–279.10.1017/S0266466603192031CrossRefGoogle Scholar
Ing, C.K. & Wei, C.Z. (2003) On same-realization prediction in an infinite-order autoregressive process. Journal of Multivariate Analysis 85, 130–155.10.1016/S0047-259X(02)00029-5CrossRefGoogle Scholar
Ing, C.K. & Wei, C.Z. (2005) Order selection for same-realization predictions in autoregressive processes. Annals of Statistics 33, 2423–2474.10.1214/009053605000000525CrossRefGoogle Scholar
Kiviet, J.F. (1995) On bias, inconsistency and efficiency of some estimators in dynamic panel data models. Journal of Econometrics 68, 53–78.10.1016/0304-4076(94)01643-ECrossRefGoogle Scholar
Kunitomo, N. & Yamamoto, T. (1985) Properties of predictors in misspecified autoregressive time series models. Journal of the American Statistical Association 80, 941–950.10.1080/01621459.1985.10478208CrossRefGoogle Scholar
Lee, Y. (2006) A General Approach to Bias Correction in Dynamic Panels under Time Series Misspecification. Ph.D. dissertation, Yale University.10.2139/ssrn.1004386CrossRefGoogle Scholar
Lee, Y. (2012) Bias in dynamic panel models under time series misspecification. Journal of Econometrics 169, 54–60.10.1016/j.jeconom.2012.01.009CrossRefGoogle Scholar
Lee, Y. & Phillips, P.C.B. (2015) Model selection in the presence of incidental parameters. Journal of Econometrics 188, 474–489.10.1016/j.jeconom.2015.03.012CrossRefGoogle Scholar
Mallows, C.L. (1973) Some comments on Cp. Technometrics 15, 661–675.Google Scholar
Mark, N. & Sul, D. (2001) Nominal exchange rates and monetary fundamentals: Evidence from a seventeen country panel. Journal of International Economics 53, 29–52.10.1016/S0022-1996(00)00052-0CrossRefGoogle Scholar
Nickell, S. (1981) Biases in dynamic models with fixed effects. Econometrica 49, 1417–1425.10.2307/1911408CrossRefGoogle Scholar
Phillips, P.C.B. & Sul, D. (2007) Bias in dynamic panel estimation with fixed effects, incidental trends and cross section dependence. Journal of Econometrics 137, 162–188.10.1016/j.jeconom.2006.03.009CrossRefGoogle Scholar
Rapach, D.E. & Wohar, M.E. (2004) Testing the monetary model of exchange rate determination: A closer look at panels. Journal of International Money and Finance 23, 841–865.10.1016/j.jimonfin.2004.05.002CrossRefGoogle Scholar
Schorfheide, F. (2005) VAR forecasting under misspecification. Journal of Econometrics 128, 99–136.10.1016/j.jeconom.2004.08.009CrossRefGoogle Scholar
Shibata, R. (1980) Asymptotically efficient selection of the order of the model for estimating parameters of a linear process. Annals of Statistics 8, 147–164.10.1214/aos/1176344897CrossRefGoogle Scholar
Speed, T.P. & Yu, B. (1993) Model selection and prediction: Normal regression. Annals of the Institute of Statistical Mathematics 45, 35–54.10.1007/BF00773667CrossRefGoogle Scholar
Stock, J.H. & Watson, M.W. (2010) Introduction to Econometrics, 3rd ed. Addison-Wesley.Google Scholar
Taub, A.J. (1979) Prediction in the context of the variance components model. Journal of Econometrics 10, 103–107.10.1016/0304-4076(79)90068-XCrossRefGoogle Scholar