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A NEW PANEL DATA TREATMENT FOR HETEROGENEITY IN TIME TRENDS

Published online by Cambridge University Press:  01 May 2012

Alois Kneip ,
Robin C. Sickles  and
Wonho Song
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Abstract

This paper introduces a new estimation method for arbitrary temporal heterogeneity in panel data models. The paper provides a semiparametric method for estimating general patterns of cross-sectional specific time trends. The methods proposed in the paper are related to principal component analysis and estimate the time-varying trend effects using a small number of common functions calculated from the data. An important application for the new estimator is in the estimation of time-varying technical efficiency considered in the stochastic frontier literature. Finite sample performance of the estimators is examined via Monte Carlo simulations. We apply our methods to the analysis of productivity trends in the U.S. banking industry.

Type
Research Article
Information
Econometric Theory , Volume 28 , Issue 3 , June 2012 , pp. 590 - 628
Copyright
Copyright © Cambridge University Press 2011

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Footnotes

Earlier versions of this paper under the title “On Estimating the Mixed Effects Model” were presented at North American Productivity Workshop, Toronto, June 2004; COMPSTAT 2004, Prague, August 2004; and the European Workshop of Efficiency and Productivity IX, Brussels, July 2005. The paper was also given at the 2005 Econometric Society World Congress, University College, London, and Michigan State, Rice University, and Syracuse University econometrics workshops. The authors thank participants at those conferences and workshops, particularly Peter Schmidt, Mahmoud El-Gamal, Yoosoon Chang, Joon Park, and Leopold Simar, for constructive criticisms and insights. We also thank three anonymous referees and editor Guido Kuersteiner for their insightful comments and criticisms that substantially strengthened the paper, and Levent Kutlu for his very crucial research assistance. The usual caveat applies. Song gratefully acknowledges financial support from Chung-Ang University through research grants in 2011.

References

REFERENCES

Adams, R.M., Berger, A.N., & Sickles, R.C. (1999) Semiparametric approaches to stochastic panel frontiers with applications in the banking industry. Journal of Business and Economic Statistics 17, 349–358.Google Scholar
Ahn, S.C., Lee, Y., & Schmidt, P.J. (2007) Stochastic frontier models with multiple time-varying individual effects. Journal of Productivity Analysis 27, 1–12.CrossRefGoogle Scholar
Aigner, D.J., Lovell, C.A.K., & Schmidt, P. (1977) Formulation and estimation of stochastic frontier models. Journal of Econometrics 6, 21–37.Google Scholar
Bada, O., Kneip, A., & Sickles, R.C. (2011) Panel Models with Temporal Heterogeneity in Effects and Correlated Errors. Mimeo, Rice University.Google Scholar
Bai, J. (2003) Inferential theory for factor models of large dimensions. Econometrica 71, 135–171.Google Scholar
Bai, J. (2009) Panel data models with interactive fixed effects. Econometrica 77, 1229–1279.Google Scholar
Bai, J. & Ng, S. (2002) Determining the number of factors in approximate factor models. Econometrica 70, 191–221.Google Scholar
Baltagi, B. (2005) Econometric Analysis of Panel Data. Wiley.Google Scholar
Baltagi, B., Egger, P., & Pfaffermayr, M. (2003) A generalized design for bilateral trade flow models. Economics Letters 80, 391–397.CrossRefGoogle Scholar
Battese, G.E. & Coelli, T.J. (1992) Frontier production functions, technical efficiency and panel data: With application to paddy farmers in India. Journal of Productivity Analysis 3, 153–169.CrossRefGoogle Scholar
Berger, A.N. (1993) “Distribution-free” estimates of efficiency in U.S. banking industry and tests of the standard distributional assumption. Journal of Productivity Analysis 4, 261–292.CrossRefGoogle Scholar
Bernanke, B.S. & Boivin, J. (2003) Monetary policy in a data-rich environment. Journal of Monetary Economics 50, 525–546.CrossRefGoogle Scholar
de Boor, C. (1978) A Practical Guide to Splines. Springer-Verlag.CrossRefGoogle Scholar
Chang, Y. (2004) Bootstrap unit root tests in panels with cross-sectional dependency. Journal of Econometrics 120, 263–293.CrossRefGoogle Scholar
Cornwell, C., Schmidt, P., & Sickles, R.C. (1990) Production frontiers with cross-sectional and time-series variation in efficiency levels. Journal of Econometrics 46, 185–200.Google Scholar
Engle, R., Granger, C., Rice, J., & Weiss, A. (1986) Nonparametric estimates of the relation between weather and electricity sales. Journal of the American Statistical Association 81, 310–320.Google Scholar
Eubank, R.L. (1988) Nonparametric Regression and Spline Smoothing. Marcel Dekker.Google Scholar
Ferré, L. (1995) Improvement of some multivariate estimates by reduction of dimensionality. Journal of Multivariate Analysis 54, 147–162.CrossRefGoogle Scholar
Forni, M., Hallin, M., Lippi, M., & Reichlin, L. (2000) The generalized dynamic factor model: Identification and estimation. Review of Economics and Statistics 82, 540–554.CrossRefGoogle Scholar
Forni, M. & Lippi, M. (1997) Aggregation and the Microfoundations of Dynamic Macroeconomics. Oxford University Press.CrossRefGoogle Scholar
Forni, M. & Reichlin, L. (1998) Let’s get real: A factor analytic approach to disaggregated business cycle dynamics. Review of Economic Studies 65, 453–473.CrossRefGoogle Scholar
Hahn, J. & Newey, W. (2004) Jackknife and analytical bias reduction for nonlinear panel models. Econometrica 72, 1295–1319.CrossRefGoogle Scholar
Härdle, W., Liang, H., & Gao, J. (2000) Partially Linear Models. Physica-Verlag.CrossRefGoogle Scholar
Jayasiriya, R. (2000) Essays on Structural Modeling Using Nonparametric and Parametric Methods with Applications in the U.S. Banking Industry. Ph.D. dissertation, Rice University.Google Scholar
Kao, C. & Chiang, M.H. (2000) On the estimation and inference of a cointegrated regression in panel data. Advances in Econometrics 15, 179–222.CrossRefGoogle Scholar
Klee, E.C. & Natalucci, F.M. (2005) Profits and balance sheet developments at U.S. commercial banks in 2004. Federal Reserve Bulletin, Spring.CrossRefGoogle Scholar
Kneip, A. (1994) Nonparametric estimation of common regressors for similar curve data. Annals of Statistics 22, 1386–1427.CrossRefGoogle Scholar
Kneip, A. & Utikal, K.J. (2001) Inference for density families using functional principal component analysis. Journal of the American Statistical Association 96, 519–532.Google Scholar
Maddala, G.S. & Kim, I.M. (1998) Unit Roots, Cointegration and Structural Change. Cambridge University Press.Google Scholar
Mark, N.C. & Sul, D. (2003) Cointegration vector estimation by panel DOLS and long-run money demand. Oxford Bulletin of Economics and Statistics 65, 655–680.CrossRefGoogle Scholar
Meeusen, W. & van den Broeck, J. (1977) Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review 18, 435–444.CrossRefGoogle Scholar
Nelson, C.R. & Plosser, C.I. (1982) Trends and random walks in macroeconomics time series: Some evidence and implications. Journal of Monetary Economics 10, 139–162.Google Scholar
Park, B.U., Sickles, R.C., & Simar, L. (1998) Stochastic frontiers: A semiparametric approach. Journal of Econometrics 84, 273–301.CrossRefGoogle Scholar
Park, B.U., Sickles, R.C., & Simar, L. (2003) Semiparametric efficient estimation of AR(1) panel data models. Journal of Econometrics 117, 279–309.Google Scholar
Park, B.U., Sickles, R.C., & Simar, L. (2007) Semiparametric efficient estimation of dynamic panel data models. Journal of Econometrics 136, 281–301.CrossRefGoogle Scholar
Park, B.U. & Simar, L. (1994) Efficient semiparametric estimation in stochastic frontier models. Journal of the American Statistical Association 89, 929–936.Google Scholar
Phillips, P.C.B. (2010) Two New Zealand pioneer econometricians. New Zealand Economic Papers 44, 1–26.CrossRefGoogle Scholar
Ramsay, J. & Silverman, B. (1997) Functional Data Analysis. Springer-Verlag.Google Scholar
Rao, C.R. (1958) Some statistical methods for the comparison of growth curves. Biometrics 14, 1–17.Google Scholar
Schmidt, P. & Sickles, R.C. (1984) Production frontiers and panel data. Journal of Business and Economic Statistics 2, 367–374.Google Scholar
Sickles, R.C. (2005) Panel estimators and the identification of firm-specific efficiency levels in parametric, semiparametric and nonparametric settings. Journal of Econometrics 126, 305–334.CrossRefGoogle Scholar
Speckman, P. (1988) Kernel smoothing in partial linear models. Journal of the Royal Statistical Society, Series B 50, 413–436.Google Scholar
Stock, J.H. & Watson, M.W. (2002) Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association 97, 1167–1179.CrossRefGoogle Scholar
Stock, J.H. & Watson, M.W. (2005) Implications of Dynamic Factor Models for VAR Analysis. National Bureau of Economic Research Working Paper 11467.Google Scholar
Utreras, F. (1983) Natural spline functions, their associated eigenvalue problem. Numerical Mathematics 42, 107–117.CrossRefGoogle Scholar