Hostname: page-component-74656f9884-c52kr Total loading time: 0 Render date: 2026-11-08T06:58:04.883Z Has data issue: false hasContentIssue false
  • English
  • Français

TESTING HOMOGENEITY IN PANEL DATA MODELS WITH INTERACTIVE FIXED EFFECTS

Published online by Cambridge University Press:  07 August 2013

Liangjun Su  and
Qihui Chen
Liangjun Su
Affiliation:
Singapore Management University
Qihui Chen
Affiliation:
University of California, San Diego
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

This paper proposes a residual-based Lagrange Multiplier (LM) test for slope homogeneity in large-dimensional panel data models with interactive fixed effects. We first run the panel regression under the null to obtain the restricted residuals and then use them to construct our LM test statistic. We show that after being appropriately centered and scaled, our test statistic is asymptotically normally distributed under the null and a sequence of Pitman local alternatives. The asymptotic distributional theories are established under fairly general conditions that allow for both lagged dependent variables and conditional heteroskedasticity of unknown form by relying on the concept of conditional strong mixing. To improve the finite-sample performance of the test, we also propose a bootstrap procedure to obtain the bootstrap p-values and justify its validity. Monte Carlo simulations suggest that the test has correct size and satisfactory power. We apply our test to study the Organization for Economic Cooperation and Development economic growth model.

Information

Type
ARTICLES
Information
Econometric Theory , Volume 29 , Issue 6 , December 2013 , pp. 1079 - 1135
Copyright
Copyright © Cambridge University Press 2013 

Footnotes

1

The authors gratefully thank the co-editor, Guido Kuersteiner, and three anonymous referees for their many constructive comments on the previous version of the paper. They also express their sincere appreciation to Hashem Pesaran for discussions on the subject matter. They sincerely thank the participants at the 2011 Tsinghua International Conference in Econometrics and the seminar at the University of Adelaide who provided valuable suggestions and discussion. The first author gratefully acknowledges the Singapore Ministry of Education for Academic Research Fund under grant number MOE2012-T2-2-021. correspondence to Liangjun Su, School of Economics, Singapore Management University, 90 Stamford Road, Singapore, 178903; e-mail: ljsu@smu.edu.sg.

References

REFERENCES

Andrews, D.W.K. (2005) Cross-section regression with common shocks. Econometrica 73, 1551–1585.CrossRefGoogle Scholar
Anselin, L. (1988) Spatial Econometric Methods and Models. Kluwer.CrossRefGoogle Scholar
Bai, J. (2009a) Panel data models with interactive fixed effects. Econometrica 77, 1229–1279.Google Scholar
Bai, J. (2009b) Supplement to “Panel data models with interactive fixed effects”: Technical details and proofs. Econometrica Supplementary Material.Google Scholar
Bai, J. & Ng, S. (2002) Determining the number of factors in approximate factor models. Econometrica 70, 191–221.Google Scholar
Bai, J. (2003) Inferential theory for factors models of large dimensions. Econometrica 71, 135–173.CrossRefGoogle Scholar
Bai, J. & Ng, S. (2006) Evaluating latent and observed factors in macroeconomics and finance. Journal of Econometrics 131, 507–537.CrossRefGoogle Scholar
Baltagi, B.H., Bresson, G., & Pirotte, A. (2008) To pool or not to pool? In Mátyás, L. and Sevestre, P. (eds.), The Econometrics of Panel Data, pp. 517–546, Springer-Verlag.CrossRefGoogle Scholar
Bassanini, A. & Scarpetta, S. (2002) Does human capital matter for growth in OECD countries? A pooled mean-group approach. Economics Letter 74, 399–405.CrossRefGoogle Scholar
Bernstein, D.S. (2005) Matrix Mathematics: Theory, Facts, and Formulas with Application to Linear Systems Theory. Princeton University Press.Google Scholar
Blomquist, J. (2010) A Panel Bootstrap Test for Slope Homogeneity. Working paper, Lund University.Google Scholar
Bond, S., Leblebicioglu, A., & Schiantarelli, F. (2010) Capital accumulation and growth: A new look at the empirical evidence. Journal of Applied Econometrics 25, 1073–1099.CrossRefGoogle Scholar
Connor, G. & Korajzcyk, R. (1998) Risk and return in an equilibrium APT application of a new test methodology. Journal of Financial Economics 21, 225–289.Google Scholar
Eberhardt, M. & Teal, F. (2011) Econometrics for grumblers: A new look at the literature on cross-country growth empirics. Journal of Economic Surveys 25, 109–155.CrossRefGoogle Scholar
Fama, E.F. & French, K. (1993) Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 18, 61–90.Google Scholar
Godfrey, L.G. (1988) Misspecification Tests in Econometrics: The Lagrangian Multiplier Principle and Other Approaches. Cambridge University Press.Google Scholar
Gonçalves, S. & Kilian, L. (2004) Bootstrapping autoregressions with conditional heteroskedasticity of unknown form. Journal of Econometrics 123, 89–120.CrossRefGoogle Scholar
Greenaway-McGrevy, R., Han, C., & Sul, D. (2012) Asymptotic distribution of factor augmented estimators for panel regression. Journal of Econometrics 169, 48–53.CrossRefGoogle Scholar
Hahn, J. & Kuersteiner, G. (2011) Reduction for dynamic nonlinear panel models with fixed effects. Econometric Theory 27, 1152–1191.CrossRefGoogle Scholar
Hansen, B.E. (2000) Testing for structural change in conditional models. Journal of Econometrics 97, 93–115.CrossRefGoogle Scholar
Hsiao, C. (2003) Analysis of Panel Data. Cambridge University Press.CrossRefGoogle Scholar
Jin, S. & Su, L. (2013) A nonparametric poolability test for panel data models with cross section dependence. Econometric Reviews 32, 469–512.CrossRefGoogle Scholar
Kapetanios, G. & Pesaran, M.H. (2007) Alternative approaches to estimation and inference in large multifactor panels: Small sample results with an application to modelling of asset return. In Phillips, G. and Tzavalis, E. (eds.), The Refinement of Econometric Estimation and Testing Procedures: Finite Sample and Asymptotic Analysis. Cambridge University Press, pp. 239–281.Google Scholar
Lee, A.J. (1990) U-statistics: Theory and Practice. Marcel Dekker.Google Scholar
Lehmann, B. & Modest, D. (1988) The empirical foundations of the arbitrage pricing theory. Journal of Financial Economics 21, 213–254.CrossRefGoogle Scholar
Lin, C.-C. (2010) Testing for Slope Homogeneity in a Linear Panel Model with Fixed Effects and Conditional Heteroskedasticity. Working paper, Institute of Economics, Academia Sinica.Google Scholar
Moon, H. & Weidner, M. (2010a) Linear Regression for Panel with Unknown Number of Factors as Interactive Fixed Effects. Working paper, University of Southern California.Google Scholar
Moon, H. & Weidner, M. (2010b) Dynamic Linear Panel Regression Models with Interactive Fixed Effects. Working paper, University of Southern California.Google Scholar
Pesaran, H., Smith, R., & Im, K.S. (1996) Dynamic linear models for heterogeneous panels. In Mátyás, L. and Sevestre, P. (eds.), The Econometrics of Panel Data: A Handbook of the Theory with Applications, 2nd rev. ed., pp. 145–195. Kluwer Academic Publishers.CrossRefGoogle Scholar
Pesaran, M.H. (2006) Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica 74, 967–1012.Google Scholar
Pesaran, M.H. & Tosetti, E. (2011) Large panels with common factors and spatial correlation. Journal of Econometrics 161, 182–202.CrossRefGoogle Scholar
Pesaran, M.H. & Yamagata, T. (2008) Testing slope homogeneity in large panels. Journal of Econometrics 142, 50–93.Google Scholar
Phillips, P.C.B. & Sul, D. (2003) Dynamic panel estimation and homogeneity testing under cross section dependence. Econometrics Journal 6, 217–259.Google Scholar
Pollard, D. (1984) Convergence of Stochastic Processes. Springer-Verlag.CrossRefGoogle Scholar
Prakasa Rao, B.L.S. (2009) Conditional independence, conditional mixing and conditional association. Annals of the Institute of Statistical Mathematics 61, 441–460.CrossRefGoogle Scholar
Roussas, G.G. (2008) On conditional independence, mixing and association. Stochastic Analysis and Applications 26, 1274–1309.CrossRefGoogle Scholar
Su, L. & Jin, S. (2012) Sieve estimation of panel data models with cross section dependence. Journal of Econometrics 169, 34–47.CrossRefGoogle Scholar
Su, L. & Ullah, A. (2013) A nonparametric goodness-of-fit-based test for conditional heteroskedasticity. Econometric Theory, 29, 187–212.CrossRefGoogle Scholar
Sun, Y. (2005) Estimation and Inference in Panel Structure Models. Working paper, Department of Economics, University of California, San Diego.CrossRefGoogle Scholar
Zhang, Y., Su, L., & Phillips, P.C.B. (2012) Testing for common trends in semiparametric panel data models with fixed effects. Econometrics Journal 15, 56–100.CrossRefGoogle Scholar
Supplementary material: PDF

Liangjun Su & Qihui Chen Supplementary Material

Supplementary Material

Download Liangjun Su & Qihui Chen Supplementary Material(PDF)
PDF 339.6 KB