Empirical Economics

, Volume 50, Issue 4, pp 1359–1381 | Cite as

Panel bootstrap tests of slope homogeneity

Article

Abstract

This paper proposes two bootstrap-based tests that can be used to infer whether the individual slopes in a panel regression model are homogenous. The first test is suitable when wanting to infer the null of homogeneity versus the general alternative, while the second is suitable when wanting to infer the units of the panel that can be pooled. Both approaches are shown to be asymptotically valid, a property that is verified in small samples using Monte Carlo simulation.

Keywords

Panel data Slope homogeneity test Block bootstrap 

JEL Classification

C12 C13 C33 

References

  1. Andrews DWK (1991) Heteroskedasticity and autocorrelation consistent covariance matrix estimation. Econometrica 59:817–858CrossRefGoogle Scholar
  2. Anselin L, Le Gallo J, Jayet H (2008) Spatial panel econometrics. In: Mátyás L, Sevestre P (eds) The econometrics of panel data. Springer, BerlinGoogle Scholar
  3. Baltagi BH (2008) Econometric analysis of panel data. Wiley, ChichesterGoogle Scholar
  4. Baltagi BH, Bresson G, Pirotte A (2008) To pool or not to pool? In: Mátyás L, Sevestre P (eds) The econometrics of panel data. Springer, BerlinGoogle Scholar
  5. Bun MJG (2004) Testing poolability in a system of dynamic regressions with nonspherical disturbances. Empir Econ 29:89–106CrossRefGoogle Scholar
  6. Chudik A, Pesaran MH, Tosetti E (2011) Weak and strong cross-section dependence and estimation of large panels. Econom J 14:C45–C90CrossRefGoogle Scholar
  7. Davidson J (1994) Stochastic limit theory. Oxford University Press, OxfordCrossRefGoogle Scholar
  8. Davidson R, MacKinnon JG (1999) The size distortion of bootstrap tests. Econom Theory 15:361–376CrossRefGoogle Scholar
  9. Fitzenberger B (1997) The moving blocks bootstrap and robust inference for linear least squares and quantile regressions. J Econom 82:235–287CrossRefGoogle Scholar
  10. Freedman DA (1981) Bootstrapping regression models. Ann Stat 9:1218–1228CrossRefGoogle Scholar
  11. Gonçalves S (2011) The moving blocks bootstrap for panel linear regression models with individual fixed effects. Econom Theory 27:1048–1082CrossRefGoogle Scholar
  12. Gonçalves S, White H (2005) Bootstrap standard error estimates for linear regression. J Am Stat Assoc 100:970–979CrossRefGoogle Scholar
  13. Hall P, Horowitz JL, Jing B-Y (1995) On blocking rules for the bootstrap with dependent data. Biometrika 82:561–574CrossRefGoogle Scholar
  14. Hidalgo J (2003) An alternative bootstrap to moving blocks for time series regression models. J Econom 117:369–399CrossRefGoogle Scholar
  15. Hsiao C (2003) Analysis of panel data. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  16. Hsiao C, Pesaran MH (2008) Random coefficient panel data models. In: Mátyás L, Sevestre P (eds) The econometrics of panel data. Springer, BerlinGoogle Scholar
  17. Kapetanios G (2003) Determining the poolability properties of individual series in panel datasets. Queen Mary, University of London Working Paper No. 499Google Scholar
  18. Kapetanios G (2006) Cluster analysis of panel data sets using non-standard optimisation of information criteria. J Econ Dyn Control 30:1389–1408CrossRefGoogle Scholar
  19. Kapetanios G (2008) A bootstrap procedure for panel data sets with many cross-sectional units. Econom J 11:377–395CrossRefGoogle Scholar
  20. Lahiri SN (2002) On the jackknife-after-bootstrap method for dependent data and its consistency properties. Econom Theory 18:79–98CrossRefGoogle Scholar
  21. Lahiri SN, Furukawa K, Lee Y-D (2007) A nonparametric plug-in rule for selecting optimal block lengths for block bootstrap methods. Stat Methodol 4:292–321CrossRefGoogle Scholar
  22. Lehmann EL, Romano JP (2005) Testing statistical hypotheses. Springer, New YorkGoogle Scholar
  23. Lin C-C, Ng S (2012) Estimation of panel data models with parameter heterogeneity when group membership is unknown. J Econom Methods 1:42–55CrossRefGoogle Scholar
  24. MacKinnon JG (2007) Bootstrap hypothesis testing. Queen’s economics department Working Paper No. 1127Google Scholar
  25. Mathai AM, Provost SB (1992) Quadratic forms in random variables. Marcel Dekker, New YorkGoogle Scholar
  26. Newey WK, West KD (1994) Automatic lag selection in covariance matrix estimation. Rev of Econ Stud 61:613–653CrossRefGoogle Scholar
  27. Pesaran MH, Tosetti E (2010) Large panels with common factors and spatial correlations. J Econom 161:182–202CrossRefGoogle Scholar
  28. Pesaran MH, Yamagata T (2008) Testing slope homogeneity in large panels. J Econom 142:50–93CrossRefGoogle Scholar
  29. Pesaran MH, Chudik A (2013) Econometric analysis of high dimensional VARs featuring a dominant unit. Econom Rev 32:592–649CrossRefGoogle Scholar
  30. Pesaran MH, Smith R, Im KS (1996) Dynamic linear models for heterogeneous panels. In: Mátyás L, Sevestre P (eds) The econometrics of panel data. Springer, BerlinGoogle Scholar
  31. Phillips PCB, Sul D (2003) Dynamic panel estimation and homogeneity testing under cross section dependence. Econom J 6:217–259CrossRefGoogle Scholar
  32. Smeekes S (2015) Bootstrap sequential tests to determine the stationary units in a panel. J Time Ser Anal (forthcoming)Google Scholar
  33. White H (2000) A reality check for data snooping. Econometrica 68:1097–1126CrossRefGoogle Scholar
  34. White H (2001) Asymptotic theory for econometricians. Academic Press, New YorkGoogle Scholar
  35. Zhou Z, Shao X (2013) Inference for linear models with dependent errors. J R Stat Soc Ser B 75:323–343CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.AgriFood Economics Center, Department of EconomicsSwedish University of Agricultural SciencesUppsalaSweden
  2. 2.Department of EconomicsLund UniversityLundSweden
  3. 3.Financial Econometrics Group, Centre for Research in Economics and Financial EconometricsDeakin UniversityGeelongAustralia

Personalised recommendations