A robust test for serial correlation in panel data models

References

• Arellano, M. (2003). Panel Data Econometrics. Oxford: Oxford University Press.
   Google Scholar
• Arellano, M., Bond, S. (1991). Some tests of specification for panel data: Monte carlo evidence and an application to employment equations. The Review of Economic Studies 58(2):277–297. doi:10.2307/2297968
   Web of Science ®Google Scholar
• Bai, J. (2003). Inferential theory for factor models of large dimensions. Econometrica 71(1):135–171. doi:10.1111/1468-0262.00392
   Web of Science ®Google Scholar
• Baltagi, B. H. (2021). Econometric Analysis of Panel Data. New York: Springer International Publishing.
   Google Scholar
• Baltagi, B. H., Jung, B. C., Song, S. H. (2010). Testing for heteroskedasticity and serial correlation in a random effects panel data model. Journal of Econometrics 154(2):122–124. doi:10.1016/j.jeconom.2009.04.009
   Web of Science ®Google Scholar
• Baltagi, B. H., Li, Q. (1995). Testing AR(1) against MA(1) disturbances in an error component model. Journal of Econometrics 68(1):133–151. doi:10.1016/0304-4076(94)01646-H
   Web of Science ®Google Scholar
• Baltagi, B. H., Song, S. H., Jung, B. C., Koh, W. (2007). Testing for serial correlation, spatial autocorrelation and random effects using panel data. Journal of Econometrics 140(1):5–51. doi:10.1016/j.jeconom.2006.09.001
   Web of Science ®Google Scholar
• BeltraTo, K. I., Bloomfield, P. (1987). Determining the bandwidth of a kernel spectrum estimate. Journal of Time Series Analysis 8(1):21–38. doi:10.1111/j.1467-9892.1987.tb00418.x
   Google Scholar
• Bera, A. K., Sosa-Escudero, W., Yoon, M. (2001). Tests for the error component model in the presence of local misspecification. Journal of Econometrics 101(1):1–23. doi:10.1016/S0304-4076(00)00071-3
   Web of Science ®Google Scholar
• Bertrand, M., Duflo, E., Mullainathan, S. (2004). How much should We trust differences-in-differences estimates? The Quarterly Journal of Economics 119(1):249–275. doi:10.1162/003355304772839588
   Web of Science ®Google Scholar
• Bhargava, A., Franzini, L., Narendranathan, W. (1982). Serial correlation and fixed effects model. The Review of Economic Studies 49(4):533–549. doi:10.2307/2297285
   Web of Science ®Google Scholar
• Born, B., Breitung, J. (2016). Testing for serial correlation in Fixed-Effects panel data models. Econometric Reviews 35(7):1290–1316. doi:10.1080/07474938.2014.976524
   Web of Science ®Google Scholar
• Box, G. E. P., Pierce, D. A. (1970). Distribution of residual autocorrelations in autoregressive integrated moving average time series models. Journal of the American Statistical Association 65(332):1509–1526. doi:10.1080/01621459.1970.10481180
   Google Scholar
• Brown, B. M. (1971). Martingale limit theorems. The Annals of Mathematical Statistics 42(1):59–66. doi:10.1214/aoms/1177693494
   Google Scholar
• Buhlmann, P. (1996). Locally adaptive lag-window spectral estimation. Journal of Time Series Analysis 17(3):247–270.
   Google Scholar
• Chen, B., Hong, Y. (2011). Generalized spectral testing for multivariate continuous-time models. Journal of Econometrics 164(2):268–293. doi:10.1016/j.jeconom.2011.06.001
   Web of Science ®Google Scholar
• Chen, J., Gao, J., Li, D. (2012). A new diagnostic test for cross-section uncorrelatedness in nonparametric panel data models. Econometric Theory 28(5):1144–1163. doi:10.1017/S0266466612000072
   Web of Science ®Google Scholar
• Choi, I. (1999). Testing the random walk hypothesis for real exchange rates. Journal of Applied Econometrics 14(3):293–308. doi:10.1002/(SICI)1099-1255(199905/06)14:3<293::AID-JAE503>3.0.CO;2-5
   Web of Science ®Google Scholar
• Chudik, A., Pesaran, M. H., Tosetti, E. (2011). Weak and strong cross-section dependence and estimation of large panels. Econometrics Journal 14:45–90.
   Web of Science ®Google Scholar
• Deo, C. (1975). A functional Central limit theorem for stationary random fields. The Annals of Probability 3(4):708–715. doi:10.1214/aop/1176996310
   Web of Science ®Google Scholar
• Driscoll, J., Kraay, A. (1998). Consistent covariance matrix estimation with spatially dependent panel data. Review of Economics and Statistics 80(4):549–560. doi:10.1162/003465398557825
   Web of Science ®Google Scholar
• Durbin, J. (1973). Distribution theory for tests based on the sample distribution function. Society for Industrial and Applied Mathematics 9, 1–64.
   Google Scholar
• Durlauf, S. (1991). Spectral based testing of the martingale hypothesis. Journal of Econometrics 50(3):355–376. doi:10.1016/0304-4076(91)90025-9
   Web of Science ®Google Scholar
• Escanciano, J. C. (2006). Goodness-of-fit tests for linear and nonlinear time series models. Journal of the American Statistical Association 101(474):531–541. doi:10.1198/016214505000001050
   Web of Science ®Google Scholar
• Escanciano, J. C., Lobato, I. (2009). An automatic portmanteau test for serial correlation. Journal of Econometrics 151(2):140–149. doi:10.1016/j.jeconom.2009.03.001
   Web of Science ®Google Scholar
• Fan, J., Yao, Q. (2003). Nonlinear Time Series: Nonparametric and Parametric Methods. New York: SpringerVerlag.
   Google Scholar
• Franke, J., Härdle, W. (1992). On bootstrapping kernel spectral estimates. Annnals of Statistics 20:1, 121–45.
   Web of Science ®Google Scholar
• Gao, J. (2007). Nonlinear Time Series: Semiparametric and Nonparametric Methods. London: Chapman and Hall.
   Google Scholar
• Goncalves, S. (2011). The moving blocks bootstrap for panel linear regression models with individual fixed-effects. Econometric Theory 27:1048–1082.
   Web of Science ®Google 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(4):1639–1657. doi:10.1111/1468-0262.00344
   Web of Science ®Google Scholar
• Hansen, C. (2007). Asymptotic properties of a robust variance matrix estimator for panel data when T is large. Journal of Econometrics 141(2):597–620. doi:10.1016/j.jeconom.2006.10.009
   Web of Science ®Google Scholar
• Holtz-Eakin, D. (1988). Testing for individual effects in autoregressive models. Journal of Econometrics 39(3):297–307. doi:10.1016/0304-4076(88)90060-7
   Web of Science ®Google Scholar
• Hong, Y. (1996). Consistent testing for serial correlation of unknown form. Econometrica 64(4):837–864. doi:10.2307/2171847
   Web of Science ®Google Scholar
• Hong, Y. (1999). Hypothesis testing in time series via the empirical characteristic function: a generalized spectral density approach. Journal of the American Statistical Association 94(448):1201–1220. doi:10.1080/01621459.1999.10473874
   Web of Science ®Google Scholar
• Hong, Y., Kao, C. W. (2004). Wavelet-based testing for serial correlation of unknown form in panel models. Econometrica 72(5):1519–1563. doi:10.1111/j.1468-0262.2004.00542.x
   Web of Science ®Google Scholar
• Hsiao, C. (2007). Panel data analysis advantages and challenges. Test 16(1):1–22. doi:10.1007/s11749-007-0046-x
   Web of Science ®Google Scholar
• Hsiao, C. (2014). Analysis of Panel Data. Cambridge: Cambridge University Press.
   Google Scholar
• Hurvich, C. M. (1985). Data-driven choice of a spectrum estimate: Extending the applicability of cross-validation methods. Journal of the American Statistical Association 80(392):933–940. doi:10.1080/01621459.1985.10478207
   Web of Science ®Google Scholar
• Lee, Y. J. (2014). Testing a linear dynamic panel data model against nonlinear alternatives. Journal of Econometrics 178:146–166. doi:10.1016/j.jeconom.2013.08.013
   Web of Science ®Google Scholar
• Liu, R. Y. (1988). Bootstrap procedures under some non i.i.d. Models, Annals of Statistics 16:1696–1708.
   Web of Science ®Google Scholar
• Ljung, G. M., Box, G. E. P. (1978). On a measure of lack of fit in time series models. Biometrika 65(2):297–303. doi:10.1093/biomet/65.2.297
   Web of Science ®Google Scholar
• Lobato, I., Nankervis, J., Savin, N. (2002). Testing for zero autocorrelation in the presence of statistical dependence. Econometric Theory 18(3):730–743. doi:10.1017/S0266466602183083
   Web of Science ®Google Scholar
• Mammen, E. (1993). Bootstrap and wild bootstrap for high dimensional linear models. Annals of Statistics 21:255–285.
   Web of Science ®Google Scholar
• Newey, W. K., West, K. D. (1987). A simple, positive Semi-Definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica 55(3):703–708. doi:10.2307/1913610
   Web of Science ®Google Scholar
• Parzen, E. (1957). On consistent estimates of the spectrum of a stationary time series. The Annals of Mathematical Statistics 28(2):329–348. doi:10.1214/aoms/1177706962
   Google Scholar
• Petersen, M. A. (2009). Estimating standard errors in finance panel data sets: comparing approaches. Review of Financial Studies 22(1):435–480. doi:10.1093/rfs/hhn053
   Web of Science ®Google Scholar
• Phillips, P. C. B., Moon, H. (1999). Linear regression limit theory for nonstationary panel data. Econometrica 67(5):1057–1111. doi:10.1111/1468-0262.00070
   Web of Science ®Google Scholar
• Sosa-Escudero, W., Bera, A. K. (2008). Tests for unbalanced error-components models under local misspecification. The Stata Journal: Promoting Communications on Statistics and Stata 8(1):68–78. doi:10.1177/1536867X0800800105
   Google Scholar
• Thompson, S. (2011). Simple formulas for standard errors that cluster by both firm and time. Journal of Financial Economics 99(1):1–10. doi:10.1016/j.jfineco.2010.08.016
   Web of Science ®Google Scholar
• Vogelsang, T. (2012). Heteroskedasticity, autocorrelation, and spatial correlation robust inference in linear panel models with fixed-effects. Journal of Econometrics 166(2):303–319. doi:10.1016/j.jeconom.2011.10.001
   Web of Science ®Google Scholar
• Wolfers, J. (2006). Did unilateral divorce laws raise divorce rates? a reconciliation and new results. American Economic Review 96(5):1802–1820. doi:10.1257/aer.96.5.1802
   Web of Science ®Google Scholar
• Yamagata, T. (2008). A joint serial correlation test for linear panel data models. Journal of Econometrics 146(1):135–145. doi:10.1016/j.jeconom.2008.08.005
   Web of Science ®Google Scholar