An LM Test for the Conditional Independence between Regressors and Factor Loadings in Panel Data Models with Interactive Effects

References

• Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W., and Wager, S. (2021), “Synthetic Difference in Differences,” American Economic Review, 111, 4088–4118. DOI: 10.1257/aer.20190159.
   Web of Science ®Google Scholar
• Athey, S., and Imbens, G. W. (2022), “Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption,” Journal of Econometrics, 226, 62–79. DOI: 10.1016/j.jeconom.2020.10.012.
   Web of Science ®Google Scholar
• Bai, J. (2009), “Panel Data Models with Interactive Fixed Effects,” Econometrica, 77, 1229–1279.
   Web of Science ®Google Scholar
• Bai, J., and Ng, S. (2002), “Determining the Number of Factors in Approximate Factor Models,” Econometrica, 70, 191–221. DOI: 10.1111/1468-0262.00273.
   Web of Science ®Google Scholar
• Baltagi, B. H., Lagravinese, R., Moscone, F., and Tosetti, E. (2017), “Health Care Expenditure and Income: A Global Perspective,” Health Economics, 26, 863–874. DOI: 10.1002/hec.3424.
   PubMed Web of Science ®Google Scholar
• Baltagi, B. H., and Li, J. (2014), “Further Evidence on the Spatio-Tempral Model of House Prices in the United States,” Journal of Applied Econometrics, 29, 515–522. DOI: 10.1002/jae.2372.
   Web of Science ®Google Scholar
• Cameron, A. C., Gelbach, J. B., and Miller, D. L. (2011), “Robust Inference with Multiway Clustering,” Journal of Business & Economic Statistics, 29, 238–249. DOI: 10.1198/jbes.2010.07136.
   Web of Science ®Google Scholar
• Castagnetti, C., Rossi, E., and Trapani, L. (2015), “Testing for no Factor Structures: On the Use of Hausman-Type Statistics,” Economics Letters, 130, 66–68. DOI: 10.1016/j.econlet.2015.02.030.
   Web of Science ®Google Scholar
• Charbonneau, K. B. (2017), “Multiple Fixed Effects in Binary Response Panel Data Models,” Econometrics Journal, 20, S1–S13. DOI: 10.1111/ectj.12093.
   Web of Science ®Google Scholar
• Choi, I., Lin, R., and Shin, Y. (2021), “Canonical Correlation-based Model Selection for the Multilevel Factors,” Journal of Econometrics, 233, 22–44. DOI: 10.1016/j.jeconom.2021.09.008.
   Web of Science ®Google Scholar
• Chudik, A., Mohaddes, K., Pesaran, M. H., and Raissi, M. (2017), “Is There a Debt-Threshold Effect on Output Growth?” The Review of Economics and Statistics, 99, 135–150. DOI: 10.1162/REST_a_00593.
   Web of Science ®Google Scholar
• Chudik, A., and Pesaran, M. H. (2015), “Common Correlated Effects Estimation of Heterogeneous Dynamic Panel Data Models with Weakly Exogenous Regressors,” Journal of Econometrics, 188, 393–420. DOI: 10.1016/j.jeconom.2015.03.007.
   Web of Science ®Google Scholar
• Coakley, J., Fuertes, A.-M., and Smith, R. (2006), “Unobserved Heterogeneity in Panel Time Series Models,” Computational Statistics and Data Analysis, 50, 2361–2380. DOI: 10.1016/j.csda.2004.12.015.
   Web of Science ®Google Scholar
• Coe, D. T., Helpman, E., and Hoffmaister, A. W. (2009), “International R&D Spillovers and Institutions,” European Economic Review, 53, 723–741. DOI: 10.1016/j.euroecorev.2009.02.005.
   Web of Science ®Google Scholar
• Cornwell, C., and Rupert, P. (1988), “Efficient Estimation with Panel Data: An Empirical Comparison of Instrumental Variables Estimators,” Journal of Applied Econometrics, 3, 149–155. DOI: 10.1002/jae.3950030206.
   Web of Science ®Google Scholar
• Cui, G., Hayakawa, K., Nagata, S., and Yamagata, T. (2023), “A Robust Approach to Heteroskedasticity, Error Serial Correlation and Slope Heterogeneity in Linear Models with Interactive Effects for Large Panel Data,” Journal of Business & Economic Statistics, 41, 862–875. DOI: 10.1080/07350015.2022.2077349.
   Web of Science ®Google Scholar
• Cui, G., Sarafidis, V., and Yamagata, T. (2022), “IV Estimation of Spatial Dynamic Panels with Interactive Effects: Large Sample Theory and an Application on Bank Attitude towards Risk,” The Econometrics Journal, 226, 62–79. DOI: 10.1093/ectj/utac026.
   Google Scholar
• Dell, M., Jones, B. F., and Olken, B. A. (2012), “Temperature Shocks and Economic Growth: Evidence from the Last Half Century,” American Economic Journal: Macroeconomics, 4, 66–95. DOI: 10.1257/mac.4.3.66.
   Web of Science ®Google Scholar
• Ditzen, J. (2018), “Estimating Dynamic Common-Correlated Effects in Stata,” The Stata Journal, 18, 585–617. DOI: 10.1177/1536867X1801800306.
   Web of Science ®Google Scholar
• Eberhardt, M., Helmers, C., and Strauss, H. (2013), “Do Spillovers Matter When Estimating Private Returns to R&D?” The Review of Economics and Statistics, 95, 436–448. DOI: 10.1162/REST_a_00272.
   Web of Science ®Google Scholar
• Eberhardt, M., and Teal, F. (2020), “The Magnitude of the Task Ahead: Macro Implications of Heterogeneous Technology,” Review of Income and Wealth, 66, 334–360. DOI: 10.1111/roiw.12415.
   Web of Science ®Google Scholar
• Ertur, C., and Musolesi, A. (2017), “Weak and Strong Cross-Sectional Dependence: A Panel Data Analysis of International Technology Diffusion,” Journal of Applied Econometrics, 32, 477–503. DOI: 10.1002/jae.2538.
   Web of Science ®Google Scholar
• Everaert, G., and Vierke, H. (2016), “Demographics and Business Cycle Volatility: A Spurious Relationship?” Journal of Applied Econometrics, 31, 1467–1477. DOI: 10.1002/jae.2519.
   Web of Science ®Google Scholar
• Fernandez-Val, I., and Weidner, M. (2016), “Individual and Time Effects in Nonlinear Panel Models with Large N, T,” Journal of Econometrics, 192, 291–312. DOI: 10.1016/j.jeconom.2015.12.014.
   Web of Science ®Google Scholar
• Godfrey, L. G. (1989), “Misspecification Tests in Econometrics: The Lagrange Multiplier Principle and Other Approaches. Econometric Society Monographs. Cambridge: Cambridge University Press.
   Google Scholar
• Greene, W. (2004), “Distinguishing between Heterogeneity and Inefficiency: Stochastic Frontier Analysis of the World Health Organization’s Panel Data on National Health Care Systems,” Health Economics, 13, 959–980. DOI: 10.1002/hec.938.
   PubMed Web of Science ®Google Scholar
• Hannan, E. J., and Deistler, M. (1989), “The Statistical Theory of Linear Systems,” Statistical Papers, 30, 239–242.
   Google Scholar
• Holly, S., Pesaran, M. H., and Yamagata, T. (2010), “A Spatio-Temporal Model of House Prices in the USA,” Journal of Econometrics, 158, 160–173. DOI: 10.1016/j.jeconom.2010.03.040.
   Web of Science ®Google Scholar
• Hsiao, C. (2018), “Panel Models with Interactive Effects,” Journal of Econometrics, 206, 645–673. DOI: 10.1016/j.jeconom.2018.06.017.
   Web of Science ®Google Scholar
• Islam, N. (1995), “Growth Empirics: A Panel Data Approach,” The Quarterly Journal of Economics, 110, 1127–1170. DOI: 10.2307/2946651.
   Web of Science ®Google Scholar
• Juodis, A. (2022), “A Regularization Approach to Common Correlated Effects Estimation,” Journal of Applied Econometrics, 37, 788–810. DOI: 10.1002/jae.2899.
   Web of Science ®Google Scholar
• Juodis, A., and Reese, S. (2021), “The Incidental Parameters Problem in Testing for Remaining Cross-section Correlation,” Journal of Business & Economic Statistics, 40, 1191–1203. DOI: 10.1080/07350015.2021.1906687.
   Web of Science ®Google Scholar
• Kapetanios, G., Pesaran, M. H., and Yamagata, T. (2011), “Panels with Non-stationary Multifactor Error Structures,” Journal of Econometrics, 160, 326–348. DOI: 10.1016/j.jeconom.2010.10.001.
   Web of Science ®Google Scholar
• Kapetanios, G., Serlenga, L., and Shin, Y. (2023), “Estimation and Inference for Multi-Dimensional Heterogeneous Panel Datasets with Hierarchical Multi-Factor Error Structure,” Journal of Econometrics, 220, 504–531. DOI: 10.1016/j.jeconom.2020.04.011.
   Web of Science ®Google Scholar
• ———(2023), “Testing for Correlation between the Regressors and Factor Loadings in Heterogeneous Panels with Interactive Effects,” Empirical Economics, 64, 2611–2659.
   Web of Science ®Google Scholar
• Karabiyik, H., Reese, S., and Westerlund, J. (2017), “On the Role of the Rank Condition in CCE Estimation of Factor-Augmented Panel Regressions,” Journal of Econometrics, 197, 60–64. DOI: 10.1016/j.jeconom.2016.10.006.
   Web of Science ®Google Scholar
• Kim, D.-H., Chen, T.-C., and Lin, S.-C. (2020), “Does Oil Drive Income Inequality? New Panel Evidence,” Structural Change and Economic Dynamics, 55, 137–152. DOI: 10.1016/j.strueco.2020.08.002.
   Web of Science ®Google Scholar
• Kripfganz, S., and Sarafidis, V. (2021), “Instrumental-Variable Estimation of Large-t Panel-Data Models with Common Factors,” Stata Journal, 21, 659–686. DOI: 10.1177/1536867X211045558.
   Web of Science ®Google Scholar
• Lewis, R. A., and Reinsel, G. C. (1988), “Prediction Error of Multivariate Time Series with Mis-specified Models,” Journal of Time Series Analysis, 9, 43–57. DOI: 10.1111/j.1467-9892.1988.tb00452.x.
   Google Scholar
• Liddle, B. (2018), “Consumption-based Accounting and the Trade-Carbon Emissions Nexus,” Energy Economics, 69, 71–78. DOI: 10.1016/j.eneco.2017.11.004.
   Web of Science ®Google Scholar
• Liu, W. (2014), “Modeling Gasoline Demand in the United States: A Flexible Semiparametric Approach,” Energy Economics, 45, 244–253. DOI: 10.1016/j.eneco.2014.07.004.
   Web of Science ®Google Scholar
• Ma, B. (2015), “Does Urbanization Affect Energy Intensities across Provinces in China? Long-Run Elasticities Estimation Using Dynamic Panels with Heterogeneous Slopes,” Energy Economics, 49, 390–401. DOI: 10.1016/j.eneco.2015.03.012.
   Web of Science ®Google Scholar
• Mastromarco, C., Serlenga, L., and Shin, Y. (2016), “Modelling Technical Efficiency in Cross Sectionally Dependent Stochastic Frontier Panels,” Journal of Applied Econometrics, 31, 281–297. DOI: 10.1002/jae.2439.
   Web of Science ®Google Scholar
• Moon, H. R., and Weidner, M. (2015), “Linear Regression for Panel with Unknown Number of Factors as Interactive Fixed Effects,” Econometrica, 83, 1543–1579. DOI: 10.3982/ECTA9382.
   Web of Science ®Google Scholar
• ———(2017), “Dynamic Linear Panel Regression Models with Interactive Fixed Effects,” Econometric Theory, 33, 158–195.
   Web of Science ®Google Scholar
• Munnell, A. (1990), “How Does Public Infrastructure Affect Regional Economic Performance,” New England Economic Review, 34, 11–33.
   Google Scholar
• Norkute, M., Sarafidis, V., Yamagata, T., and Cui, G. (2021), “Instrumental Variable Estimation of Dynamic Linear Panel Data Models with Defactored Regressors and a Multifactor Error Structure,” Journal of Econometrics, 220, 416–446. DOI: 10.1016/j.jeconom.2020.04.008.
   Web of Science ®Google Scholar
• Pesaran, M., and Smith, R. (1995), “Estimating Long-Run Relationships from Dynamic Heterogeneous Panels,” Journal of Econometrics, 68, 79–113. DOI: 10.1016/0304-4076(94)01644-F.
   Web of Science ®Google Scholar
• Pesaran, M. H. (2006), “Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure,” Econometrica, 74, 967–1012. DOI: 10.1111/j.1468-0262.2006.00692.x.
   Web of Science ®Google Scholar
• ———(2015), “Testing Weak Cross-Sectional Dependence in Large Panels,” Econometric Reviews, 34, 1089–1117.
   Web of Science ®Google Scholar
• ———(2021), “General Diagnostic Tests for Cross-Sectional Dependence in Panels,” Empirical Economics, 60, 13–50.
   Web of Science ®Google Scholar
• Pesaran, M. H., and Xie, Y. (2021), “A Bias-Corrected CD Test for Error Cross-Sectional Dependence in Panel Data Models with Latent Factors,” arXiv Working Paper 2109.00408.
   Google Scholar
• Pieri, F., Vecchi, M., and Venturini, F. (2018), “Modelling the Joint Impact of R&D and ICT on Productivity: A Frontier Analysis Approach,” Research Policy, 47, 1842–1852. DOI: 10.1016/j.respol.2018.06.013.
   Web of Science ®Google Scholar
• Sarafidis, V., and Wansbeek, T. (2012), “Cross-Sectional Dependence in Panel Data Analysis,” Econometric Reviews, 31, 483–531. DOI: 10.1080/07474938.2011.611458.
   Web of Science ®Google Scholar
• Sarafidis, V., Yamagata, T., and Robertson, D. (2009), “A Test of Cross Section Dependence for a Linear Dynamic Panel Model with Regressors,” Journal of Econometrics, 148, 149–161. DOI: 10.1016/j.jeconom.2008.10.006.
   Web of Science ®Google Scholar
• Serlenga, L., and Shin, Y. (2007), “Gravity Models of Intra-EU Trade: Application of the CCEP-HT Estimation in Heterogeneous Panels with Unobserved Common Time-Specific Factors,” Journal of Applied Econometrics, 22, 361–381. DOI: 10.1002/jae.944.
   Web of Science ®Google Scholar
• Song, M. (2013), “Asymptotic Theory for Dynamic Heterogeneous Panels with Cross-sectional Dependence and Its Applications,” Mimeo, Columbia University.
   Google Scholar
• Vos, I. D., and Everaert, G. (2021), “Bias-Corrected Common Correlated Effects Pooled Estimation in Dynamic Panels,” Journal of Business & Economic Statistics, 39, 294–306. DOI: 10.1080/07350015.2019.1654879.
   Web of Science ®Google Scholar
• Westerlund, J. (2019a), “On Estimation and Inference in Heterogeneous Panel Regressions with Interactive Effects,” Journal of Time Series Analysis, 40, 852–857. DOI: 10.1111/jtsa.12432.
   Web of Science ®Google Scholar
• ———(2019b), “Testing Additive versus Interactive Effects in Fixed-T Panels,” Economics Letters, 174, 5–8.
   Web of Science ®Google Scholar
• Westerlund, J., and Urbain, J. P. (2013), “On the Estimation and Inference in Factor-Augmented Panel Regressions with Correlated Loadings,” Economics Letters, 119, 247–250. DOI: 10.1016/j.econlet.2013.03.022.
   Web of Science ®Google Scholar
• ———(2015), “Cross-Sectional Averages versus Principal Components,” Journal of Econometrics, 185(2):372–377.
   Web of Science ®Google Scholar
• Winkelmann, R. (2004), “Health Care Reform and the Number of Doctor Visits: An Econometric Analysis,” Journal of Applied Econometrics, 19, 455–472. DOI: 10.1002/jae.764.
   Web of Science ®Google Scholar