Panel Data Models With Interactive Fixed Effects and Multiple Structural Breaks

References

• Ang, J. B. (2007), “CO2 Emissions, Energy Consumption, and Output in France,” Energy Policy, 35, 4772–4778.
   Web of Science ®Google Scholar
• Apergis, N., and Payne, J. E. (2009), “CO2 Emissions, Energy Usage, and Output in Central America,” Energy Policy, 37, 3282–3286.
   Web of Science ®Google Scholar
• Apergis, N. (2010), “The Emissions, Energy Consumption, and Growth Nexus: Evidence From the Common Wealth of Independent States,” Energy Policy, 38, 650–655.
   Web of Science ®Google Scholar
• Arouri, M. H., Youssef, A. B., M’Henni, H., and Rault, C. (2012), “Energy Consumption, Economic Growth and CO2 Emissions in Middle East and North African Countries,” Energy Policy, 45, 342–349.
   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. (2010), “Common Breaks in Means and Variances for Panel Data,” Journal of Econometrics, 157, 78–92.
   Web of Science ®Google Scholar
• Bai, J., and Li, K. (2014), “Theory and Methods of Panel Data Models With Interactive Fixed Effects,” Annals of Statistics, 42, 142–170.
   Web of Science ®Google Scholar
• Bai, J., and Ng, S. (2002), “Determining the Number of Factors in Approximate Factor Models,” Econometrica, 70, 191–221.
   Web of Science ®Google Scholar
• Bai, J., and Perron, P. (1998), “Estimating and Testing Liner Models With Multiple Structural Changes,” Econometrica, 66, 47–78.
   Web of Science ®Google Scholar
• Baltagi, B. H., Feng, Q., and Kao, C. (2016), “Estimation of Heterogeneous Panels With Structural Breaks,” Journal of Econometrics, 191, 176–195.
   Web of Science ®Google Scholar
• Chan, N. H., Yau, C. Y., and Zhang, R. (2014), “Group LASSO for Structural Break Time Series,” Journal of American Statistical Association, 109, 590–599.
   Web of Science ®Google Scholar
• Csörgö, M., and Horváth, L. (1997), Limit Theorems in Change-Point Analysis, Wiley Series in Probability and Statistics. New York: Wiley.
   Google Scholar
• De Watcher, S., and Tzavalis, E. (2012), “Detection of Structural Breaks in Linear Dynamic Panel Data Models,” Computational Statistics and Data Analysis, 56, 3020–3034.
   Web of Science ®Google Scholar
• Fan, J., and Li, R. (2001), “Variable Selection via Nonconcave Penalized Likelihood and Its Oracle Properties,” Journal of American Statistical Association, 96, 1348–1360.
   Web of Science ®Google Scholar
• Fan, J. (2006), “Statistical Challenges With High Dimensionality: Feature Selection in Knowledge Discovery,” in Proceedings of the International Congress of Mathematicians (Vol. III), eds. M. Sanz-Sole, J. Soria, J. L. Varona, and J. Verdera, Zurich: European Mathematical Society, pp. 595–622.
   Google Scholar
• Farhani, S., Mrizak, S., Chaibi, A., and Rault, C. (2014), “The Environmental Kuznets Curve and Sustainability: A Panel Data Analysis,” Energy Policy, 71, 189–198.
   Web of Science ®Google Scholar
• Hallin, M., and Liska, R. (2007), “Determining the Number of Factors in the General Dynamic Factor Model,” Journal of the American Statistical Association, 102, 603–617.
   Web of Science ®Google Scholar
• Harchaoui, Z., and Lévy-Leduc, C. (2010), “Multiple Change-Point Estimation With a Total Variation Penalty,” Journal of the American Statistical Association, 105, 1480–1493.
   Web of Science ®Google Scholar
• Kim, D. (2011), “Estimating a Common Deterministic Time Trend Break in Large Panels With Cross Sectional Dependence,” Journal of Econometrics, 164, 310–330.
   Web of Science ®Google Scholar
• Lean, H. H., and Smyth, R. (2010), “CO2 Emissions, Electricity Consumption and Output in ASEAN,” Applied Energy, 87, 1858–1864.
   Web of Science ®Google Scholar
• Lu, X., and Su, L. (2016), “Shrinkage Estimation of Dynamic Panel Data Models With Interactive Fixed Effects,” Journal of Econometrics, 190, 148–175.
   Web of Science ®Google Scholar
• Moon, H. R., and Weidner, M. (2015a), “Linear Regression for Panel With Unknown Number of Factors as Interactive Fixed Effects,” Econometrica, 83, 1543–1579.
   Web of Science ®Google Scholar
• Moon, H. R. (2015b), “Dynamic Linear Panel Data Regression Models With Interactive Fixed Effects,” Econometric Theory, http://dx.doi.org/10.1017/S0266466615000328.
   PubMed 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.
   Web of Science ®Google Scholar
• Qian, J., and Su, L. (2015), “Shrinkage Estimation of Regression Models With Multiple Structural Changes,” Econometric Theory, http://dx.doi.org/10.1017/S0266466615000237.
   PubMed Web of Science ®Google Scholar
• Qian, J. (2016), “Shrinkage Estimation of Common Breaks in Panel Data Models via Adaptive Group Fused Lasso,” Journal of Econometrics, 191, 86–109.
   Web of Science ®Google Scholar
• Qu, Z., and Perron, P. (2007), “Estimating and Testing Structural Changes in Multiple Regressions,” Econometrica, 75, 459–502.
   Web of Science ®Google Scholar
• Rinaldo, A. (2009), “Properties and Refinement of the Fused Lasso,” Annals of Statistics, 37, 2922–2952.
   Web of Science ®Google Scholar
• Su, L., and Ju, G. (2016), “Identifying Latent Grouped Effects in Panel Data Models with Interactive Fixed Effects,” Working Paper, Singapore Management University.
   Google Scholar
• Tibshirani, R., Saunders, M., Rosset, S., Zhu, J., and Knight, K. (2005), “Sparsity and Smoothness via the Fused Lasso,” Journal of the Royal Statistical Society, Series B, 67, 91–108.
   Google Scholar
• Yuan, M., and Lin, Y. (2006), “Model Selection and Estimation in Regression With Grouped Variables,” Journal of the Royal Statistical Society, Series B, 68, 49–67.
   Google Scholar
• Zou, H. (2006), “The Adaptive Lasso and Its Oracle Properties,” Journal of the American Statistical Association, 101, 1418–1429.
   Web of Science ®Google Scholar