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Using information criteria to select averages in CCE

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  • Luca Margaritella
  • Joakim Westerlund

Abstract

SummaryIn the interactive effects panel data literature information criteria are commonly used to consistently determine which of the estimated principal components factors to include. The present paper shows that the same approach can be applied to factors estimated by taking the cross-sectional averages of the observables, as prescribed by the popular common correlated effects (CCE) approach. This should be useful to practitioners because at the moment there is no other theory that justifies the use of information criteria in CCE.

Suggested Citation

  • Luca Margaritella & Joakim Westerlund, 2023. "Using information criteria to select averages in CCE," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 405-421.
    • Handle: RePEc:oup:emjrnl:v:26:y:2023:i:3:p:405-421.
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    1. Markus Eberhardt & Christian Helmers & Hubert Strauss, 2013. "Do Spillovers Matter When Estimating Private Returns to R&D?," The Review of Economics and Statistics, MIT Press, vol. 95(2), pages 436-448, May.
    2. Bertoli, Simone & Fernández-Huertas Moraga, Jesús, 2013. "Multilateral resistance to migration," Journal of Development Economics, Elsevier, vol. 102(C), pages 79-100.
    3. Greenaway-McGrevy, Ryan & Han, Chirok & Sul, Donggyu, 2012. "Estimating the number of common factors in serially dependent approximate factor models," Economics Letters, Elsevier, vol. 116(3), pages 531-534.
    4. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    5. Michael W. McCracken & Serena Ng, 2016. "FRED-MD: A Monthly Database for Macroeconomic Research," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 574-589, October.
    6. In Choi & Hanbat Jeong, 2019. "Model selection for factor analysis: Some new criteria and performance comparisons," Econometric Reviews, Taylor & Francis Journals, vol. 38(6), pages 577-596, July.
    7. Simon Reese & Joakim Westerlund, 2016. "Panicca: Panic on Cross‐Section Averages," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(6), pages 961-981, September.
    8. M. Hashem Pesaran, 2021. "General diagnostic tests for cross-sectional dependence in panels," Empirical Economics, Springer, vol. 60(1), pages 13-50, January.
    9. Seung C. Ahn & Alex R. Horenstein, 2013. "Eigenvalue Ratio Test for the Number of Factors," Econometrica, Econometric Society, vol. 81(3), pages 1203-1227, May.
    10. Alexei Onatski, 2009. "Testing Hypotheses About the Number of Factors in Large Factor Models," Econometrica, Econometric Society, vol. 77(5), pages 1447-1479, September.
    11. Kapetanios, George, 2010. "A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(3), pages 397-409.
    12. Donald W. K. Andrews, 1999. "Consistent Moment Selection Procedures for Generalized Method of Moments Estimation," Econometrica, Econometric Society, vol. 67(3), pages 543-564, May.
    13. Alexander Chudik & M. Hashem Pesaran & Elisa Tosetti, 2011. "Weak and strong cross‐section dependence and estimation of large panels," Econometrics Journal, Royal Economic Society, vol. 14(1), pages 45-90, February.
    14. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    15. Sydney C. Ludvigson & Serena Ng, 2009. "Macro Factors in Bond Risk Premia," The Review of Financial Studies, Society for Financial Studies, vol. 22(12), pages 5027-5067, December.
    16. Jostein Paulsen, 1984. "Order Determination Of Multivariate Autoregressive Time Series With Unit Roots," Journal of Time Series Analysis, Wiley Blackwell, vol. 5(2), pages 115-127, March.
    17. Emanuel Moench & Serena Ng & Simon Potter, 2013. "Dynamic Hierarchical Factor Model," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1811-1817, December.
    18. Maurice J. G. Bun & Franc J. G. M. Klaassen, 2007. "The Euro Effect on Trade is not as Large as Commonly Thought," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 69(4), pages 473-496, August.
    19. Hyungsik Roger Moon & Martin Weidner, 2015. "Linear Regression for Panel With Unknown Number of Factors as Interactive Fixed Effects," Econometrica, Econometric Society, vol. 83(4), pages 1543-1579, July.
    20. Joakim Westerlund & Yana Petrova & Milda Norkute, 2019. "CCE in fixed‐T panels," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(5), pages 746-761, August.
    21. Jushan Bai & Serena Ng, 2009. "Boosting diffusion indices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 607-629.
    22. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, July.
    23. Andrews, Donald W. K. & Lu, Biao, 2001. "Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models," Journal of Econometrics, Elsevier, vol. 101(1), pages 123-164, March.
    24. Anindya Banerjee & Josep Lluís Carrion-i-Silvestre, 2017. "Testing for Panel Cointegration Using Common Correlated Effects Estimators," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(4), pages 610-636, July.
    25. Donald, Stephen G & Newey, Whitney K, 2001. "Choosing the Number of Instruments," Econometrica, Econometric Society, vol. 69(5), pages 1161-1191, September.
    26. Hyungsik Roger Moon & Martin Weidner, 2018. "Nuclear Norm Regularized Estimation of Panel Regression Models," Papers 1810.10987, arXiv.org, revised Jun 2023.
    27. Artūras Juodis & Simon Reese, 2022. "The Incidental Parameters Problem in Testing for Remaining Cross-Section Correlation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1191-1203, June.
    28. Westerlund, Joakim & Urbain, Jean-Pierre, 2015. "Cross-sectional averages versus principal components," Journal of Econometrics, Elsevier, vol. 185(2), pages 372-377.
    29. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
    30. Hande Karabiyik & Jean‐Pierre Urbain & Joakim Westerlund, 2019. "CCE estimation of factor‐augmented regression models with more factors than observables," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(2), pages 268-284, March.
    31. Joakim Westerlund & Yousef Kaddoura, 2022. "CCE in heterogenous fixed-T panels [To pool or not to pool: Homogeneous versus heterogenous estimators applied to cigarette demand]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 719-738.
    32. James H. James & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," Working Papers 2005-2, Princeton University. Economics Department..
    33. Wan, Alan T.K. & Zhang, Xinyu & Zou, Guohua, 2010. "Least squares model averaging by Mallows criterion," Journal of Econometrics, Elsevier, vol. 156(2), pages 277-283, June.
    34. De Visscher, Stef & Eberhardt, Markus & Everaert, Gerdie, 2020. "Estimating and testing the multicountry endogenous growth model," Journal of International Economics, Elsevier, vol. 125(C).
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    1. De Vos, Ignace & Stauskas, Ovidijus, 2024. "Cross-section bootstrap for CCE regressions," Journal of Econometrics, Elsevier, vol. 240(1).

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