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INTERACTIVE EFFECTS PANEL DATA MODELS WITH GENERAL FACTORS AND REGRESSORS

Published online by Cambridge University Press:  05 September 2023

Bin Peng Open the ORCID record for Bin Peng [Opens in a new window] ,
Liangjun Su ,
Joakim Westerlund  and
Yanrong Yang
Bin Peng*
Affiliation:
Monash University
Liangjun Su
Affiliation:
Tsinghua University
Joakim Westerlund
Affiliation:
Lund University and Deakin University
Yanrong Yang
Affiliation:
Australian National University
*
Address correspondence to Bin Peng, Department of Econometrics and Business Statistics, Monash University, Caulfield East, VIC, Australia; e-mail: [email protected].
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Abstract

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This paper considers a model with general regressors and unobservable common factors. An estimator based on iterated principal component analysis is proposed, which is shown to be not only asymptotically normal, but under certain conditions also free of the otherwise so common asymptotic incidental parameters bias. Interestingly, the conditions required to achieve unbiasedness become weaker the stronger the trends in the factors, and if the trending is strong enough, unbiasedness comes at no cost at all. The approach does not require any knowledge of how many factors there are, or whether they are deterministic or stochastic. The order of integration of the factors is also treated as unknown, as is the order of integration of the regressors, which means that there is no need to pre-test for unit roots, or to decide on which deterministic terms to include in the model.

Type
MISCELLANEA
Information
Econometric Theory , First View , pp. 1 - 17
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press

Footnotes

Previous versions of the paper were presented at seminars at Aarhus University, Lund University, and Michigan State University. The authors would like to thank seminar participants, and in particular Richard Baillie, Nicholas Brown, David Edgerton, Iván Fernández-Val (Co-Editor), Yousef Kaddoura, Yana Petrova, Simon Reese, Tim Vogelsang, Jeffrey Wooldridge, Morten Ørregaard Nielsen, and the anonymous referees for useful comments. Su thanks the National Natural Science Foundation of China for financial support under the grant number 72133002, Westerlund thanks the Knut and Alice Wallenberg Foundation for financial support through a Wallenberg Academy Fellowship, and Peng, Westerlund, and Yang thank the Australian Research Council Discovery Grants Program for financial support under grant numbers DP210100476 and DP230102250.

References

Ahn, S.C. & Horenstein, A.R. (2013) Eigenvalue ratio test for the number of factors. Econometrica 81, 1203–1227.Google Scholar
Ando, T. & Bai, J. (2017) Clustering huge number of financial time series: A panel data approach with high-dimensional predictors and factor structures. Journal of the American Statistical Association 112, 1182–1198.CrossRefGoogle Scholar
Bai, J. (2004) Estimating cross-section common stochastic trends in nonstationary panel data. Journal of Econometrics 122, 137–183.CrossRefGoogle Scholar
Bai, J. (2009) Panel data models with interactive fixed effects. Econometrica 77, 1229–1279.Google Scholar
Bai, J., Kao, C., & Ng, S. (2009) Panel cointegration with global stochastic trends. Journal of Econometrics 149, 82–99.CrossRefGoogle Scholar
Bai, J. & Ng, S. (2002) Determining the number of factors in approximate factor models. Econometrica 70, 191–221.CrossRefGoogle Scholar
Bai, J. & Ng, S. (2004) A p anic attack on unit roots and cointegration. Econometrica 72, 1127–1177.CrossRefGoogle Scholar
Chudik, A., Pesaran, M.H., & Tosetti, E. (2011) Weak and strong cross section dependence and estimation of large panels. The Econometrics Journal 14, 45–90.CrossRefGoogle Scholar
Huang, W., Jin, S., Phillips, P.C.B., & Su, L. (2021) Nonstationary panel models with latent group structures and cross-section dependence. Journal of Econometrics 221, 198–222.CrossRefGoogle Scholar
Lam, C. & Yao, Q. (2012) Factor modeling for high-dimensional time series: Inference for the number of factors. Annals of Statistics 40, 694–726.CrossRefGoogle Scholar
Moon, H.R. & Weidner, M. (2015) Linear regression for panel with unknown number of factors as interactive fixed effects. Econometrica 83, 1543–1579.CrossRefGoogle Scholar
Park, J.Y. & Phillips, P.C.B. (2000) Nonstationary binary choice. Econometrica 68, 1249–1280.CrossRefGoogle Scholar
Pesaran, M.H. (2006) Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica 74, 967–1012.CrossRefGoogle Scholar
Westerlund, J. (2018) CCE in panels with general unknown factors. The Econometrics Journal 21, 264–276.CrossRefGoogle Scholar
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