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Interactive Effects Panel Data Models with General Factors and Regressors

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Název: Interactive Effects Panel Data Models with General Factors and Regressors
Autoři: Westerlund, Joakim, Su, Liangjun, Peng, Bin, Yang, Yanrong
Přispěvatelé: Lund University, Lund University School of Economics and Management, LUSEM, Department of Economics, Lunds universitet, Ekonomihögskolan, Nationalekonomiska institutionen, Originator
Zdroj: Econometric Theory. 41(2):472-488
Témata: Social Sciences, Economics and Business, Economics, Samhällsvetenskap, Ekonomi och näringsliv, Nationalekonomi, Natural Sciences, Mathematical Sciences, Probability Theory and Statistics, Naturvetenskap, Matematik, Sannolikhetsteori och statistik
Popis: 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.
Přístupová URL adresa: https://doi.org/10.1017/S0266466623000270
Databáze: SwePub
Popis
Abstrakt: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.
ISSN:14694360
DOI:10.1017/S0266466623000270