Recursive estimation in large panel data models: Theory and practice

Bin Jiang (), Yanrong Yang (), Jiti Gao and Cheng Hsiao

No 5/17, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: Bai (2009) proposes a recursive least-squares estimation method for large panel data models with unobservable interactive fixed effects, but the impact of recursion on the asymptotic properties of the least-squares estimators is not taken into account. In this paper, we extend Bai (2009) by investigating the recursive estimator asymptotically. In general, the asymptotic properties we establish for the recursive estimators largely complement the theory and practice of the recursive least-squares procedure suggested by Bai (2009). In particular, we show that consistency of the recursive estimator depends on three key points, consistency of the initial OLS estimator, the number of recursive steps and the endogeneity arising due to the dependence between regressors and interactive effects. Compared to the theoretical estimator in Bai (2009), such endogeneity affects the convergence rate of recursive least-squares estimators. Finite sample properties of the proposed estimators are investigated using a simulation study.

Keywords: Bayesian average; conditional mean estimation; ergodic theorem; summary statistic. (search for similar items in EconPapers)
Pages: 40
Date: 2017
New Economics Papers: this item is included in nep-ore
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Citations: View citations in EconPapers (8)

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Journal Article: Recursive estimation in large panel data models: Theory and practice (2021)
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