Combined Estimation of Semiparametric Panel Data Models

Tae Hwy Lee, Bai Huang () and Aman Ullah
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Bai Huang: Central University of Finance and Economics

No 201915, Working Papers from University of California at Riverside, Department of Economics

Abstract: The combined estimation for the semiparametric panel data models is proposed. The properties of estimators for the semiparametric panel data models with random effects (RE) and fixed effects (FE) are examined. When the RE estimator suffers from endogeneity due to the individual effects correlated with the regressors, the semiparametric RE and FE estimators may be adaptively combined, with the combining weights depending on the degree of endogeneity. The asymptotic distributions of these three estimators (RE, FE, and combined estimators) for the semiparametric panel data models are derived using a local asymptotic framework. These three estimators are then compared in asymptotic risk. The semiparametric combined estimator has strictly smaller asymptotic risk than the semiparametric fixed effect estimator. The Monte Carlo study shows that the semiparametric combined estimator outperforms semiparametric FE and RE estimators except when the degrees of endogeneity and heterogeneity of the individual effects are very small. Also presented is an empirical application where the effect of public sector capital in the private economy production function is examined using the US state level panel data.

Keywords: Endogeneity; Panel Data; Semiparametric FE estimator; Semiparametric RE estimator; Semiparametric Combined Estimator; Local Asymptotics; Hausman Test. (search for similar items in EconPapers)
JEL-codes: C13 C33 C52 (search for similar items in EconPapers)
Pages: 30 Pages
Date: 2018-07
New Economics Papers: this item is included in nep-ecm and nep-ore
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https://economics.ucr.edu/repec/ucr/wpaper/201915.pdf First version, 2018 (application/pdf)

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Journal Article: Combined estimation of semiparametric panel data models (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:ucr:wpaper:201915

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