Inference on time-invariant variables using panel data: a pretest estimator

Jean-Bernard Chatelain and Kirsten Ralf

Working Papers from HAL

Abstract: For panel data models including time-invariant variables, this paper proposes a new Hausman pretest estimator of the internal instruments of Hausman-Taylor estimator. It assumes Mundlak and Krishnakumar linear specification for the endogeneity of random individual effects. Furthermore, the paper evaluates the biases of currently used estimators: repeated between, ordinary least squares, two-stage restricted between, Oaxaca-Geisler estimator, fixed effect vector decomposition, and generalized least squares. Some of these may lead to erroneous conclusions regarding the statistical significance of the estimated parameter values of time-invariant variables, especially when time-invariant variables are correlated with the individual effects.

Keywords: Time-Invariant Variables; Panel Data; Time-Series Cross-Sections; Hausman Pretest Estimator; Mundlak Estimator; Hausman-Taylor Estimator (search for similar items in EconPapers)
Date: 2020-12-13
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Related works:
Journal Article: Inference on time-invariant variables using panel data: A pretest estimator (2021)
Working Paper: Inference on time-invariant variables using panel data: A pretest estimator (2021)
Working Paper: Inference on time-invariant variables using panel data: A pretest estimator (2021)
Working Paper: Inference on time-invariant variables using panel data: a pretest estimator (2021)
Working Paper: Inference on time-invariant variables using panel data: a pretest estimator (2021)
Working Paper: Inference on time-invariant variables using panel data: a pretest estimator (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:wpaper:halshs-03059883

DOI: 10.2139/ssrn.3165633

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