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

Jean-Bernard Chatelain () and Kirsten Ralf

Economic Modelling, 2021, vol. 97, issue C, 157-166

Abstract: For static panel data models that include endogenous time-invariant variables correlated with individual effects, exogenous averages over time of time-varying variables can be internal instruments. To pretest their exogeneity, we first estimate a random effects model that includes all averages over time of time-varying variables (Mundlak, 1978; Krishnakumar, 2006). Internal instruments are then selected if their parameter is statistically different from zero (Mundlak, 1978; Hausman and Taylor, 1981). Finally, we estimate a Hausman-Taylor (1981) model using these internal instruments. We then evaluate the biases of currently used alternative estimators in a Monte-Carlo simulation: repeated between, ordinary least squares, two-stage restricted between, Oaxaca-Geisler estimator, fixed effect vector decomposition, and random effects (restricted generalized least squares).

Keywords: Time-invariant variables; Panel data; Pretest estimator; Instrumental variables; Mundlak estimator; Hausman-Taylor estimator (search for similar items in EconPapers)
JEL-codes: C01 C22 C23 (search for similar items in EconPapers)
Date: 2021
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Related works:
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) Downloads
Working Paper: Inference on time-invariant variables using panel data: a pretest estimator (2021) Downloads
Working Paper: Inference on time-invariant variables using panel data: a pretest estimator (2020)
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:eee:ecmode:v:97:y:2021:i:c:p:157-166

DOI: 10.1016/j.econmod.2021.01.014

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