Identification and estimation of nonlinear dynamic panel data models with unobserved covariates

Ji-Liang Shiu and Yingyao Hu

Journal of Econometrics, 2013, vol. 175, issue 2, 116-131

Abstract: This paper considers nonparametric identification of nonlinear dynamic models for panel data with unobserved covariates. Including such unobserved covariates may control for both the individual-specific unobserved heterogeneity and the endogeneity of the explanatory variables. Without specifying the distribution of the initial condition with the unobserved variables, we show that the models are nonparametrically identified from two periods of the dependent variable Yit and three periods of the covariate Xit. The main identifying assumptions include high-level injectivity restrictions and require that the evolution of the observed covariates depends on the unobserved covariates but not on the lagged dependent variable. We also propose a sieve maximum likelihood estimator (MLE) and focus on two classes of nonlinear dynamic panel data models, i.e., dynamic discrete choice models and dynamic censored models. We present the asymptotic properties of the sieve MLE and investigate the finite sample properties of these sieve-based estimators through a Monte Carlo study. An intertemporal female labor force participation model is estimated as an empirical illustration using a sample from the Panel Study of Income Dynamics (PSID).

Keywords: Nonlinear dynamic panel data model; Dynamic discrete choice model; Dynamic censored model; Nonparametric identification; Initial condition; Correlated random effects; Unobserved heterogeneity; Unobserved covariate; Endogeneity (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (11)

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Related works:
Working Paper: Identification and Estimation of Nonlinear Dynamic Panel Data Models with Unobserved Covariates (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:175:y:2013:i:2:p:116-131

DOI: 10.1016/j.jeconom.2013.03.001

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