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Shiu, Ji-Liang
Hu, Yingyao
Year of Publication: 
Series/Report no.: 
Working Paper 557
This paper considers nonparametric identification of nonlinear dynamic models for panel data with unobserved voariates. 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 three periods of data. The main identifying assumption requires the evolution of the observed covariates depends on the unoberved 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 property of the sieve MLE and investigate the finite sample properties of these sieve-based estimator 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).
Dynamic nonlinear panel data model
dynamic discrete choice model
dynamic censored model
nonparametric identification
initial condition
random effects
unobserved heterogeneity
unobserved covariate
intertemporal labor force participation
Document Type: 
Working Paper

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