Recent Developments in Semiparametric and Nonparametric Estimation of Panel Data Models with Incomplete Information: A Selected Review

Yu Yvette Zhang, Qi Li and Dong Li

A chapter in Missing Data Methods: Cross-sectional Methods and Applications, 2011, pp 41-62 from Emerald Group Publishing Limited

Abstract: This chapter reviews the recent developments in the estimation of panel data models in which some variables are only partially observed. Specifically we consider the issues of censoring, sample selection, attrition, missing data, and measurement error in panel data models. Although most of these issues, except attrition, occur in cross-sectional or time series data as well, panel data models introduce some particular challenges due to the presence of persistent individual effects. The past two decades have seen many stimulating developments in the econometric and statistical methods dealing with these problems. This review focuses on two strands of research of the rapidly growing literature on semiparametric and nonparametric methods for panel data models: (i) estimation of panel models with discrete or limited dependent variables and (ii) estimation of panel models based on nonparametric deconvolution methods.

Keywords: Panel data models; limited dependent variables; measurement errors; semiparametric and nonparametric methods (search for similar items in EconPapers)
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-9053(2011)000027a005

DOI: 10.1108/S0731-9053(2011)000027A005

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