RECENT DEVELOPMENTS IN SEMIPARAMETRIC AND NONPARAMETRIC ESTIMATION OF PANEL DATA MODELS WITH INCOMPLETE INFORMATION: A SELECTED REVIEW Chapter

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: estimation of panel models with discrete or limited dependent variable and (ii) estimation of panel models based on nonparametric deconvolution methods. Copyright 2011 by Emerald Group Publishing Limited.

authors

• Zhang, Yu (Yvette)

author list (cited authors)

• Zhang, Y. Y., Li, Q. i., & Li, D.

citation count

• 0

complete list of authors

• Zhang, Yu Yvette||Li, Qi||Li, Dong

Book Title

• MISSING DATA METHODS: CROSS-SECTIONAL METHODS AND APPLICATIONS

publication date

• January 2011

publisher

• Emerald  Publisher

published in

• Advances in Econometrics  Journal

keywords

• Limited Dependent Variables
• Measurement Errors
• Panel Data Models
• Semiparametric And Nonparametric Methods

Digital Object Identifier (DOI)

• 10.1108/S0731-9053(2011)000027A005

International Standard Book Number (ISBN) 13

• 9781780525242

start page

• 41

end page

• 62

volume

• 27

URL

• http%3A%2F%2Fdx.doi.org%2F10.1108%2Fs0731-9053%282011%29000027a005