Nonparametric heteroskedasticity in persistent panel processes: An application to earnings dynamics

This paper considers a dynamic panel model where a latent state variable follows a unit root process with nonparametric heteroskedasticity. We develop constructive nonparametric identification and estimation of the skedastic function. Applying this method to the Panel Survey of Income Dynamics (PSID...

Full description

Saved in:
Bibliographic Details
Published inJournal of econometrics Vol. 203; no. 2; pp. 283 - 296
Main Authors Botosaru, Irene, Sasaki, Yuya
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.04.2018
Elsevier Sequoia S.A
Subjects
Conditional heteroskedasticity
Earnings
Earnings risk
Economic models
Nonparametric identification
Nonparametric statistics
Recessions
Volatility
Conditional heteroskedasticity
C23
E24
Nonparametric identification
Earnings risk
C14
Online AccessGet full text

Cover

More Information
Summary:This paper considers a dynamic panel model where a latent state variable follows a unit root process with nonparametric heteroskedasticity. We develop constructive nonparametric identification and estimation of the skedastic function. Applying this method to the Panel Survey of Income Dynamics (PSID) in the framework of earnings dynamics, we found that workers with lower pre-recession permanent earnings had higher earnings risk during the three most recent recessions.
ISSN:0304-4076
1872-6895
DOI:10.1016/j.jeconom.2017.11.010