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

Irene Botosaru, Yuya Sasaki

Research output: Contribution to journalArticle (Academic Journal)peer-review

3 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)283-296
Number of pages14
JournalJournal of Econometrics
Volume203
Issue number2
Early online date21 Dec 2017
DOIs
Publication statusPublished - 1 Apr 2018

Structured keywords

  • ECON CEPS Data

Keywords

  • Conditional heteroskedasticity
  • Earnings risk
  • Nonparametric identification

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