Identifying Distributions in a Panel Model with Heteroskedasticity: An Application to Earnings Volatility
Discussion Papers from Department of Economics, Simon Fraser University
This paper considers a panel model with heteroskedasticity, where the parameter of interest is the probability density function of the heteroskedasticity. The nonparametric identification results are established sequentially via a deconvolution argument (in the first step) and solving a linear Fredholm integral equation of the first kind (in the second step). The identification results are constructive and give rise to nonparametric estimators. The model is relevant to the literature on earnings dynamics. Applied to data from the Panel Study of Income Dynamics (PSID), the method developed in this paper reveals a high degree of unobserved heterogeneity in earnings risk. In particular, the evolution over time of the quantiles of the conditional shock variance shows that it is those in the right tail of the distribution who experience the highest volatilities (particularly during recessions), with lower quantiles experiencing relatively constant volatilities during the business cycle. This type of heterogeneity may be relevant to the study of the cyclicality of income risk.
Keywords: Earnings dynamics; panel data; deconvolution; integral equation (search for similar items in EconPapers)
JEL-codes: C14 C23 D31 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:sfu:sfudps:dp17-11
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