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Working Paper
What Do Data on Millions of U.S. Workers Reveal about Life-Cycle Earnings Risk?
We study the evolution of individual labor earnings over the life cycle using a large panel data set of earnings histories drawn from U.S. administrative records. Using fully nonparametric methods, our analysis reaches two broad conclusions. First, earnings shocks display substantial deviations from lognormality?the standard assumption in the incomplete markets literature. In particular, earnings shocks display strong negative skewness and extremely high kurtosis?as high as 30 compared with 3 for a Gaussian distribution. The high kurtosis implies that in a given year, most individuals ...
Working Paper
Nonparametric Time Varying IV-SVARs: Estimation and Inference
This paper studies the estimation and inference of time-varying impulse response functions in structural vector autoregressions (SVARs) identified with external instruments. Building on kernel estimators that allow for nonparametric time variation, we derive the asymptotic distributions of the relevant quantities. Our estimators are simple and computationally trivial and allow for potentially weak instruments. Simulations suggest satisfactory empirical coverage even in relatively small samples as long as the underlying parameter instabilities are sufficiently smooth. We illustrate the methods ...