We show the extent of errors made in the award of disability insurance using matched survey-administrative data. False rejections (Type I errors) are widespread, and there are large gender differences in these type I error rates.
We compare two groups of the non-student Korean population—native-born South Koreans (SK) and North Korean refugees (NK)—with contrasting institutional and cultural backgrounds.
We investigate the consequences of discreteness in the assignment variable in regression-discontinuity designs for cases where the outcome variable is itself discrete.
This paper presents a weighted optimization framework that unifies the binary, multi-valued, continuous, as well as mixture of discrete and continuous treatment, under unconfounded treatment assignment.
We propose a solution to the measurement error problem that plagues the estimation of the relation between the expected return of the stock market and its conditional variance due to the latency of these conditional moments.
We present a general framework for studying regularized estimators; such estimators are pervasive in estimation problems wherein “plug-in” type estimators are either ill-defined or ill-behaved.
This paper investigates how different income shocks shape consumption dynamics over the business cycle. First, we break new ground by creating a unique, panel dataset of transitory and permanent income shocks, using subjective income expectations from the Dutch Household Survey.
A new quantile regression model for survival data is proposed that permits a positive proportion of subjects to become unsusceptible to recurrence of disease following treatment or based on other observable characteristics.
This paper applies a novel bootstrap method, the kernel block bootstrap, to quasi-maximum likelihood estimation of dynamic models with stationary strong mixing data.
We specify an equilibrium model of car ownership with private information where individuals sell and purchase new and second-hand cars over their life-cycle.
