Regularized regression methods have attracted much attention in the literature, mainly due to its application in high-dimensional variable selection problems. Most existing regularization methods assume that the predictors are directly observed and precisely measured. It is well known that in a...

In linear regression models, measurement error in a covariate causes Ordinary Least Squares (OLS) to be biased and inconsistent. Instrumental Variables (IV) is a common solution. While IV is also biased, it is consistent. Here, we undertake an asymptotic comparison of OLS and IV in the case...

When evaluating partial effects, it is important to distinguish between structural endogeneity and measurement errors. In contrast to linear models, these two sources of endogeneity affect partial effects differently in nonlinear models. We study this issue focusing on the Instrumental Variable...

High nonresponse rates have become a rule in survey sampling. In panel surveys there occur additional sample losses due to panel attrition, which are thought to worsen the bias resulting from initial nonresponse. However, under certain conditions an initial wave nonresponse bias may vanish in...

This paper conducts a comprehensive investigation of the "forecast combination puzzle" in the context of the heterogeneous autoregressive (HAR) model for volatility forecasting. The widely-used HAR model can be considered as a theoretically optimal combination of three random-walk forecasts...

While partial moments like semivariance, lower and upper partial moments have seen acceptance by both academics and investment professionals, there are some who consider these measures to be ad hoc measures of investment performance. This paper seeks to provide the academic foundation for the...

Accurate data on crime rates is essential for evaluating many social programs. However, in the United States, officials often impute crime rates to compensatefor the 20--40% of agencies that do not report crime statistics. I show that all commonly used methods underestimate crime. First, I show...