One of the perceived advantages of difference-in-differences (DiD) methods is that they do not explicitly restrict how units select into treatment. However, when justifying DiD, researchers often argue that the treatment is "quasi-randomly" assigned. We investigate what selection mechanisms are...

I expose the risk of false discoveries in the context of multiple treatment effects. A false discovery is a nonexistent effect that is falsely labeled as statistically significant by its individual t-value. Labeling nonexistent effects as statistically significant has wide-ranging academic and...

Conditional distributions for the analysis of convergence are usually estimated using a standard kernel smoother but this is known to be biased. Hyndman et al. (1996) thus suggest a conditional density estimator with a mean function specified by a local polynomial smoother, i.e. one with better...

This paper aims to establish asymptotic normality of the local linear kernel estimator for quantile regression under near epoch dependence, a useful concept in characterising time series dependence of extensive interests in Econometrics. In particular, near epoch dependence can cover a wide...

This paper addresses the choice of an optimal smoothing parameter for local polynomial matching. A version of Empirical Bias Bandwidth Selection (EBBS) proposed by Ruppert (1997) is applied to account for the MSE computation of the matching estimator. Thereby, an estimator for the large sample...

We propose various semiparametric estimators for nonlinear selection models, where slope and intercept can be separately identifed. When the selection equation satisfies a monotonic index restriction, we suggest a local polynomial estimator, using only observations for which the marginal...

This paper investigates the estimation and inference of spatial panel data models in which the regression coefficient vector is a trending function. We use time differences to eliminate the individual effects and employ GMM estimations for regression coefficients with both linear and quadratic...

I expose the risk of false discoveries in the context of multiple treatment effects. A false discovery is a nonexistent effect that is falsely labeled as statistically significant by its individual t-value. Labeling nonexistent effects as statistically significant has wide-ranging academic and...

The sample covariance matrix is known to contain substantial statistical noise, making it inappropriate for use in financial decision making. Leading researchers have proposed various filtering methods that attempt to reduce the level of noise in the covariance matrix estimator. In most cases,...