Identification in most sample selection models depends on the independence of the regressors and the error terms conditional on the selection probability. All quantile and mean functions are parallel in these models; this implies that quantile estimators cannot reveal any - per assumption...

This paper presents a Bayesian significance test for stationarity of a regression equation using the highest posterior density credible set. In addition, a solution to the Behrens- Fisher problem is provided. From a Monte Carlo simulation study, it has been shown that the Bayesian significance...

We propose a test for the key identification and estimation conditions in Regression Discontinuity (RD) designs. We characterize the set of sharp testable implications of the RD assumptions, for which the proposed test is uniformly valid under a class of distributions, is consistent against all...

This paper provides distribution free tests for detecting sample selection in conditional quantile functions. The first test is an omitted predictor test with the propensity score as the omitted variable. In the case of rejection we cannot distinguish between rejection due to genuine selection...

We develop a new permutation test for inference on a subvector of coefficients in linear models. The test is exact when the regressors and the error terms are independent. Then we show that the test is asymptotically of correct level, consistent, and has power against local alternatives when the...

The hypothesis that the legalisation of abortion contributed significantly to the reduction of crime in the United States in 1990s is one of the most prominent ideas from the recent “economics-made-fun” movement sparked by the book Freakonomics. This paper expands on the existing literature...

We develop a method of testing linearity using power transforms of regressors, allowing for stationary processes and time trends. The linear model is a simplifying hypothesis that derives from the power transform model in three different ways, each producing its own identification problem. We...

Consider the nonparametric regression model Y = m(X)+e, where the function m is smooth, but unknown. We construct tests for the independence of e and X, based on n independent copies of (X; Y). The testing procedures are based on differences of neighboring Y's. We establish asymptotic results...

We consider the effects of using residuals from robust regression in place of OLS residuals in test statistics for the normality of the errors. We find that this can lead to substantially improved ability to detect lack of normality in suitable situations. Using simulations, we find that...

This paper is concerned with problem of variable selection and forecasting in the presence of parameter instability. There are a number of approaches proposed for forecasting in the presence of breaks, including the use of rolling windows or exponential down-weighting. However, these studies...