In a high dimensional linear regression model, we propose a new procedure for testing statistical significance of a subset of regression coefficients. Specifically, we employ the partial covariances between the response variable and the tested covariates to obtain a test statistic. The resulting...

This paper considers testing problems where several of the standard regularity conditions fail to hold. We consider the case where (i) parameter vectors in the null hypothesis may lie on the boundary of the maintained hypothesis and (ii) there may be a nuisance parameter that appears under the...

The estimation of regression models subject to linear restrictions is a widely applied technique, however, aside from simple examples, the equivalence between the linear restricted case to the reparameterization or substitution case is rarely employed. We derive a general relationship that...

Recently, there has been much discussion about replicability and credibility. By integrating the full research record, increasing statistical power, reducing bias and enhancing credibility, meta-analysis is widely regarded as 'best evidence'. Through Monte Carlo simulation, closely calibrated on...

Count data regressions are an important tool for empirical analyses ranging from analyses of patent counts to measures of health and unemployment. Along with negative binomial, Poisson panel regressions are a preferred method of analysis because the Poisson conditional fixed effects maximum...

This paper considers regression models for cross-section data that exhibit cross-section dependence due to common shocks, such as macroeconomic shocks. The paper analyzes the properties of least squares (LS) and instrumental variables (IV) estimators in this context. The results of the paper...

This paper proposes several tests of restricted specification in nonparametric instrumental regression. Based on series estimators, test statistics are established that allow for tests of the general model against a parametric or nonparametric specification as well as a test of exogeneity of the...

Model selection and forecasting in stress tests can be facilitated using machine learning techniques. These techniques have proved robust in other fields for dealing with the curse of dimensionality, a situation often encountered in applied stress testing. Lasso regressions, in particular, are...

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...