Most Difference-in-Difference (DD) papers rely on many years of data and focus on serially correlated outcomes. Yet almost all these papers ignore the bias in the estimated standard errors that serial correlation introduces. This is especially troubling because the independent variable of...

We have presented two simple methods to produce a feasible (i.e. real, symmetric, and positivesemidefinite) correlation matrix when the econometric one is either noisy, unavailable, or inappropriate. The first method is to the knowledge of the authors more general than any of the approaches...

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...

We employ a wavelet approach and conduct a time-frequency analysis of dynamic correlations between pairs of key traded assets (gold, oil, and stocks) covering the period from 1987 to 2012. The analysis is performed on both intra-day and daily data. We show that heterogeneity in correlations...

The problem of regression shrinkage and selection for multivariate regression is considered. The goal is to consistently identify those variables relevant for regression. This is done not only for predictors but also for responses. To this end, a novel relationship between multivariate...

In this note we provide the analytical gradient of the full model likelihood of the DCC specification of Engle (2002), the generalized version of Cappiello et al. (2006), and of the cDCC model of Aielli (2008)

This paper proves that the mean independence of the error term from the covariates in a linear regression model is equivalent to, rather than just a sufficient condition for, the error term being uncorrelated with any function of the covariates. Therefore, correct functional form specification...