Though ordinary least square (OLS) estimates are super-consistent with cointegrated variables, their finite-T bias can be large in the presence of endogenous feedback. Fully modified OLS (FMOLS) are parsimonious tools to measure the cointegrating [long-run] slope between integrated variables in...

In this article we study the widely used in randomized experiments approach of decreasing variance of estimate of average treatment effect (ATE) by estimating ATE on residuals using linear regression, i.e. when we first fit using linear regression on a pooled dataset exploratory...

We study semi-parametric estimation and inference in cointegrated panels with endogenous feedback, allowing for general time-series and cross-section dependence and heterogeneity.Central to this literature are the fully-modified OLS of Phillips and Hansen (1990) that use a spectral...

This paper shows how to increase the power of Hausman's (1978) specification test as well as the difference test in a large class of models. The idea is to impose the restrictions of the null and the alternative hypotheses when estimating the covariance matrix. If the null hypothesis is true...

This paper examines different approaches for assessing causality as typically followed in econometrics and proposes a constructive perspective for improving statistical models elaborated in view of causal analysis. Without attempting to be exhaustive, this paper examines some of these...

We consider the implications of choosing weighting matrix (projection) on the asymptotic risk of the restricted GMM and WLS estimators. Decomposing expected square error risk into variance part and squared bias part, we show that for both GMM and WLS its variance part is minimized by choosing...

Accounting journals regularly include articles where regression coefficients are estimated and reported. However, the level of accuracy with which the estimated coefficients are reported is often not consistent within a single paper. Furthermore, in almost all cases, the level of accuracy...

This note formalizes the synthetic difference-in-differences estimator for staggered treatment adoption settings, as briefly described in Arkhangelsky et al. (2021). To illustrate the importance of this estimator, I use replication data from Abrams (2012), I compare the estimators obtained using...

The instrumental variable model is one of the central tools for the analysis of causal relationships in observational data. The Anderson and Rubin (1949) test is an important method that allows for reliable inference in the instrumental variable model when the instruments are weak. Yet, the...