Linear regression models form the cornerstone of applied research in economics and other scientific disciplines. When conditional heteroskedasticity is present, or at least suspected, the practice of reweighting the data has long been abandoned in favor of estimating model parameters by ordinary...

This paper shows how asymptotically valid inference in regression models based on the weighted least squares (WLS) estimator can be obtained even when the model for reweighting the data is misspecified. Like the ordinary least squares estimator, the WLS estimator can be accompanied by...

Conditional heteroskedasticity of the error terms is a common occurrence in financial factor models, such as the CAPM and Fama-French factor models. This feature necessitates the use of heteroskedasticity consistent (HC) standard errors to make valid inference for regression coefficients. In...

In the presence of conditional heteroskedasticity, inference about the coefficients in a linear regression model these days is typically based on the ordinary least squares estimator in conjunction with using heteroskedasticity consistent standard errors. Similarly, even when the true form of...

Conditional heteroskedasticity can be exploited to identify the structural vector autoregressions (SVAR) but the implications for inference on structural impulse responses have not been investigated in detail yet. We consider the conditionally heteroskedastic SVAR-GARCH model and propose a...

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 gives a relatively simple, well behaved solution to the problem of many instruments in heteroskedastic data. Such settings are common in microeconometric applications where many instruments are used to improve efficiency and allowance for heteroskedasticity is generally important. The...

This paper re-examines inference for cluster samples. Sensitivity analysis is proposed as a new method to perform inference when the number of groups is small. Based on estimations using disaggregated data, the sensitivity of the standard errors with respect to the variance of the cluster...

We extend the standard evaluation framework to allow for interactions between individuals within segmented markets. An individual's outcome depends not only on the assigned treatment status but also on (features of) the distribution of treatments in his market. To evaluate how the distribution...

This paper gives a test of overidentifying restrictions that is robust to many instruments and heteroskedasticity. It is based on a jackknife version of the Sargan test statistic, having a numerator that is the objective function minimized by the JIVE2 estimator of Angrist, Imbens, and Krueger...