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...

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...

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...

Dealing with endogenous regressors is a central challenge of applied research. The standard solution is to use instrumental variables that are assumed to be uncorrelated with unobservables. We instead allow the instrumental variable to be correlated with the error term, but we assume the...

This paper develops a nonlinear spatial autoregressive model. Of particular interest is a structural interaction model for share data. We consider possible instrumental variable (IV) and maximum likelihood estimation (MLE) for this model, and analyze asymptotic properties of the IV and MLE based...

This paper considers a factor-augmented regression model in the presence of structural change. We propose a two-step procedure to estimate the coefficients of explanatory variables. We show that when the number of units (N) and the number of periods (T) are large and comparable, the proposed...

Measurement error in an independent variable is one reason why OLS estimates may not be consistent. However, as shown by Dagenais (1994), in some circumstances the OLS bias may be ameliorated somewhat given the presence of serially correlated disturbances, and OLS may prove superior to standard...

In this chapter, we present econometric and statistical methods for analyzing randomized experiments. For basic experiments, we stress randomization-based inference as opposed to sampling-based inference. In randomization-based inference, uncertainty in estimates arises naturally from the random...

Economists who use survey or administrative data for inferences regarding a population may want to combine information obtained from two or more samples drawn from the population. This is the case if there is no single sample that contains all relevant variables. A special case occurs if...