The mixture innovation (MI) model places spike-and-slab mixture distributions on the innovations of time-varying regression coefficients and allows for a wide variety of time variation patterns. Despite its appeal, estimating the MI model can be computationally prohibitive when the number of...

This paper considers a class of fixed-T nonlinear panel models with time-varying link function, fixed effects, and endogenous regressors. We establish sufficient conditions for the identification of the regression coefficients, the time-varying link function, the distribution of counterfactual...

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...

This paper presents an inferential approach for the influential-imitator dynamics, a broadly accepted extension of the traditional Bass model, for cases in which the population has two segments: influentials (who influence each other) and imitators (whose choices are affected by the ones of...

This brief introduces district-level GDP and GDP per capita estimates for Uganda’s 116 districts1 utilizing a method based on models using imagery of nighttime lights and agricultural data. Several approaches for estimating grid-level GDP were tested. After comparing model outputs across...

This paper studies large sample properties of a Bayesian approach to inference about slope parameters in linear regression models with a structural break. In contrast to the conventional approach to inference about the slope parameters that does not take into account the uncertainty of the...

We develop a new method for estimating industry-level and aggregate total factor productivity (TFP) growth. Our method accounts for profits and adjustment costs, and uses firm surveys to proxy for changes in factor utilization. Using it to compute TFP growth rates in the United States and in...

This paper considers bootstrap inference in model averaging for predictive regressions. We firstshow that a naïve bootstrap approach, which consists of stacking all residuals at time t into a vector, and then resampling these cross-sectional vectors of residuals over time is invalid in the...

We examine the implications of arbitrage in a market with many assets. The absence of arbitrage opportunities implies that the linear functionals that give the mean and cost of a portfolio are continuous; hence there exist unique portfolios that represent these functionals. The mean variance...

This paper develops asymptotic distribution theory for generalized method of moments (GMM) estimators and test statistics when some of the parameters are well identified, but others are poorly identified because of weak instruments. The asymptotic theory entails applying empirical process theory...