Abstract coming soon

Abstract coming soon

Many studies use instruments or treatments that combine a set of exogenous shocks with other predetermined variables via a known formula. Examples include shift-share instruments and measures of social or spatial spillovers. I review recent econometric tools for this setting that leverage the assignment process of the exogenous shocks and the structure of the formula for identification. I compare this design-based approach with conventional estimation strategies based on conditional unconfoundedness and contrast it with alternative strategies that leverage a model for unobservables.

This article introduces a novel command, tvpreg, that implements the following path estimators: (i) the asymptotically weighted average risk minimizing path estimators by Müller and Petalas, 2010, Review of Economic Studies 77: 1508–1539; (ii) and the path estimators proposed in Inoue, Rossi, and Wang, 2024, Journal of Econometrics: 105726, namely, the time-varying parameter local projections, the time-varying parameter instrumental-variables estimator, and the version of the latter estimator robust to weak instruments.

This talk gives an overview of the existing strategies for identification of causal effects in models with endogeneity that leverage bunching in the treatment variable. It covers some of the most user-friendly estimators, some strategies for robustness analysis, and a light discussion of the most recent nonparametric methods for identification of local effects.

Abstract coming soon

This presentation provides a practical guide for Stata users on the consequences of heteroskedasticity and heterogeneity in sample-selection models and estimators. First, we review the consequences of heteroskedasticity in the outcome equation, highlighting when standard sample-selection estimators are and are not consistent and how to obtain valid inference using the gtsheckman command. Given the strong implications of heteroskedasticity, we propose three tests to determine if and what type of heteroskedasticity is present. Next we consider the case of heteroskedasticity in both the outcome and sample-selection equation, again utilizing the gtsheckman command to implement a general control function approach. Finally, we extend to the panel-data setting, where we show how the same methods introduced earlier provide consistent estimation of panel-data sample-selection models that incorporate individual heterogeneity in both the intercept and the slopes.