Alexandre Poirier

How Should the Graduate Economics Core be Changed

The authors present suggestions by graduate students from a range of economics departments for improving the first-year core sequence in economics. The students identified a number of elements that should be added to the core: more training in building microeconomic models, a discussion of the methodological foundations of model-building, more emphasis on institutions to motivate and contextualize macroeconomic models, and greater focus on econometric practice rather than theory. The authors hope that these suggestions will encourage departments to take a fresh look at the content of the first-year core.

Identification of Treatment Effects Under Conditional Partial Independence

Conditional independence of treatment assignment from potential outcomes is a commonly used but nonrefutable assumption. We derive identified sets for various treatment effect parameters under nonparametric deviations from this conditional independence assumption. These deviations are defined via a conditional treatment assignment probability, which makes it straightforward to interpret. Our results can be used to assess the robustness of empirical conclusions obtained under the baseline conditional independence assumption.

Identification of Treatment Effects under Conditional Partial Independence

Conditional independence of treatment assignment from potential outcomes is a commonly used but nonrefutable assumption. We derive identified sets for various treatment effect parameters under nonparametric deviations from this conditional independence assumption. These deviations are defined via a conditional treatment assignment probability, which makes it straightforward to interpret. Our results can be used to assess the robustness of empirical conclusions obtained under the baseline conditional independence assumption.

Efficient Estimation in Models with Independence Restrictions

Unconditional and conditional independence restrictions are used in many econometric models to identify their parameters. However, there are few results about efficient estimation procedures for finite-dimensional parameters under these independence restrictions. This paper computes the efficiency bound for finite-dimensional parameters under independence restrictions, and proposes an estimator that is consistent, asymptotically normal and which achieves the efficiency bound. The estimator is based on a growing number of zero-covariance conditions that are asymptotically equivalent to the independence restriction. The results are illustrated with examples, including an instrumental variables regression model and partially linear regression models. A small Monte Carlo study is performed to investigate the estimator’s small sample properties and to quantify the efficiency gains that can be made by using the proposed efficient estimator.

Estimation of Models with Multiple-Valued Explanatory Variables

We study estimation and inference when there are multiple values (“matches”) for the explanatory variables and only one of the matches is the correct one. This problem arises often when two dataset...