Eduardo A. Souza-Rodrigues
Department of Economics
University of Toronto
Max Gluskin House, 150 St. George Street, room 324
Toronto, ON, Canada
Institutional Affiliation: University of Toronto
NBER Working Papers and Publications
|February 2020||Partial Identification and Inference for Dynamic Models and Counterfactuals|
with Myrto Kalouptsidi, Yuichi Kitamura, Lucas Lima: w26761
We provide a general framework for investigating partial identification of structural dynamic discrete choice models and their counterfactuals, along with uniformly valid inference procedures. In doing so, we derive sharp bounds for the model parameters, counterfactual behavior, and low-dimensional outcomes of interest, such as the average welfare effects of hypothetical policy interventions. We char- acterize the properties of the sets analytically and show that when the target outcome of interest is a scalar, its identified set is an interval whose endpoints can be calculated by solving well-behaved constrained optimization problems via standard algorithms. We obtain a uniformly valid inference pro- cedure by an appropriate application of subsampling. To illustrate the performance and co...
|March 2019||Optimal Environmental Targeting in the Amazon Rainforest|
with Juliano Assunção, Robert McMillan, Joshua Murphy: w25636
This paper sets out an empirically-driven approach for targeting environmental policies optimally in order to combat deforestation. We focus on the Amazon, the world's most extensive rainforest, where Brazil's federal government issued a ‘Priority List’ of municipalities in 2008, to be targeted with more intense environmental monitoring and enforcement. In this setting, we first estimate the causal impact of the Priority List on deforestation using ‘changes-in-changes’ (Athey and Imbens, 2006), a flexible treatment effects estimation method, finding that it reduced deforestation by 40 percent and cut emissions by 39.5 million tons of carbon. Second, we develop a novel framework for computing targeted ex-post optimal blacklists. This involves a procedure for assigning municipalities to a c...
|October 2018||Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models|
with Myrto Kalouptsidi, Paul T. Scott: w25134
In structural dynamic discrete choice models, the presence of serially correlated unobserved states and state variables that are measured with error may lead to biased parameter estimates and misleading inference. In this paper, we show that instrumental variables can address these issues, as long as measurement problems involve state variables that evolve exogenously from the perspective of individual agents (i.e., market-level states). We define a class of linear instrumental variables estimators that rely on Euler equations expressed in terms of conditional choice probabilities (ECCP estimators). These estimators do not require observing or modeling the agent’s entire information set, nor solving or simulating a dynamic program. As such, they are simple to implement and computationally ...
|September 2015||Identification of Counterfactuals in Dynamic Discrete Choice Models|
with Myrto Kalouptsidi, Paul T. Scott: w21527
Dynamic discrete choice models (DDC) are not identified nonparametrically. However, the non-identification of DDC models does not necessarily imply non-identification of counterfactuals of interest. Using a novel approach that can accommodate both nonparametric and restricted payoff functions, we provide necessary and sufficient conditions for the identification of counterfactual behavior and welfare for a broad class of counterfactuals. The conditions are simple to check and can be applied to virtually all counterfactuals in the DDC literature. To explore the robustness of counterfactual results to model restrictions in practice, we consider a numerical example of a monopolist's entry problem, as well as an empirical model of agricultural land use. In each case, we provide examples of bot...