Browsing by Author "Bugni, Federico A"
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Item Open Access Essays on Microeconometrics(2016) Ura, TakuyaMy dissertation has three chapters which develop and apply microeconometric tech- niques to empirically relevant problems. All the chapters examines the robustness issues (e.g., measurement error and model misspecification) in the econometric anal- ysis. The first chapter studies the identifying power of an instrumental variable in the nonparametric heterogeneous treatment effect framework when a binary treat- ment variable is mismeasured and endogenous. I characterize the sharp identified set for the local average treatment effect under the following two assumptions: (1) the exclusion restriction of an instrument and (2) deterministic monotonicity of the true treatment variable in the instrument. The identification strategy allows for general measurement error. Notably, (i) the measurement error is nonclassical, (ii) it can be endogenous, and (iii) no assumptions are imposed on the marginal distribution of the measurement error, so that I do not need to assume the accuracy of the measure- ment. Based on the partial identification result, I provide a consistent confidence interval for the local average treatment effect with uniformly valid size control. I also show that the identification strategy can incorporate repeated measurements to narrow the identified set, even if the repeated measurements themselves are endoge- nous. Using the the National Longitudinal Study of the High School Class of 1972, I demonstrate that my new methodology can produce nontrivial bounds for the return to college attendance when attendance is mismeasured and endogenous.
The second chapter, which is a part of a coauthored project with Federico Bugni, considers the problem of inference in dynamic discrete choice problems when the structural model is locally misspecified. We consider two popular classes of estimators for dynamic discrete choice models: K-step maximum likelihood estimators (K-ML) and K-step minimum distance estimators (K-MD), where K denotes the number of policy iterations employed in the estimation problem. These estimator classes include popular estimators such as Rust (1987)’s nested fixed point estimator, Hotz and Miller (1993)’s conditional choice probability estimator, Aguirregabiria and Mira (2002)’s nested algorithm estimator, and Pesendorfer and Schmidt-Dengler (2008)’s least squares estimator. We derive and compare the asymptotic distributions of K- ML and K-MD estimators when the model is arbitrarily locally misspecified and we obtain three main results. In the absence of misspecification, Aguirregabiria and Mira (2002) show that all K-ML estimators are asymptotically equivalent regardless of the choice of K. Our first result shows that this finding extends to a locally misspecified model, regardless of the degree of local misspecification. As a second result, we show that an analogous result holds for all K-MD estimators, i.e., all K- MD estimator are asymptotically equivalent regardless of the choice of K. Our third and final result is to compare K-MD and K-ML estimators in terms of asymptotic mean squared error. Under local misspecification, the optimally weighted K-MD estimator depends on the unknown asymptotic bias and is no longer feasible. In turn, feasible K-MD estimators could have an asymptotic mean squared error that is higher or lower than that of the K-ML estimators. To demonstrate the relevance of our asymptotic analysis, we illustrate our findings using in a simulation exercise based on a misspecified version of Rust (1987) bus engine problem.
The last chapter investigates the causal effect of the Omnibus Budget Reconcil- iation Act of 1993, which caused the biggest change to the EITC in its history, on unemployment and labor force participation among single mothers. Unemployment and labor force participation are difficult to define for a few reasons, for example, be- cause of marginally attached workers. Instead of searching for the unique definition for each of these two concepts, this chapter bounds unemployment and labor force participation by observable variables and, as a result, considers various competing definitions of these two concepts simultaneously. This bounding strategy leads to partial identification of the treatment effect. The inference results depend on the construction of the bounds, but they imply positive effect on labor force participa- tion and negligible effect on unemployment. The results imply that the difference- in-difference result based on the BLS definition of unemployment can be misleading
due to misclassification of unemployment.
Item Open Access Essays on the Econometrics of Dynamic Discrete Choice(2021) Bunting, JacksonDisentangling the causal effect of policy from that ofbehavior---i.e. controlling for selection---is a foundational empirical challenge in economics. Dynamic discrete choice models are a structural approach that posits that selection is driven by forward-looking, optimizing decision makers. The resultant econometric problem is to recover the structural parameters that characterize the model.
This dissertation contributes tothe econometrics of dynamic discrete choice models in several directions. Chapter two shows identification of dynamic discrete choice models with continuous permanent unobserved heterogeneity. That is, the model allows for infintely many persistent unobserved differences between decision making agents. The previous literature allowed for only finitely many types of persistent unobserved differences.
The third chapter provides a hypothesis test for animportant modeling assumption, that of ‘homogeneity’. Commonly, it is assumed that behavior is sufficiently similar across time or markets that data can be pooled across these dimensions. However, this assumption may fail in the presence of a structural break or multiple equilibria. The chapter proposes a hypothesis test to evaluate whether the homogeneity assumption holds in the data. As an approximate randomization test, the hypothesis test is valid even without a large sample.
The fourth chapter provides a computationally advantageous estimator for dynamicdiscrete choice models. The estimator is based on the observation that dynamic discrete choice models possess a multiple index structure. The chapter shows that index sufficiency can be used to construct a set of equality constraints, which restrict the parameter of interest to belong to a subspace of the parameter space. The chapter proposes an estimator that imposes these restrictions, thus attaining computational gains by reducing the effective dimension of the parameter space.