Essays in industrial organization and econometrics

• Main Author: Blevins, Jason R
• Format: Dissertation
• Language: English
• Published: ProQuest Dissertations & Theses 01-01-2010
• Subjects: Economics
• ISBN: 9781109712322; 1109712324

This dissertation consists of three chapters relating to identification and inference in dynamic microeconometric models including dynamic discrete games with many players, dynamic games with discrete and continuous choices, and semiparametric binary choice and duration panel data models. The first chapter provides a framework for estimating large-scale dynamic discrete choice models (both single- and multi-agent models) in continuous time. The advantage of working in continuous time is that state changes occur sequentially, rather than simultaneously, avoiding a substantial curse of dimensionality that arises in multi-agent settings. Eliminating this computational bottleneck is the key to providing a seamless link between estimating the model and performing post-estimation counterfactuals. While recently developed two-step estimation techniques have made it possible to estimate large-scale problems, solving for equilibria remains computationally challenging. In many cases, the models that applied researchers estimate do not match the models that are then used to perform counterfactuals. By modeling decisions in continuous time, we are able to take advantage of the recent advances in estimation while preserving a tight link between estimation and policy experiments. We also consider estimation in situations with imperfectly sampled data, such as when we do not observe the decision not tomove, or when data is aggregated over time, such as when only discrete-time data are available at regularly spaced intervals. We illustrate the power of our framework using several large-scale Monte Carlo experiments. The second chapter considers semiparametric panel data binary choice and duration models with fixed effects. Such models are point identified when at least one regressor has full support on the real line. It is common in practice, however, to have only discrete or continuous, but possibly bounded, regressors. We focus on identification, estimation, and inference for the identified set in such cases, when the parameters of interest may only be partially identified. We develop a set of general results for criterion-function based estimation and inference in partially identified models which can be applied to both regular and irregular models. We apply our general results first to a fixed effects binary choice panel data model where we obtain a sharp characterization of the identified set and propose a consistent set estimator, establishing its rate of convergence under different conditions. Rates arbitrarily close to n–1/3 are possible when a continuous, but possibly bounded, regressor is present. When all regressors are discrete the estimates converge arbitrarily fast to the identified set. We also propose a subsampling-based procedure for constructing confidence regions in the models we consider. Finally, we carry out a series of Monte Carlo experiments to illustrate and evaluate the proposed procedures. We also consider extensions to other fixed effects panel datamodels such as binary choice models with lagged dependent variables and duration models. The third chapter considers nonparametric identification of dynamic games of incomplete information in which players make both discrete and continuous choices. Such models are commonly used in applied work in industrial organization where, for example, firms make discrete entry and exit decisions followed by continuous investment decisions. We first review existing identification results for single agent dynamic discrete choice models before turning to single-agent models with an additional continuous choice variable and finally to multi-agentmodels with both discrete and continuous choices. We provide conditions for nonparametric identification of the utility function in both cases.