<p>This dissertation consists of three chapters relating to</p>
<p>identification and inference in dynamic microeconometric models</p>
<p>including dynamic discrete games with many players, dynamic games with</p>
<p>discrete and continuous choices, and semiparametric binary choice and</p>
<p>duration panel data models.</p>
<p>The first chapter provides a framework for estimating large-scale</p>
<p>dynamic discrete choice models (both single- and multi-agent models)</p>
<p>in continuous time. The advantage of working in continuous time is</p>
<p>that state changes occur sequentially, rather than simultaneously,</p>
<p>avoiding a substantial curse of dimensionality that arises in</p>
<p>multi-agent settings. Eliminating this computational bottleneck is</p>
<p>the key to providing a seamless link between estimating the model and</p>
<p>performing post-estimation counterfactuals. While recently developed</p>
<p>two-step estimation techniques have made it possible to estimate</p>
<p>large-scale problems, solving for equilibria remains computationally</p>
<p>challenging. In many cases, the models that applied researchers</p>
<p>estimate do not match the models that are then used to perform</p>
<p>counterfactuals. By modeling decisions in continuous time, we are able</p>
<p>to take advantage of the recent advances in estimation while</p>
<p>preserving a tight link between estimation and policy experiments. We</p>
<p>also consider estimation in situations with imperfectly sampled data,</p>
<p>such as when we do not observe the decision not to move, or when data</p>
<p>is aggregated over time, such as when only discrete-time data are</p>
<p>available at regularly spaced intervals. We illustrate the power of</p>
<p>our framework using several large-scale Monte Carlo experiments.</p>
<p>The second chapter considers semiparametric panel data binary choice</p>
<p>and duration models with fixed effects. Such models are point</p>
<p>identified when at least one regressor has full support on the real</p>
<p>line. It is common in practice, however, to have only discrete or</p>
<p>continuous, but possibly bounded, regressors. We focus on</p>
<p>identification, estimation, and inference for the identified set in</p>
<p>such cases, when the parameters of interest may only be partially</p>
<p>identified. We develop a set of general results for</p>
<p>criterion-function-based estimation and inference in partially</p>
<p>identified models which can be applied to both regular and irregular</p>
<p>models. We apply our general results first to a fixed effects binary</p>
<p>choice panel data model where we obtain a sharp characterization of</p>
<p>the identified set and propose a consistent set estimator,</p>
<p>establishing its rate of convergence under different conditions.</p>
<p>Rates arbitrarily close to <italic>n<super>-1/3</super></italic> are</p>
<p>possible when a continuous, but possibly bounded, regressor is</p>
<p>present. When all regressors are discrete the estimates converge</p>
<p>arbitrarily fast to the identified set. We also propose a</p>
<p>subsampling-based procedure for constructing confidence regions in the</p>
<p>models we consider. Finally, we carry out a series of Monte Carlo</p>
<p>experiments to illustrate and evaluate the proposed procedures. We</p>
<p>also consider extensions to other fixed effects panel data models such</p>
<p>as binary choice models with lagged dependent variables and duration</p>
<p>models.</p>
<p>The third chapter considers nonparametric identification of dynamic</p>
<p>games of incomplete information in which players make both discrete</p>
<p>and continuous choices. Such models are commonly used in applied work</p>
<p>in industrial organization where, for example, firms make discrete</p>
<p>entry and exit decisions followed by continuous investment decisions.</p>
<p>We first review existing identification results for single agent</p>
<p>dynamic discrete choice models before turning to single-agent models</p>
<p>with an additional continuous choice variable and finally to</p>
<p>multi-agent models with both discrete and continuous choices. We</p>
<p>provide conditions for nonparametric identification of the utility</p>
<p>function in both cases.</p> / Dissertation
| Identifer | oai:union.ndltd.org:DUKE/oai:dukespace.lib.duke.edu:10161/2365 |
| Date | January 2010 |
| Creators | Blevins, Jason Ryan |
| Contributors | Hong, Han, Khan, Shakeeb |
| Source Sets | Duke University |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 874673 bytes, application/pdf |
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