关键词：
Economics
General
continuous time
discrete choice
dynamic games
nonparametric identification
panel data
partial identification
摘要：
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
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