Browsing by Subject "Dynamic games"

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 to move, 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 data models 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-agent models with both discrete and continuous choices. We

provide conditions for nonparametric identification of the utility

function in both cases.

Chapter 1: Although there are widely accepted theoretical explanations for overexploitation of common-pool resources, empirically we have limited information about the micro-level mechanisms that cause individually efficient exploitation to result in macro inefficiency. This paper conducts the first empirical investigation of common-pool resource users' dynamic and strategic behavior at the micro level. With an application to the North Carolina shrimp fishery, we examine fishermen's strategies in a fully dynamic game that accounts for latent resource dynamics and other players' actions. Combining a simulation-based Conditional Choice Probability estimator and a Pseudo Maximum Likelihood estimator, we recover the profit structure of the fishery from fishermen's repeated choices. Using the estimated structural parameters, we compare the fishermen's actual exploitation path to the socially optimal one under a time-specific limited entry system with transferrable permits, and then quantify the dynamic efficiency costs of common-pool resource use. We find that individual fishermen respond to other users by exerting a higher level of exploitation effort than what is socially optimal. Based on our counterfactual experiments, we estimate the efficiency costs of this behavior to be 17.39\% of the annual revenues from the fishery, which translates into 31.4\% of the rent without deducting the cost of capital.

Chapter 2: Although hypoxia is a threat to coastal ecosystems, policy makers have limited information about the potential economic impacts on fisheries. Studies using spatially and temporally aggregated data generally fail to detect statistically significant fishery effects of hypoxia. Limited recent work using disaggregated fishing data (microdata) reports modest effects of hypoxia on catches of recreationally harvested species. These prior studies have not accounted for important spatial and temporal aspects of the system, however. For example, the effects of hypoxia on catches may not materialize instantaneously but instead may involve a lagged process with catches reflecting cumulative past exposure to environmental conditions. This paper develops a differenced bioeconomic model to account for the lagged effects of hypoxia on the North Carolina brown shrimp fishery. It integrates high-resolution oxygen monitoring data with fishery-dependent microdata from North Carolina's trip ticket program to investigate the detailed spatial and temporal relationships of hypoxia to commercial fishery harvest. The main finding is that hypoxia potentially resulted in a 12.9\% annual decrease in brown shrimp harvest from 1999-2005. The paper also develops two alternative models---a non-differenced model and a polynomial distributed lag model---and results are consistent with the main model.

Chapter 3: The emergence of ecosystem-based management suggests that traditional fisheries

management and protection of environmental quality are increasingly interrelated. Fishery managers, however, have limited control over most sources of marine and estuarine pollution and at best can only adapt to environmental conditions. We develop a bioeconomic model of optimal harvest of an annual species that is subject to an environmental disturbance. We parameterize the model to analyze the effect of hypoxia (low dissolved oxygen) on the optimal harvest path of brown shrimp, a commercially important species that is fished in hypoxic waters in the Gulf of Mexico and in estuaries in the southeastern United States. We find that hypoxia alters the qualitative pattern of optimal harvest and shifts the season opening earlier in the year; more severe hypoxia leads to even earlier season openings. However, the quantitative effects of adapting fishery management to hypoxia in terms of fishery rents are small. This suggests that it is critical for other regulatory agencies to control estuarine pollution, and fishery managers need to generate value from the fishery resources through other means such as rationalization.