| This dissertation consists of three essays, two on estimating dynamic entry games and one on the inference for misspecified models with fixed regressors.;Big box retail stores have large impact on local economies and receive large subsidies from local governments. Hence it is important to understand how discount retail chains choose store locations. In the first two essays, I study the entry decisions of those firms, examine the role of preemptive incentives, and evaluate the impact of government subsidies on those decisions. To quantify preemptive incentives, I model firms' entry decisions using a dynamic duopoly location game. Stores compete over the shopping-dollars of close-by consumers, making store profitability spatially interdependent. I use separability and two-stage budgeting to reduce the state space of the game and make the model tractable. Instead of adopting census geographic units, I infer market divisions from data using a clustering algorithm built on separability conditions. I introduce a `rolling window' approximation to compute the value function and estimate the parameters of the game. The results suggest that preemptive incentives are important in chain stores' location decisions and that they lead to loss of production efficiency. On average, the combined sum of current and future profits of the two firms is lowered by 1 million dollars per store. Finally, I assess the impact of government subsidies to encourage entry when one retailer exits, as happened in the recent crisis. I find that although the welfare loss such exits cause on local economies can be substantial, the average size of observed subsidies is not enough to affect firms' entry decisions. This study is organized as follows. In the first essay, I provide descriptive evidence of preemptive entry in the discount retail industry and explain how I model firms' entry decisions. In the second essay, I describe the estimation strategy and present the counterfactual analyses.;The third essay is joint work with Alberto Abadie and Guido Imbens. Following the work by Eicker (1967), Huber (1967) and White (1980ab; 1982) it is common in empirical work to report standard errors that are robust against general misspecification. In a regression setting these standard errors are valid for the parameter that minimizes the squared difference between the conditional expectation and the linear approximation, averaged over the population distribution of the covariates. In this essay, we discuss an alternative parameter that corresponds to the approximation to the conditional expectation based on minimization of the squared difference averaged over the sample, rather than the population, distribution of the covariates. We argue that in some cases this may be a more interesting parameter. We derive the asymptotic variance for this parameter, which is generally smaller than the Eicker-Huber-White robust variance, and propose a consistent estimator for this asymptotic variance. |
