Mergers and investments in the wireless industry
Keywords: Business.

This dissertation studies the U.S. wireless telecommunications industry. I propose a novel model, which I then estimate. I analyze potential counterfactual results in the first chapter of my dissertation. Second (joint with Nick Doudchenko) and third (joint with Dmitry Arkhangelsky and Lanier Benkard) chapters develop methodology that is necessary for the estimation of the models alike the one analyzed in the first chapter. The first chapter studies the outcomes of a hypothetical T-Mobile/Sprint and AT& T/T-Mobile mergers in the U.S. wireless telecommunications industry. I propose a model in which consumers trade off price and network coverage, so firms have to compete on both price and investment. The key finding is that had T-Mobile and Sprint merged in 2009, consumers would have benefited from expanded network coverage. The two firms would have increased profits due to less duplication on the investment side. An acquisition of T-Mobile by AT& T, on the other hand, would have harmed consumers because it would not have resulted in better coverage. Additionally, the outcomes of the T-Mobile/Sprint merger vary across geographic areas. Markets with high population density or flat terrain typically have a strong initial Sprint or T-Mobile presence, and would therefore experience lower, often negative, changes in consumer surplus as a result of the merger. Conversely, markets where the merging parties struggle to enter separately, mainly those with lower population density and harder to cover terrain, benefit more because the merger would diversify carrier choices. In the second chapter we propose a new method of estimation for discrete choice demand models when individual level data are available. The method employs a two-step procedure. Step 1 predicts the choice probabilities as functions of the observed individual level characteristics. Step 2 estimates the structural parameters of the model using the estimated choice probabilities at a fixed point and the moment restrictions. In essence, the method uses nonparametric approximation (followed by) moment estimation. Hence the name---NAME. We use simulations to compare the performance of NAME with the standard methodology. We find that our method delivers an improved precision as well as a substantially faster convergence time. We supplement the analysis by providing the large sample properties of the proposed estimator. In the third chapter we investigate the finite sample properties of iterative two-step procedures. We show how iterations of the fixed point equation might reduce the first-order bias in the problem. Based on this, we propose a new iterative estimator that works in games and achieves fully parametric properties even if the initial first stage estimator is not accurate. The estimator has several appealing properties such as bias reduction, stability, and computational feasibility. Some known results, such as iterative estimators by policy function iterations in the single agent dynamic discrete choice models Aguirregabiria and Mira (2002) or recursive projections methods from Kasahara and Shimotsu (2009) are special cases of our corrected procedure. We test the performance of our estimator in several examples via simulations