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Essays on the Analysis of High-Dimensional Dynamic Games and Data Combination

Legacy carriers in the U.S. airline industry have a long history of vigorously defending their most important hubs from low cost carrier expansion. Since 2005, the U.S. airline industry has undergone some of the most dramatic merger activity in its history, with five mergers between major carriers reducing the number of major carriers from eight to four. This merger activity has coincided with low cost carrier expansion into some hubs previously dominated by legacy carriers. This dissertation quantifies how mergers change the incentives of incumbent legacy carriers to accommodate new entry. A technical challenge in doing so lies in the well-known âcurse of dimensionalityâ for modeling dynamic strategic competition, which is especially prohibitive in the context of network industries (including airlines). In the airline context, this curse is induced by the high-dimensionality of airline networks, since firms make simultaneous decisions about route structures, flight frequencies, and prices across thousands of markets. We solve this challenge by proposing a novel method for studying high-dimensional dynamic strategic competition which combines tools from machine learning, the econometrics of dynamic games, and approximate dynamic programming. Using this tool to analyze the Delta and Northwest merger and Southwest Airlineâs entry patterns, we find evidence that Southwest was more likely to enter flight segments where, from Delta and Northwestâs perspective, the expected value of committing aircraft capacity, relative to other flight segments, fell the most post-merger. Outside of the airline context, we further illustrate this method by studying a dynamic spatial store placement game among big box retailers (including Walmart), extending the analysis of Holmes (2011). Finally, in an unrelated context, we systematically study the identification of the average treatment effect on the treated under the difference-in-differences design in the context of repeated cross-sectional data when post-treatment treatment status is unknown for the pre-treatment sample. We illustrate our approach by estimating the effect of the Americans with Disabilities Act of 1991 on employment outcomes of the disabled.

Identiferoai:union.ndltd.org:VANDERBILT/oai:VANDERBILTETD:etd-05202016-231108
Date24 June 2016
CreatorsManzanares, Carlos Andrew
ContributorsAlejandro Molnar, Patrick Bajari, Andrea Moro, Yanqin Fan, Tong Li
PublisherVANDERBILT
Source SetsVanderbilt University Theses
LanguageEnglish
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.library.vanderbilt.edu/available/etd-05202016-231108/
Rightsunrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Vanderbilt University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.

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