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Daniel Lopez Gomez - Dissertation.pdf (1.25 MB)

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High Dimensional Data Methods in Industrial Organization Type Discrete Choice Models

Lopez Gomez, Daniel Felipe
http://orcid.org/0000-0001-5869-0890
http://rave.ohiolink.edu/etdc/view?acc_num=osu1649961010607273

Abstract Details

2022, Doctor of Philosophy, Ohio State University, Economics.
This dissertation is composed of three main papers. Each of these papers studies a different classical discrete choice model setting within the realm of Industrial Organization (IO) that now has the added complexity of containing a high-dimensional component that renders ineffective the traditional methods used and thus requires alternative approaches. In the first paper, I study a static single equilibrium market entry game of homogenous firms that contains a high-dimensional set of exogenous market characteristics that could enter a firm’s profit function. In such type of high-dimensional setting we are at high risk of overfitting, i.e. estimating model parameters that are tailored too closely to the sample data available and thus don’t generalize well to new data. The focus of this paper is exploring the use of different regularization techniques with the purpose of reducing overfitting when predicting market entry for a previously unobserved market. The second paper extends the previous market entry framework by now examining a static multiple equilibria market entry game of heterogeneous firms. The high-dimensional component in this setting arises from the way in which such a model is partially identified, which is through a set of moment inequalities that have to be met for a particular set of values of the parameters of interest to be consistent with the data. The number of moment inequalities that characterize this type of model can very easily grow beyond traditional sample sizes, thus requiring special attention from the researcher when testing whether a vector of values for the parameters of interest is indeed accepted by the model. This paper studies different approaches of high-dimensional testing applied to this market entry model and evaluates their performance. Finally, in the third paper I consider a different but still extremely relevant model of Industrial Organization, the aggregate discrete choice model with random coefficients for demand of differentiated products. Similar to the first paper, the high-dimensional component here will come from a high-dimensional set of exogenous covariates available for estimation, however, in this setting the non-linear nature of the estimation problem makes dealing with this high-dimensional covariate space much less straightforward. In this paper, I evaluate the performance of a recently introduced algorithm for the estimation of these types of models, as well as propose an extension to this algorithm to allow for more than one single random coefficient in the model specification.
Jason Blevins (Advisor)
Adam Dearing (Committee Member)
Robert de Jong (Committee Member)
118 p.
Economics
High dimensional data, LASSO, regularization, discrete choice models, industrial organization, big data

Recommended Citations

Citations

  • Lopez Gomez, D. F. (2022). High Dimensional Data Methods in Industrial Organization Type Discrete Choice Models [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1649961010607273

    APA Style (7th edition)

  • Lopez Gomez, Daniel. High Dimensional Data Methods in Industrial Organization Type Discrete Choice Models. 2022. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1649961010607273.

    MLA Style (8th edition)

  • Lopez Gomez, Daniel. "High Dimensional Data Methods in Industrial Organization Type Discrete Choice Models." Doctoral dissertation, Ohio State University, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=osu1649961010607273

    Chicago Manual of Style (17th edition)

osu1649961010607273
131
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