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Econometrics on interactions-based models: methods and applications

Liu, Xiaodong
http://rave.ohiolink.edu/etdc/view?acc_num=osu1180283230

Abstract Details

2007, Doctor of Philosophy, Ohio State University, Economics.
My dissertation research emphasizes estimation methods in evaluating the extent of social, strategic and spatial interactions among economic agents. My first essay, based on my joint research with Lung-fei Lee and John Kagel, generalizes Heckman’s (1981) dynamic discrete-choice panel data models by introducing time-lagged social interactions and proposes simulation based methods to implement the maximum likelihood estimation. We use this generalized model to investigate learning from peers in experimental signaling games. We find that subjects’ decisions are significantly influenced by the past decisions of their peers in the experiment. Hence the imitation of peers’ strategies is an important component of the learning process of strategic play. My second essay explores the robustness of Guerre, Perrigne and Vuong’s (2000) two-step nonparametric estimation procedure in first-price sealed-bid auctions with a large number of risk-averse bidders. With an asymptotic approximation of the intractable equilibrium bidding function of risk-averse bidders, I demonstrate that Guerre et al.’s two-step nonparametric estimator based on the equilibrium bidding behavior of risk-neutral bidders is still uniformly consistent even if bidders are risk-averse as long as the number of players in an auction is sufficiently large and derive the uniform convergence rate of the estimator. Furthermore, I show in Monte Carlo experiments that the two-step nonparametric estimator performs reasonably well with a moderate number of risk-averse bidders like six. In my third essay, which is based on my joint research with Lung-fei Lee and Christopher Bollinger, we consider the GMM estimation of the regression model with spatial autoregressive disturbances and the mixed-regressive spatial autoregressive model. We derive the best GMM estimator within the class of GMM estimators that are based on linear and quadratic moment conditions. Our best GMM estimator has the merit of computational simplicity and asymptotic efficiency. We show that it is asymptotically as efficient as the conventional maximum likelihood estimator under normality and asymptotically more efficient than the quasi-maximum likelihood estimator when the normality assumption does not hold. We show in Monte Carlo studies that, with moderate sample sizes, the proposed best GMM estimator has its biggest advantage when the disturbances are asymmetrically distributed.
Lung-fei Lee (Advisor)
Economics, General
Peer Group Effects; Discrete Choice Dynamic Panel Data Models; Simulated Maximum Likelihood; First-price Auctions; Independent Private Value; Nonparametric Estimation; Risk Aversion; Spatial Autoregressive Models; Generalized Method of Moments

Recommended Citations

Citations

  • Liu, X. (2007). Econometrics on interactions-based models: methods and applications [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1180283230

    APA Style (7th edition)

  • Liu, Xiaodong. Econometrics on interactions-based models: methods and applications. 2007. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1180283230.

    MLA Style (8th edition)

  • Liu, Xiaodong. "Econometrics on interactions-based models: methods and applications." Doctoral dissertation, Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=osu1180283230

    Chicago Manual of Style (17th edition)

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© 2007, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.