Three Essays on Spatial Econometrics and Empirical Industrial Organization

Three Essays on Spatial Econometrics and Empirical Industrial Organization
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Total Pages : 117
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ISBN-10 : OCLC:248016166
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Book Synopsis Three Essays on Spatial Econometrics and Empirical Industrial Organization by : Sang-Yeob Lee

Download or read book Three Essays on Spatial Econometrics and Empirical Industrial Organization written by Sang-Yeob Lee and published by . This book was released on 2008 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: The first essay explores the consequences of misspecified spatial interdependence structure in SAR models with a row-normalized weight matrix. I provide the analytical formulae for the asymptotic biases of the OLS estimator when a spatial weight matrix is over-specified, under-specified, or omitted in a simple linear regression model. I then design Monte Carlo experiments to study how a misspecified spatial weight matrix in the SAR model might impact the finite sample properties of the 2SLSE and MLE. The major finding is that an "over-specification" of the weight matrix causes less bias in 2SLSE and MLE as well as lower RMSE than an "under-specification." The results also strongly suggest that goodness of fit measures such as adjusted R-square and log-likelihood can serve as selection criteria for the choice of a spatial weight matrix. In the second essay, I consider the effectiveness of Wald, distance difference, minimum Chi-square, and gradient tests within GMM framework in selecting different specifications of spatial weights in SAR models. The two major results I obtain are (1) that for each of the five tests, GMM framework significantly improves the empirical power of the tests over 2SLS framework, and (2) that when performed in GMM framework, all five tests have suitable empirical size and power with similar performance outcomes. Finally, the third essay investigates the nature of competition in the retail gasoline market using a two year panel data of weekly prices for gas stations in San Diego County. I use IV methods to estimate several spatial autoregressive (SAR) models of stations' price reaction functions after specifying spatial weights based on distance between stations. By using the SAR model, I am able to identify that the brand of competing stations and their relative geographic proximity to the original station are important factors in explaining price variation across gasoline stations, as opposed to just the number of competing stations.

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My job market paper, "Bayesian Estimation of a Spatial Autoregressive Model with an Unobserved Endogenous Spatial Weight Matrix and Unobserved Factors", examine