https://doi.org/10.54718/ZGQR6237

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Theses and Dissertations

Issuing Body

Mississippi State University

Advisor

Harri, Ardian

Committee Member

Li, Xiaofei

Committee Member

Park, Eunchun

Date of Degree

12-10-2021

Document Type

Graduate Thesis - Open Access

Major

Agricultural Economics

Degree Name

Master of Science (M.S.)

College

College of Agriculture and Life Sciences

Department

Department of Agricultural Economics

Abstract

This study investigates and quantifies the effect of different specifications of the spatial weights matrix (��) on estimates and inferences in the context of a regression model using the lattice perspective with polygon-type data. The study also investigates an alternative to the specification of �� by estimating a spatial variance-covariance matrix based on known features of the spatial data. Previous literature has addressed the a priori construction of �� and selection criteria but assumes point-type data. This study’s primary contribution is the setup of a true and known benchmark that allows the comparison of the different specifications of ��. This is accomplished by using a disaggregate point-type data generating process which is then aggregated into polygon-type data. Monte Carlo simulations show that current specifications of �� used in maximum likelihood estimation for the spatial error model perform poorly. Additionally, the estimated spatial variance-covariance matrix outperforms the traditional specifications of ��.

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