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Abstract Details

Activity Number: 212
Type: Invited
Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
Sponsor: Business and Economic Statistics Section
Abstract - #303540
Title: Quantifying Spatial Spillovers Using Simulated Maximum Likelihood Spatial Probit Model Estimates
Author(s): James Paul LeSage*+ and Ronald Kelley Pace and Shuang Zhu
Companies: Texas State University and Louisiana State University and Louisiana State University
Address: Department of Finance & Economics, San Marcos, TX, 78666,
Keywords: sparsity ; mortgage defaults ; spatial dependence ; separable models ; global spillovers ; local spillovers

Spillovers are a feature of spatial econometric models, since economists are interested in quantifying these. We discuss modeling local and global spatial spillovers in probit models, where focus is on decisions made by agents located at points in space. Mortgage default decisions by homeowners are used to illustrate outcomes that depend on unobservable market prices of homes, which exhibit spatial correlation. This leads to a probit model that includes mortgage and homeowner characteristics of neighboring properties that produce spatial spillovers. Spillovers are defined as changes in characteristics of a property or homeowner that produce a change in the probability of default for neighboring properties. Conventional probit models assume decisions are independent with respect to changing characteristics of other observations. Spillovers have important implications for assessing spatial risks of real estate portfolios, costs and benefits of mortgage modification programs, etc. Fast simulated maximum likelihood estimation approaches for large samples are discussed that exploit sparsity.

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