Modeling Conditional Heteroskedasticity in Time Series and Spatial Analysis

136 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.My fourth chapter investigates heterogeneity in the assessment of spatial dependence by exploring (jointly) two main mechanisms: distributional misspecification and conditional heteroskedasticity. I first derive a simple specification test for spatial autoregressive model using the information matrix (IM) test principle. As a byproduct of my test development, I obtain a general model that has similar features like autoregressive conditional heteroskedasticity (ARCH) in time series context. My suggested spatial ARCH (SARCH) model can take account of some of the stylized facts observed in spatial data. To illustrate the usefulness of our test and SARCH model, I apply our theoretical result to Boston housing price data and show the importance of modeling the conditional second moment in spatial context.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD