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Bera, Anil K. Simlai, Pradosh Kumar 2015-09-25T22:47:27Z 2015-09-25T22:47:27Z 10000-01-01 2006 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. Made available in DSpace on 2015-09-25T22:47:27Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3242994.pdf: 3348547 bytes, checksum: 29d63209eac06c6936b3e6385a1e8200 (MD5) Previous issue date: 2006 Embargo set by: Seth Robbins for item 86856 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 136 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006. http://hdl.handle.net/2142/85575 (MiAaPQ)AAI3242994 eng Economics, Theory Modeling Conditional Heteroskedasticity in Time Series and Spatial Analysis text Economics Economics University of Illinois at Urbana-Champaign Dissertation Ph.D.