Abstract
Even small cities have complicated spatial patterns that are difficult to model adequately with a small number of explanatory variables. Shopping centers, parks, lakes, and the like have local effects on variables such as housing prices, land values, and population density. Proximity to such sites can be included as explanatory variables, but the number of potential sites is large and some may be unknown beforehand. Coefficient estimates are biased when relevant sites are omitted, but are inefficient when unimportant ones are included. Moreover, functional forms are often complex for urban spatial patterns even in the absence of local peaks and valleys.
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© 2004 Springer-Verlag Berlin Heidelberg
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McMillen, D.P., McDonald, J.F. (2004). Locally Weighted Maximum Likelihood Estimation: Monte Carlo Evidence and an Application. In: Anselin, L., Florax, R.J.G.M., Rey, S.J. (eds) Advances in Spatial Econometrics. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-05617-2_10
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DOI: https://doi.org/10.1007/978-3-662-05617-2_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-07838-5
Online ISBN: 978-3-662-05617-2
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