Fractional logit estimation under varying spatial resolution

Jingyu Song (), Paul Preckel () and Michael Delgado ()
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Jingyu Song: Nationwide Mutual Insurance Company
Paul Preckel: Purdue University
Michael Delgado: Purdue University

Economics Bulletin, 2020, vol. 40, issue 4, 2959-2963

Abstract: We propose a method for estimating logit regression models in the case that the independent variables are measured at a finer-scale spatial resolution than the dependent variable. Whereas the traditional approach is to aggregate the fine-scale data to the resolution of the dependent variable prior to estimation, we propose integrating the aggregation directly into the regression so as to maximize the value of information contained at the fine-scale resolution. Monte Carlo simulations show reasonable finite sample performance and that the traditional approach is biased. Our estimator is applicable in many cases that use remotely sensed or GIS data, such as land use problems.

Keywords: Logit regression; spatial resolution; grid cells (search for similar items in EconPapers)
JEL-codes: C1 Q0 (search for similar items in EconPapers)
Date: 2020-11-14
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