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Fractional logit estimation under varying spatial resolution

Author

Listed:
  • Jingyu Song

    (Nationwide Mutual Insurance Company)

  • Paul V Preckel

    (Purdue University)

  • Michael S Delgado

    (Purdue University)

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.

Suggested Citation

  • Jingyu Song & Paul V Preckel & Michael S Delgado, 2020. "Fractional logit estimation under varying spatial resolution," Economics Bulletin, AccessEcon, vol. 40(4), pages 2959-2963.
    • Handle: RePEc:ebl:ecbull:eb-20-00633
    as

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    References listed on IDEAS

    as
    1. Papke, Leslie E & Wooldridge, Jeffrey M, 1996. "Econometric Methods for Fractional Response Variables with an Application to 401(K) Plan Participation Rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 619-632, Nov.-Dec..
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    More about this item

    Keywords

    Logit regression; spatial resolution; grid cells;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General

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