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Maximum Likelihood Estimation

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Handbook of Regional Science

Abstract

Maximum likelihood estimation has been the standard method employed for estimating spatial econometric models. This chapter introduces these methods, examines the specific case of a spatial error model, and provides an example based on a large data set. In addition, the chapter sets forth various solutions to the computational difficulties that arise for large data sets.

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Acknowledgments

I would like to thank Mark Mclean, James LeSage, and Shuang Zhu for their very helpful comments.

Author information

Authors and Affiliations

  1. Department of Finance, E.J. Ourso College of Business Administration, Louisiana State University, Baton Rouge, LA, 70803-6308, USA

    R. Kelley Pace (LREC Endowed Chair of Real Estate)

Authors
  1. R. Kelley Pace
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Correspondence to R. Kelley Pace .

Editor information

Editors and Affiliations

  1. Institute for Economic Geography and GIScience, Vienna University of Economics and Business, Vienna, Austria

    Manfred M. Fischer

  2. and Business Administration Department of Spatial Economics, Free University Faculty of Economics, Amsterdam, The Netherlands

    Peter Nijkamp

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Pace, R.K. (2014). Maximum Likelihood Estimation. In: Fischer, M., Nijkamp, P. (eds) Handbook of Regional Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23430-9_88

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  • DOI: https://doi.org/10.1007/978-3-642-23430-9_88

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23429-3

  • Online ISBN: 978-3-642-23430-9

  • eBook Packages: Business and Economics

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