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Interpreting Dynamic Space-Time Panel Data Models

Author

Listed:
  • Nicolas Debarsy

    (CERPE - Centre de Recherches en Economie Régionale et Politique Economique - FUNDP - Facultés Universitaires Notre Dame de la Paix)

  • Cem Ertur

    (LEO - Laboratoire d'économie d'Orleans [2008-2011] - UO - Université d'Orléans - CNRS - Centre National de la Recherche Scientifique)

  • James P. Lesage

    (Texas State University)

Abstract

There is a great deal of literature regarding the asymptotic properties of various approaches to estimating simultaneous space-time panel models, but little attention has been paid to how the model estimates should be interpreted. The motivation for use of space-time panel models is that they can provide us with information not available from cross-sectional spatial regressions. LeSage and Pace (2009) show that cross-sectional simultaneous spatial autoregressive models can be viewed as a limiting outcome of a dynamic space-time autoregressive process. A valuable aspect of dynamic space-time panel data models is that the own- and cross-partial derivatives that relate changes in the explanatory variables to those that arise in the dependent variable are explicit. This allows us to employ parameter estimates from these models to quantify dynamic responses over time and space as well as space-time diffusion impacts. We illustrate our approach using the demand for cigarettes over a 30 year period from 1963-1992, where the motivation for spatial dependence is a bootlegging effect where buyers of cigarettes near state borders purchase in neighboring states if there is a price advantage to doing so.

Suggested Citation

  • Nicolas Debarsy & Cem Ertur & James P. Lesage, 2012. "Interpreting Dynamic Space-Time Panel Data Models," Post-Print hal-00525740, HAL.
    • Handle: RePEc:hal:journl:hal-00525740
    DOI: 10.1016/j.stamet.2011.02.002
    Note: View the original document on HAL open archive server: https://hal.science/hal-00525740
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    References listed on IDEAS

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    1. Yu, Jihai & de Jong, Robert & Lee, Lung-fei, 2008. "Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and T are large," Journal of Econometrics, Elsevier, vol. 146(1), pages 118-134, September.
    2. Kelejian, Harry H. & Prucha, Ingmar R., 2010. "Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances," Journal of Econometrics, Elsevier, vol. 157(1), pages 53-67, July.
    3. Baltagi, Badi H & Levin, Dan, 1986. "Estimating Dynamic Demand for Cigarettes Using Panel Data: The Effects of Bootlegging, Taxation and Advertising Reconsidered," The Review of Economics and Statistics, MIT Press, vol. 68(1), pages 148-155, February.
    4. Badi H. Baltagi & Dong Li, 2004. "Prediction in the Panel Data Model with Spatial Correlation," Advances in Spatial Science, in: Luc Anselin & Raymond J. G. M. Florax & Sergio J. Rey (ed.), Advances in Spatial Econometrics, chapter 13, pages 283-295, Springer.
    5. Giuseppe Arbia & Badi H. Baltagi (ed.), 2009. "Spatial Econometrics," Studies in Empirical Economics, Springer, number 978-3-7908-2070-6, July.
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