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Markov Chain Monte Carlo estimation of spatial dynamic panel models for large samples

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  • LeSage, James P.
  • Chih, Yao-Yu
  • Vance, Colin

Abstract

Focus is on efficient estimation of a dynamic space–time panel data model that incorporates spatial dependence, temporal dependence, as well as space–time covariance and can be implemented where there are a large number of spatial units and time periods. Quasi-maximum likelihood (QML) estimation in cases involving large samples poses computational challenges because optimizing the (log) likelihood requires: (1) evaluating the log-determinant of a large matrix that appears in the likelihood, (2) imposing stability restrictions on parameters reflecting space–time dynamics, and (3) simulations to produce an empirical distribution of the partial derivatives used to interpret model estimates that require numerous inversions of large matrices.

Suggested Citation

  • LeSage, James P. & Chih, Yao-Yu & Vance, Colin, 2019. "Markov Chain Monte Carlo estimation of spatial dynamic panel models for large samples," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 107-125.
    • Handle: RePEc:eee:csdana:v:138:y:2019:i:c:p:107-125
    DOI: 10.1016/j.csda.2019.04.003
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    16. LeSage, James P. & Vance, Colin & Chih, Yao-Yu, 2017. "A Bayesian heterogeneous coefficients spatial autoregressive panel data model of retail fuel duopoly pricing," Regional Science and Urban Economics, Elsevier, vol. 62(C), pages 46-55.
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    Cited by:

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    2. Manfred M. Fischer & James P. LeSage, 2020. "Network dependence in multi-indexed data on international trade flows," Journal of Spatial Econometrics, Springer, vol. 1(1), pages 1-26, December.
    3. Bergantino, Angela S. & Capozza, Claudia & Intini, Mario, 2020. "Empirical investigation of retail fuel pricing: The impact of spatial interaction, competition and territorial factors," Energy Economics, Elsevier, vol. 90(C).
    4. Badi H. Baltagi & Georges Bresson & Anoop Chaturvedi & Guy Lacroix, 2022. "Robust Dynamic Space-Time Panel Data Models Using ε-contamination: An Application to Crop Yields and Climate Change," Center for Policy Research Working Papers 254, Center for Policy Research, Maxwell School, Syracuse University.
    5. Burnett, J. Wesley & Lacombe, Donald J. & Wallander, Steven, . "Spatial and Temporal Spillovers in US Cropland Values," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 49(1).
    6. Meseret Chanieabate & Hai He & Chuyue Guo & Betelhem Abrahamgeremew & Yuanji Huang, 2023. "Examining the Relationship between Transportation Infrastructure, Urbanization Level and Rural-Urban Income Gap in China," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    7. Edoardo Baldoni & Roberto Esposti, 2021. "Agricultural Productivity in Space: an Econometric Assessment Based on Farm‐Level Data," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(4), pages 1525-1544, August.
    8. Badi H. Baltagi & Georges Bresson & Anoop Chaturvedi & Guy Lacroix, 2023. "Robust dynamic space–time panel data models using $$\varepsilon $$ ε -contamination: an application to crop yields and climate change," Empirical Economics, Springer, vol. 64(6), pages 2475-2509, June.

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    More about this item

    Keywords

    Spatial; Time dependence; Dynamic panels; Log-marginal likelihood;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • D40 - Microeconomics - - Market Structure, Pricing, and Design - - - General

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