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A generalized framework for estimating spatial econometric interaction models

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  • Dargel, Lukas
  • Thomas-Agnan, Christine

Abstract

In the framework of spatial econometric interaction models for origin-destination flows, we develop an estimation method for the case when the list of origins may be distinct from the list of destinations, and when the origin-destination matrix may be sparse. The proposed model resembles a weighted version of the one of LeSage and Pace (2008) and we are able to retain most of the efficiency gains associated with the matrix form estimation, which we illustrate for the maximum likelihood estimator. We also derive computationally feasible tests for the coherence of the estimation results and present an efficient approximation of the conditional expectation of the flows.

Suggested Citation

  • Dargel, Lukas & Thomas-Agnan, Christine, 2022. "A generalized framework for estimating spatial econometric interaction models," TSE Working Papers 22-1312, Toulouse School of Economics (TSE).
    • Handle: RePEc:tse:wpaper:126685
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    References listed on IDEAS

    as
    1. Lukas Dargel, 2021. "Revisiting estimation methods for spatial econometric interaction models," Journal of Spatial Econometrics, Springer, vol. 2(1), pages 1-41, December.
    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. Martijn Burger & Frank van Oort & Gert-Jan Linders, 2009. "On the Specification of the Gravity Model of Trade: Zeros, Excess Zeros and Zero-inflated Estimation," Spatial Economic Analysis, Taylor & Francis Journals, vol. 4(2), pages 167-190.
    4. Bergstrand, Jeffrey H, 1985. "The Gravity Equation in International Trade: Some Microeconomic Foundations and Empirical Evidence," The Review of Economics and Statistics, MIT Press, vol. 67(3), pages 474-481, August.
    5. Lukas Dargel, 2021. "Revisiting estimation methods for spatial econometric interaction models," Post-Print hal-03460656, HAL.
    6. Kerkman, Kasper & Martens, Karel & Meurs, Henk, 2018. "Predicting travel flows with spatially explicit aggregate models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 68-88.
    7. Dargel, Lukas, 2021. "Revisiting Estimation Methods for Spatial Econometric Interaction Models," TSE Working Papers 21-1192, Toulouse School of Economics (TSE).
    8. R. Kelley Pace & James P. Lesage & Shuang Zhu, 2013. "Interpretation and Computation of Estimates from Regression Models using Spatial Filtering," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(3), pages 352-369, September.
    9. Tamás Krisztin & Manfred M. Fischer, 2015. "The Gravity Model for International Trade: Specification and Estimation Issues," Spatial Economic Analysis, Taylor & Francis Journals, vol. 10(4), pages 451-470, December.
    10. Xu, Xingbai & Lee, Lung-fei, 2015. "Maximum likelihood estimation of a spatial autoregressive Tobit model," Journal of Econometrics, Elsevier, vol. 188(1), pages 264-280.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    spatial econometric interaction models; zero flow problem;

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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