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Panel VAR Models with Spatial Dependence

Author

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  • Mutl, Jan

    (Department of Economics, Institute for Advanced Studies, Vienna, Austria)

Abstract

I consider a panel vector-autoregressive model with cross-sectional dependence of the disturbances characterized by a spatial autoregressive process. I propose a three-step estimation procedure. Its first step is an instrumental variable estimation that ignores the spatial correlation. In the second step, the estimated disturbances are used in a multivariate spatial generalized moments estimation to infer the degree of spatial correlation. The final step of the procedure uses transformed data and applies standard techniques for estimation of panel vector-autoregressive models. I compare the small-sample performance of various estimation strategies in a Monte Carlo study.

Suggested Citation

  • Mutl, Jan, 2009. "Panel VAR Models with Spatial Dependence," Economics Series 237, Institute for Advanced Studies.
    • Handle: RePEc:ihs:ihsesp:237
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    File URL: https://irihs.ihs.ac.at/id/eprint/1916
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    References listed on IDEAS

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    1. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
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    Citations

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    Cited by:

    1. Arturas Juodis, 2013. "Cointegration Testing in Panel VAR Models Under Partial Identification and Spatial Dependence," UvA-Econometrics Working Papers 13-08, Universiteit van Amsterdam, Dept. of Econometrics.
    2. Herrera Gómez, Marcos & Ruiz Marín, Manuel & Mur Lacambra, Jesús, 2014. "Testing Spatial Causality in Cross-section Data," MPRA Paper 56678, University Library of Munich, Germany.
    3. Gerald Carlino & Thorsten Drautzburg, 2020. "The role of startups for local labor markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 751-775, September.
    4. Artūras Juodis, 2018. "Rank based cointegration testing for dynamic panels with fixed T," Empirical Economics, Springer, vol. 55(2), pages 349-389, September.
    5. Marcos Herrera & Jesús Mur & Manuel Ruiz, 2016. "Detecting causal relationships between spatial processes," Papers in Regional Science, Wiley Blackwell, vol. 95(3), pages 577-594, August.
    6. Yang, Kai & Lee, Lung-fei, 2021. "Estimation of dynamic panel spatial vector autoregression: Stability and spatial multivariate cointegration," Journal of Econometrics, Elsevier, vol. 221(2), pages 337-367.
    7. Ana Angulo & F. Trívez, 2010. "The impact of spatial elements on the forecasting of Spanish labour series," Journal of Geographical Systems, Springer, vol. 12(2), pages 155-174, June.
    8. Arturas Juodis, 2013. "First Difference Transformation in Panel VAR models: Robustness, Estimation and Inference," UvA-Econometrics Working Papers 13-06, Universiteit van Amsterdam, Dept. of Econometrics.
    9. Timo MITZE & Björn ALECKE & Gerhard UNTIEDT, 2008. "Determinants of Interregional Migration Among German States and its Implications for Reducing East-West Disparities: Results from a Panel VAR Using Efficient GMM Estimation," EcoMod2008 23800089, EcoMod.

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    More about this item

    Keywords

    Spatial PVAR; Multivariate dynamic panel data model; Spatial GM; Spatial Cochrane-Orcutt transformation; Constrained maximum likelihood estimation;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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