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A Spatial Dynamic Panel Model with Random Effects Applied to Commuting Times

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  • Olivier Parent
  • James P. Lesage

    ()

Abstract

A space-time filter is set forth for spatial panel data situations that include random effects. We propose a general spatial dynamic specification that encompasses several spatiotemporal models previously used in the panel data literature. We apply the model to the case of highway induced travel demand. The theory of induced travel demand asserts that increased highway capacity will induce growth in traffic for a number of reasons. Our model allows us to quantify the spatial spillover impacts of increased highway capacity at one location in the network on travel times in neighboring locations and in future time periods.

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Bibliographic Info

Paper provided by University of Cincinnati, Department of Economics in its series University of Cincinnati, Economics Working Papers Series with number 2010-01.

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Length: 24 pages
Date of creation: 2010
Date of revision:
Handle: RePEc:cin:ucecwp:2010-01

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References

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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. Koop, Gary M & Poirier, Dale J & Tobias, Justin, 2007. "Bayesian Econometric Methods," Staff General Research Papers 12722, Iowa State University, Department of Economics.
  3. Parent, Olivier & LeSage, James P., 2012. "Spatial dynamic panel data models with random effects," Regional Science and Urban Economics, Elsevier, vol. 42(4), pages 727-738.
  4. Song, Yan & Zenou, Yves, 2005. "Property Tax and Urban Sprawl. Theory and Implications for U.S. Cities," CEPR Discussion Papers 5345, C.E.P.R. Discussion Papers.
  5. van Ommeren, Jos & Fosgerau, Mogens, 2008. "Workers' marginal costs of commuting," MPRA Paper 12010, University Library of Munich, Germany.
  6. Garrido, Rodrigo A., 2000. "Spatial interaction between the truck flows through the Mexico-Texas border," Transportation Research Part A: Policy and Practice, Elsevier, vol. 34(1), pages 23-33, January.
  7. Noland, Robert B., 2001. "Relationships between highway capacity and induced vehicle travel," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(1), pages 47-72, January.
  8. Baltagi, Badi H. & Heun Song, Seuck & Cheol Jung, Byoung & Koh, Won, 2007. "Testing for serial correlation, spatial autocorrelation and random effects using panel data," Journal of Econometrics, Elsevier, vol. 140(1), pages 5-51, September.
  9. Kim, Chansung, 2008. "Commuting time stability: A test of a co-location hypothesis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(3), pages 524-544, March.
  10. Parry, Ian & Small, Kenneth, 2002. "Does Britain or the United States Have the Right Gasoline Tax?," Discussion Papers dp-02-12-, Resources For the Future.
  11. Kirby, Dustin K. & LeSage, James P., 2009. "Changes in commuting to work times over the 1990 to 2000 period," Regional Science and Urban Economics, Elsevier, vol. 39(4), pages 460-471, July.
  12. Shirley, Chad & Winston, Clifford, 2004. "Firm inventory behavior and the returns from highway infrastructure investments," Journal of Urban Economics, Elsevier, vol. 55(2), pages 398-415, March.
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Citations

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Cited by:
  1. J. Elhorst, 2012. "Dynamic spatial panels: models, methods, and inferences," Journal of Geographical Systems, Springer, vol. 14(1), pages 5-28, January.
  2. Romain Houssa, 2010. "Spatial Propagation of Macroeconomic Shocks in Europe," Working Papers 1009, University of Namur, Department of Economics.
  3. Parent, Olivier & LeSage, James P., 2011. "A space-time filter for panel data models containing random effects," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 475-490, January.
  4. Baylis, Katherine R. & Paulson, Nicholas D. & Piras, Gianfranco, 2011. "Spatial Approaches to Panel Data in Agricultural Economics: A Climate Change Application," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 43(03), August.
  5. Viego, Valentina N. & Temporelli , Karina L., 2011. "Sobrepeso y obesidad en Argentina. Un análisis basado en técnicas de econometría espacial/Overweigh and Obesity in Argentina. A Spatial Approach," Estudios de Economía Aplicada, Estudios de Economía Aplicada, vol. 29, pages 915 (26 pá, Diciembre.
  6. LeSage, James P., 2011. "Discussion: Applications and Innovations in Spatial Econometrics," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 43(03), August.
  7. Parent, Olivier & LeSage, James P., 2012. "Spatial dynamic panel data models with random effects," Regional Science and Urban Economics, Elsevier, vol. 42(4), pages 727-738.
  8. Elhorst, J. Paul & Lacombe, Donald J. & Piras, Gianfranco, 2012. "On model specification and parameter space definitions in higher order spatial econometric models," Regional Science and Urban Economics, Elsevier, vol. 42(1-2), pages 211-220.
  9. James LeSage & Matthew Dominguez, 2012. "The importance of modeling spatial spillovers in public choice analysis," Public Choice, Springer, vol. 150(3), pages 525-545, March.
  10. Jean Dubé & Diègo Legros, 2013. "Dealing with spatial data pooled over time in statistical models," Letters in Spatial and Resource Sciences, Springer, vol. 6(1), pages 1-18, March.
  11. Viego, Valentina & Temporelli, Karina, 2010. "Econometría espacial: una aplicación a los problemas de sobrepeso y obesidad en las provincias de Argentina
    [Spatial econometrics: an application to obesity indicators in Argentinian provinces]
    ," MPRA Paper 26878, University Library of Munich, Germany.

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