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On vector autoregressive modeling in space and time

  • Valter Giacinto

    ()

Despite the fact that it provides a potentially useful analytical tool, allowing for the joint modeling of dynamic interdependencies within a group of connected areas, until lately the VAR approach had received little attention in regional science and spatial economic analysis. This paper aims to contribute in this field by dealing with the issues of parameter identification and estimation and of structural impulse response analysis. In particular, there is a discussion of the adaptation of the recursive identification scheme (which represents one of the more common approaches in the time series VAR literature) to a space-time environment. Parameter estimation is subsequently based on the Full Information Maximum Likelihood (FIML) method, a standard approach in structural VAR analysis. As a convenient tool to summarize the information conveyed by regional dynamic multipliers with a specific emphasis on the scope of spatial spillover effects, a synthetic space-time impulse response function (STIR) is introduced, portraying average effects as a function of displacement in time and space. Asymptotic confidence bands for the STIR estimates are also derived from bootstrap estimates of the standard errors. Finally, to provide a basic illustration of the methodology, the paper presents an application of a simple bivariate fiscal model fitted to data for Italian NUTS 2 regions.

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File URL: http://hdl.handle.net/10.1007/s10109-010-0116-6
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Article provided by Springer in its journal Journal of Geographical Systems.

Volume (Year): 12 (2010)
Issue (Month): 2 (June)
Pages: 125-154

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Handle: RePEc:kap:jgeosy:v:12:y:2010:i:2:p:125-154
Contact details of provider: Web page: http://www.springerlink.com/link.asp?id=103079

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  1. Fabio Canova & Matteo Ciccarelli, 2007. "Estimating Multi-country VAR models," Discussion Papers 7_2007, D.E.S. (Department of Economic Studies), University of Naples "Parthenope", Italy.
  2. Zha, Tao, 1999. "Block recursion and structural vector autoregressions," Journal of Econometrics, Elsevier, vol. 90(2), pages 291-316, June.
  3. Benkwitz, Alexander & Lütkepohl, Helmut & Wolters, Jürgen, 1999. "Comparison of bootstrap confidence intervals for impulse responses of German monetary systems," SFB 373 Discussion Papers 1999,29, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  4. Pasaran, M.H. & Im, K.S. & Shin, Y., 1995. "Testing for Unit Roots in Heterogeneous Panels," Cambridge Working Papers in Economics 9526, Faculty of Economics, University of Cambridge.
  5. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
  6. Gerald Carlino & Robert Defina, 1998. "The Differential Regional Effects Of Monetary Policy," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 572-587, November.
  7. Holtz-Eakin, Douglas & Newey, Whitney & Rosen, Harvey S, 1988. "Estimating Vector Autoregressions with Panel Data," Econometrica, Econometric Society, vol. 56(6), pages 1371-95, November.
  8. Georges Bresson & Badi H. Baltagi & Alain Pirotte, 2007. "Panel unit root tests and spatial dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(2), pages 339-360.
  9. Gerald Carlino & Robert DeFina, 1993. "Regional income dynamics," Working Papers 93-1, Federal Reserve Bank of Philadelphia.
  10. Michael Beenstock & Daniel Felsenstein, 2007. "Spatial Vector Autoregressions," Spatial Economic Analysis, Taylor & Francis Journals, vol. 2(2), pages 167-196.
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