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Traffic Sensitivity of Long-term Regional Growth Forecasts

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Abstract

We estimate the sensitivity of the regional growth forecast in the year 2002 due to expected changes in the travel time (TT) matrix. We use a dynamic panel model with spatial effects where the spatial dimension enters the explanatory variables in different ways. The spatial dimension is based on geographical distance between 227 cells in central Europe and the travel time matrix based on average train travel times. The regressor variables are constructed by a) the average past growth rates, where the travel times are used as weights, b) the average travel times across all cells (made comparable by index construction), c) the gravity potential variables based on GDP per capita, employment, productivity and population and d) dummy variables and other socio-demographic variables. We find that for the majority of the cells the relative differences in growth for the year 2020 is rather small. But there are differences as how many regions will benefit from improved train networks: GDP, employment, and population forecasts respond differently.

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References

  • BARRO, R AND SALA-I-MARTIN X. (1995), Economic Growth, McGraw Hill: New York.

    Google Scholar 

  • BARRO, R AND SALA-I-MARTIN X. (1992), “Convergence”, Journal of Political Economy, No. 100, pp. 223–251.

    Article  Google Scholar 

  • BARRO, R AND SALA-I-MARTIN X. (1995), Economic Growth, McGraw Hill: New York.

    Google Scholar 

  • BRUNOW, ST. AND HIRTE G. (2005) Age structure and Regional Income Growth, TU Dresden, Discussion paper Verkehr 1/2005.

    Google Scholar 

  • GEWEKE J. (1993), “Bayesian Treatment of the Independent Student-t Linear Model”, J. of Applied Econometrics, 8 Suppl., S19–S40.

    Google Scholar 

  • LESAGE, J. P. (1997), “Bayesian Estimation of Spatial Autoregressive Models”, International Regional Science Review, 1997 Volume 20, 113–129.

    Google Scholar 

  • LESAGE J. (1998), Spatial Econometrics, Manuscript and Function Library, http://www.spatial-econometrics.com/html/wbook.pdf

    Google Scholar 

  • LESAGE, J. P. (1997), “Bayesian Estimation of Spatial Autoregressive Models”, International Regional Science Review, 1997 Volume 20, 113–129.

    Google Scholar 

  • LESAGE, J. AND R. KELLEY PACE (2002), “Using Matrix Exponentials to Explore Spatial Structure in Regression Relationships”, mimeo, Univ. of Toledo.

    Google Scholar 

  • LESAGE, J. P. AND A. KRIVELYOVA (1999), “A Spatial Prior for Bayesian Vector Autoregressive Models,” Journal of Regional Science, Vol. 39(2), 297–317.

    Article  Google Scholar 

  • POLASEK W. AND H. BERRER (2005) Infrastructure and GDP growth in Central European Regions, IHS Vienna, mimeo.

    Google Scholar 

  • RAFTERY A. E., D. MADIGAN, AND J. A. HOETING (1997), Bayesian model averaging for linear regression models, Journal of the American Statistical Association, 92, 179–191.

    MathSciNet  Google Scholar 

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© 2006 Springer Berlin · Heidelberg

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Polasek, W., Berrer, H. (2006). Traffic Sensitivity of Long-term Regional Growth Forecasts. In: Spiliopoulou, M., Kruse, R., Borgelt, C., Nürnberger, A., Gaul, W. (eds) From Data and Information Analysis to Knowledge Engineering. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31314-1_61

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