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Regional unemployment forecasts with spatial interdependencies

  • Schanne, N.
  • Wapler, R.
  • Weyh, A.

We forecast unemployment levels for the 176 German labour-market districts on a monthly basis. Because of their small sizes, strong spatial interdependencies exist between these regional units. To account for these, as well as for the heterogeneity in the regional development over time, we apply different versions of a univariate spatial GVAR model. When comparing the forecast precision with that of univariate time series methods, we find that the spatial model does indeed perform better, or at least as well. Hence, the spatial GVAR model provides an alternative or complementary approach to commonly used methods in regional forecasting which do not consider regional interdependencies.

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Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 26 (2010)
Issue (Month): 4 (October)
Pages: 908-926

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Handle: RePEc:eee:intfor:v:26:y::i:4:p:908-926
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  1. Hylleberg, S. & Engle, R.F. & Granger, C.W.J. & Yoo, B.S., 1988. "Seasonal, Integration And Cointegration," Papers 6-88-2, Pennsylvania State - Department of Economics.
  2. Giacomini, Raffaella & Granger, Clive W. J., 2004. "Aggregation of space-time processes," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 7-26.
  3. Lutkepohl, Helmut, 2006. "Forecasting with VARMA Models," Handbook of Economic Forecasting, Elsevier.
  4. Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Uwe Blien, 2006. "New Neural Network Methods for Forecasting Regional Employment: an Analysis of German Labour Markets," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(1), pages 7-30.
  5. Inoue, Atsushi & Kilian, Lutz, 2003. "On the selection of forecasting models," Working Paper Series 0214, European Central Bank.
  6. Kelejian, Harry H. & Prucha, Ingmar R., 2007. "The relative efficiencies of various predictors in spatial econometric models containing spatial lags," Regional Science and Urban Economics, Elsevier, vol. 37(3), pages 363-374, May.
  7. Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
  8. Blien, Uwe & Tassinopoulos, Alexandros, 1999. "Forecasting Regional Employment with the ENTROP Method," ERSA conference papers ersa99pa344, European Regional Science Association.
  9. Konstantin Arkadievich Kholodilin & Boriss Siliverstovs & Stefan Kooths, 2008. "A Dynamic Panel Data Approach to the Forecasting of the GDP of German L�nder," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(2), pages 195-207.
  10. Schanne, N. & Wapler, R. & Weyh, A., 2010. "Regional unemployment forecasts with spatial interdependencies," International Journal of Forecasting, Elsevier, vol. 26(4), pages 908-926, October.
  11. Baltagi, Badi H., 2006. "Forecasting with panel data," Discussion Paper Series 1: Economic Studies 2006,25, Deutsche Bundesbank, Research Centre.
  12. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
  13. Matias Mayor & Ana Jesus Lopez & Rigoberto Perez, 2007. "Forecasting Regional Employment with Shift-Share and ARIMA Modelling," Regional Studies, Taylor & Francis Journals, vol. 41(4), pages 543-551.
  14. [Reference to Proietti], Tommaso, 2000. "Comparing seasonal components for structural time series models," International Journal of Forecasting, Elsevier, vol. 16(2), pages 247-260.
  15. James H. Stock & Mark W. Watson, 1998. "A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series," NBER Working Papers 6607, National Bureau of Economic Research, Inc.
  16. Harvey, Andrew, 2006. "Forecasting with Unobserved Components Time Series Models," Handbook of Economic Forecasting, Elsevier.
  17. Marco Aiolfi & Carlos Capistrán & Allan Timmermann, 2010. "Forecast Combinations," Working Papers 2010-04, Banco de México.
  18. Klaus, Joachim & Maußner, Alfred, 1988. "Regionale Arbeitsmarktanalysen mittels vergleichender Arbeitsmarktbilanzen," Mitteilungen aus der Arbeitsmarkt- und Berufsforschung, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 21(1), pages 74-83.
  19. Graham Elliott & Allan Timmermann, 2008. "Economic Forecasting," Journal of Economic Literature, American Economic Association, vol. 46(1), pages 3-56, March.
  20. Rubén Hernández-Murillo & Michael T. Owyang, 2004. "The information content of regional employment data for forecasting aggregate conditions," Working Papers 2004-005, Federal Reserve Bank of St. Louis.
  21. Ray, W. D., 1989. "Rates of convergence to steady state for the linear growth version of a dynamic linear model (DLM)," International Journal of Forecasting, Elsevier, vol. 5(4), pages 537-545.
  22. Joseph Beaulieu, J. & Miron, Jeffrey A., 1993. "Seasonal unit roots in aggregate U.S. data," Journal of Econometrics, Elsevier, vol. 55(1-2), pages 305-328.
  23. M. Hashem Pesaran & Til Schuermann & Scott M. Weiner, 2002. "Modeling Regional Interdependencies Using a Global Error-Correcting Macroeconometric Model," Center for Financial Institutions Working Papers 01-38, Wharton School Center for Financial Institutions, University of Pennsylvania.
  24. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
  25. Graham Schindler & Philip Israilevich & Geoffrey Hewings, 1997. "Regional Economic Performance: An Integrated Approach," Regional Studies, Taylor & Francis Journals, vol. 31(2), pages 131-137.
  26. Paulo Rodrigues & Denise Osborn, 1999. "Performance of seasonal unit root tests for monthly data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(8), pages 985-1004.
  27. Oberhofer, Walter & Blien, Uwe & Tassinopoulos, Alexandros, 2000. "Forecasting Regional Employment With A Generalised Extrapolation Method," ERSA conference papers ersa00p170, European Regional Science Association.
  28. Michael Beenstock & Daniel Felsenstein, 2007. "Spatial Vector Autoregressions," Spatial Economic Analysis, Taylor & Francis Journals, vol. 2(2), pages 167-196.
  29. Swanson, Norman R. & White, Halbert, 1997. "Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models," International Journal of Forecasting, Elsevier, vol. 13(4), pages 439-461, December.
  30. Giuseppe Arbia & Marco Bee & Giuseppe Espa, 2007. "Aggregation of regional economic time series with different spatial correlation structures," Department of Economics Working Papers 0720, Department of Economics, University of Trento, Italia.
  31. Osborn, Denise R. & Heravi, Saeed & Birchenhall, C. R., 1999. "Seasonal unit roots and forecasts of two-digit European industrial production," International Journal of Forecasting, Elsevier, vol. 15(1), pages 27-47, February.
  32. Michael Magura, 1998. "original: IO and spatial information as Bayesian priors in an employment forecasting model," The Annals of Regional Science, Springer, vol. 32(4), pages 495-503.
  33. Behrens, Kristian & Thisse, Jacques-Francois, 2007. "Regional economics: A new economic geography perspective," Regional Science and Urban Economics, Elsevier, vol. 37(4), pages 457-465, July.
  34. Weller, Barry R., 1989. "National indicator series as quantitative predictors of small region monthly employment levels," International Journal of Forecasting, Elsevier, vol. 5(2), pages 241-247.
  35. Partridge, Mark D & Rickman, Dan S, 1998. "Generalizing the Bayesian Vector Autoregression Approach for Regional Interindustry Employment Forecasting," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(1), pages 62-72, January.
  36. Franses, Philip Hans, 1991. "Seasonality, non-stationarity and the forecasting of monthly time series," International Journal of Forecasting, Elsevier, vol. 7(2), pages 199-208, August.
  37. Edlund, Per-Olov & Karlsson, Sune, 1993. "Forecasting the Swedish unemployment rate VAR vs. transfer function modelling," International Journal of Forecasting, Elsevier, vol. 9(1), pages 61-76, April.
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