Regional unemployment forecasts with spatial interdependencies

International Journal of Forecasting

Volumn 26, Issue 4, 2010, Pages 908-926

  Schanne, N ^a   Wapler, R ^a   Weyh, A ^a  

^a INSTITUTE FOR EMPLOYMENT RESEARCH IAB   (Germany)

Author keywords

Forecasting practice; Labour market forecasting; Macroeconomic forecasting; Regional forecasting; Time series

Indexed keywords

EID: 77956263063     PISSN: 01692070     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijforecast.2009.07.002     Document Type: Article

References (36)

1. Anselin L. Spatial econometrics: Methods and models 1988, Kluwer Academic Publishers, Dortrecht.
2. Arbia, G., Bee, M., & Espa, G. (2007). Aggregation of regional economic time series with different spatial correlation structures. Department of Economics Working Papers 20/2007, University of Trento.
3. Baltagi B.H. Comment on Pesaran, Schuermann, & Weiner, 2004. Journal of Business and Economic Statistics 2004, 22(2):163-164.
4. Baltagi, B. H. (2006). Forecasting with panel data. Discussion paper 25, Deutsche Bundesbank.
5. Baltagi B.H., Li D. Prediction in the panel data model with spatial correlation. Advances in spatial econometrics 2004, 283-295. Springer, Heidelberg and New York. L. Anselin, R.J.G.M. Florax, S.J. Rey (Eds.).
6. Beaulieu J.J., Miron J.A. Seasonal unit roots in aggregate U.S. data. Journal of Econometrics 1993, 55(1-2):305-328.
7. Beenstock M., Felsenstein D. Spatial vector autoregressions. Spatial Economic Analysis 2007, 2:167-196.
8. Behrens K., Thisse J.-F. Regional economics: A new economic geography perspective. Regional Science and Urban Economics 2007, 37:457-465.
9. Box G.E., Jenkins G.M. Time series analysis: Forecasting and control 1970, Holden-Day, San Francisco.
10. Chatfield C., Yar M. Holt-Winters forecasting: Some practical issues. The Statistician 1988, 37(2):129-140.
11. Diebold F.X., Mariano R.S. Comparing predictive accuracy. Journal of Business and Economic Statistics 1995, 13(3):253-263.
12. Dreger C., Reimers H.-E. Panel seasonal unit root tests: Further simulation results and an application for unemployment data. Allgemeines Statistisches Archiv 2005, 89(8):321-337.
13. Franses P. Seasonality, non-stationarity and the forecasting of monthly time series. International Journal of Forecasting 1991, 7:199-208.
14. Fujita M., Krugman P.R., Venables A.J. The spatial economy. Cities, regions, and international trade 1999, The MIT Press, Cambridge (MA), London.
15. Giacomini R., Granger C.W.J. Aggregation of space-time processes. Journal of Econometrics 2004, 118(1-2):7-26.
16. Haining R. Spatial data analysis. Theory and practice 2003, Cambridge University Press, Cambridge, New York (NY).
17. Hampel, K. E., Kunz, M., Schanne, N. G., Wapler, R., & Weyh, A. (2007). Regional employment forecasts with spatial interdependencies. Discussion paper 02/2007, IAB.
18. Harvey A.C. Forecasting with unobserved components time series models. Handbook of economic forecasting 2004, 327-412. Elsevier, Amsterdam et al. G. Elliott, C.W.J. Granger, A. Timmermann (Eds.).
19. Harvey D., Leybourne S., Newbold P. Testing the equality of prediction mean squared errors. International Journal of Forecasting 1997, 13(2):281-291.
20. Hernandez-Murillo R., Owyang M.T. The information content of regional employment data for forecasting aggregate conditions. Economic Letters 2006, 90:335-339.
21. Hurvich C.M., Tsai C.-L. Regression and time series model selection in small samples. Biometrika 1989, 76(2):297-307.
22. Hylleberg S., Engle R.F., Granger C.W., Yoo B.S. Seasonal integration and cointegration. Journal of Econometrics 1990, 44:215-238.
23. Inoue A., Kilian L. On the selection of forecasting models. Journal of Econometrics 2006, 130(2):273-306.
24. Kelejian H.H., Prucha I.R. The relative efficiencies of various predictors in spatial econometric models containing spatial lags. Regional Science and Urban Economics 2007, 37:363-374.
25. Kholodilin K.A., Siliverstovs B., Kooths S. Dynamic panel data approach to the forecasting of the GDP of German Länder. Spatial Economic Analysis 2008, 3(2):195-207.
26. Longhi S., Nijkamp P. Forecasting regional labour market developments under spatial autocorrelation. International Regional Science Review 2007, 30(2):100-119.
27. Magura M. IO and spatial information as Bayesian priors in an employment forecasting model. Annals of Regional Science 1998, 32(4):495-503.
28. Mayor M., López A.J., Pérez R. Forecasting regional employment with shift-share and ARIMA modelling. Regional Studies 2007, 41:543-551.
29. Moran P.A.P. Some theories in time series: II. The significance of the serial correlation coefficient. Biometrika 1948, 35(3-4):255-260.
30. Osborn D., Heravi S., Birchenhall C. Seasonal unit roots and forecasts of two-digit European industrial production. International Journal of Forecasting 1999, 15:27-47.
31. Patuelli R., Reggiani A., Nijkamp P., Blien U. New neural network methods for forecasting regional employment. An analysis of German labour markets. Spatial Economic Analysis 2006, 1:7-30.
32. Pesaran M.H., Schuermann T., Weiner S.M. Modeling regional interdependencies using a global error-correcting macroeconometric model. Journal of Business and Economic Statistics 2004, 22(2):129-162.
33. Rodrigues P., Osborn D. Performance of seasonal unit root tests for monthly data. Journal of The American Statistician 1999, 26(8):985-1004.
34. Stock J.H. Forecasting economic time series. A companion to theoretical econometrics 2001, 562-584. Blackwell Publishers, Malden (MA). B.H. Baltagi (Ed.).
35. Taylor A. Testing for unit roots in monthly time series. Journal of Time Series Analysis 1998, 19(3):349-368.
36. Timmermann A. Forecast combinations. Handbook of Economic Forecasting 2006, vol. 1:135-196. Elsevier, North-Holland, Amsterdam et al. G. Elliott, C.W.J. Granger, A. Timmermann (Eds.).