Forecasting Regional Labour Market Developments Under Spatial Heterogeneity and Spatial Autocorrelation
AbstractBecause of heterogeneity across regions, economic policy measures are increasingly targeted at the regional level. As a result, the need for economic forecasts at a sub-national level is rapidly increasing. The data available to compute regional forecasts is usually based on a pseudo-panel that consists of a limited number of observations over time, and a large number of areas (regions) strongly interacting with each other. In such a situation, the application of traditional time-series techniques to distinct time series of regional data may then become a sub-optimal forecasting strategy. In the field of regional forecasting of socio-economic variables, both linear and non-linear models have recently been applied and evaluated. However, often such analyses tend to ignore the spatial structure of the data and the spatial interactions that are likely to exist among regions. In this paper, we evaluate the ability of different statistical techniques – namely spatial lag and spatial error models – to correct for misspecification due to neglected spatial autocorrelation in the data set. Our empirical application concerns short-term forecasts of employment in 326 West German labour market regions. We find that the superimposed spatial structure that is required for the estimation of spatial models improves the forecasting performance of non-spatial forecasting models.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 05-041/3.
Date of creation: 25 Apr 2005
Date of revision:
Contact details of provider:
Web page: http://www.tinbergen.nl
Space-Time Data; Regional Forecasts; Spatial Heterogeneity; Spatial Spillovers;
Find related papers by JEL classification:
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Longitudinal Data; Spatial Time Series
- E27 - Macroeconomics and Monetary Economics - - Macroeconomics: Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
- R19 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Other
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Pami Dua & Stephen Miller, 1995. "Forecasting and Analyzing Economic Activity with Coincident and Leading Indexes: The Case of Connecticut," Working papers 1995-05, University of Connecticut, Department of Economics.
- Elhorst, J.P., 2000. "Dynamic models in space and time," Research Report 00C16, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
- Blien, Uwe & Tassinopoulos, Alexandros, 1999.
"Forecasting Regional Employment with the ENTROP Method,"
ERSA conference papers
ersa99pa344, European Regional Science Association.
- Uwe Blien & Alexandros Tassinopoulos, 2001. "Forecasting Regional Employment with the ENTROP Method," Regional Studies, Taylor and Francis Journals, vol. 35(2), pages 113-124.
- Hoogstrate, Andre J & Palm, Franz C & Pfann, Gerard A, 2000.
"Pooling in Dynamic Panel-Data Models: An Application to Forecasting GDP Growth Rates,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 18(3), pages 274-83, July.
- Hoogstrate, Andre J. & Palm, Franz C. & Pfann, Gerard A., 2000. "Pooling in dynamic panel data models: an application to forecasting GDP growth rates," Open Access publications from Maastricht University urn:nbn:nl:ui:27-5755, Maastricht University.
- Diebold, Francis X & Mariano, Roberto S, 2002.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 20(1), pages 134-44, January.
- Olivier Jean Blanchard & Lawrence F. Katz, 1992. "Regional Evolutions," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 23(1), pages 1-76.
- Norman R. Swanson & Halbert White, 1995.
"A Model Selection Approach to Real-Time Macroeconomic Forecasting Using Linear Models and Artificial Neural Networks,"
- Norman R. Swanson & Halbert White, 1997. "A Model Selection Approach To Real-Time Macroeconomic Forecasting Using Linear Models And Artificial Neural Networks," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 540-550, November.
- Swanson, N.R. & White, H., 1995. "A Models Selection Approach to Real-Time Macroeconomic Forecasting Using Linear Models and Artificial Neural Networks," Papers 04-95-12, Pennsylvania State - Department of Economics.
- Hausman, Jerry A, 1978. "Specification Tests in Econometrics," Econometrica, Econometric Society, vol. 46(6), pages 1251-71, November.
- Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
- 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.
- Lutz Bellmann & Uwe Blien, 2001. "Wage curve analyses of establishment data from western Germany," Industrial and Labor Relations Review, ILR Review, Cornell University, ILR School, vol. 54(4), pages 851-863, July.
- Anselin, Luc & Bera, Anil K. & Florax, Raymond & Yoon, Mann J., 1996. "Simple diagnostic tests for spatial dependence," Regional Science and Urban Economics, Elsevier, vol. 26(1), pages 77-104, February.
- 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.
- Francis X. Diebold & Lutz Kilian, 1999.
"Unit Root Tests Are Useful for Selecting Forecasting Models,"
NBER Working Papers
6928, National Bureau of Economic Research, Inc.
- Diebold, Francis X & Kilian, Lutz, 2000. "Unit-Root Tests Are Useful for Selecting Forecasting Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(3), pages 265-73, July.
- Francis X. Diebold & Lutz Kilian, 1999. "Unit Root Tests are Useful for Selecting Forecasting Models," New York University, Leonard N. Stern School Finance Department Working Paper Seires 99-063, New York University, Leonard N. Stern School of Business-.
- Raymond J.G.M. Florax & Peter Nijkamp, 2003. "Misspecification in Linear Spatial Regression Models," Tinbergen Institute Discussion Papers 03-081/3, Tinbergen Institute.
- Anselin, Luc, 2002. "Under the hood : Issues in the specification and interpretation of spatial regression models," Agricultural Economics, Blackwell, vol. 27(3), pages 247-267, November.
- Dan S. Rickman, 2002. "A Bayesian forecasting approach to constructing regional input-output based employment multipliers," Papers in Regional Science, Springer, vol. 81(4), pages 483-498.
- 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (webmaster-tinbergen).
If references are entirely missing, you can add them using this form.