A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series
AbstractA forecasting comparison is undertaken in which 49 univariate forecasting methods, plus various forecast pooling procedures, are used to forecast 215 U.S. monthly macroeconomic time series at three forecasting horizons over the period 1959 - 1996. All forecasts simulate real time implementation, that is, they are fully recursive. The forecasting methods are based on four classes of models: autoregressions (with and without unit root pretests), exponential smoothing, artificial neural networks, and smooth transition autoregressions. The best overall performance of a single method is achieved by autoregressions with unit root pretests, but this performance can be improved when it is combined with the forecasts from other methods.
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 National Bureau of Economic Research, Inc in its series NBER Working Papers with number 6607.
Date of creation: Jun 1998
Date of revision:
Publication status: Published as "Evidence on Structural Instability in Macroeconomic Time Series Relations", JBES, Vol. 14, no. 1 (January 1996): 11-30.
Note: EFG ME
Contact details of provider:
Postal: National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.
Web page: http://www.nber.org
More information through EDIRC
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
This paper has been announced in the following NEP Reports:
- NEP-ECM-1998-07-13 (Econometrics)
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.:
- McNees, Stephen K., 1990. "The role of judgment in macroeconomic forecasting accuracy," International Journal of Forecasting, Elsevier, Elsevier, vol. 6(3), pages 287-299, October.
- Eric Ghysels & Clive W.J. Granger & Pierre L. Siklos, 1995.
"Is Seasonal Adjustment a Linear or Nonlinear Data Filtering Process?,"
CIRANO Working Papers, CIRANO
- Ghysels, Eric & Granger, Clive W J & Siklos, Pierre L, 1996. "Is Seasonal Adjustment a Linear or Nonlinear Data-Filtering Process?," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 14(3), pages 374-86, July.
- Ghysels, E. & Granger, C.W.J. & Siklos, P.L., 1995. "Is Seasonal Adjustment a Linear or Nonlinear Data Filtring Process," Cahiers de recherche, Universite de Montreal, Departement de sciences economiques 9517, Universite de Montreal, Departement de sciences economiques.
- Ghysels, E. & Granger, C.W.J. & Siklos, P.L., 1995. "Is Seasonal Adjustment a Linear or Nonlinear Data Filtring Process," Cahiers de recherche, Centre interuniversitaire de recherche en Ã©conomie quantitative, CIREQ 9517, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
- Meese, Richard & Geweke, John, 1984. "A Comparison of Autoregressive Univariate Forecasting Procedures for Macroeconomic Time Series," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 2(3), pages 191-200, July.
- Clive W. Granger & Timo Terasvirta & Heather M. Anderson, 1993. "Modeling Nonlinearity over the Business Cycle," NBER Chapters, in: Business Cycles, Indicators and Forecasting, pages 311-326 National Bureau of Economic Research, Inc.
- 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, Pennsylvania State - Department of Economics 04-95-12, Pennsylvania State - Department of Economics.
- Norman R. Swanson & Halbert White, 1995. "A Model Selection Approach to Real-Time Macroeconomic Forecasting Using Linear Models and Artificial Neural Networks," Macroeconomics, EconWPA 9503004, EconWPA.
- Terasvirta, Timo & Tjostheim, Dag & W.J. Granger, Clive, 1986. "Aspects of modelling nonlinear time series," Handbook of Econometrics, Elsevier, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 48, pages 2917-2957 Elsevier.
- Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, Elsevier, vol. 14(1-2), pages 3-24, February.
- Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996.
"Efficient Tests for an Autoregressive Unit Root,"
Econometrica, Econometric Society,
Econometric Society, vol. 64(4), pages 813-36, July.
- Tom Doan, . "GLSDETREND: RATS procedure to perform local to unity GLS detrending," Statistical Software Components RTS00077, Boston College Department of Economics.
- Graham Elliott & Thomas J. Rothenberg & James H. Stock, 1992. "Efficient Tests for an Autoregressive Unit Root," NBER Technical Working Papers 0130, National Bureau of Economic Research, Inc.
- Tom Doan, . "ERSTEST: RATS procedure to perform Elliott-Rothenberg-Stock unit root tests," Statistical Software Components RTS00066, Boston College Department of Economics.
- Granger, Clive W. J. & Terasvirta, Timo, 1993. "Modelling Non-Linear Economic Relationships," OUP Catalogue, Oxford University Press, Oxford University Press, number 9780198773207, October.
- Stock, James H, 1996. "VAR, Error Correction and Pretest Forecasts at Long Horizons," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 58(4), pages 685-701, November.
- Granger, Clive W J, 1993. "Strategies for Modelling Nonlinear Time-Series Relationships," The Economic Record, The Economic Society of Australia, The Economic Society of Australia, vol. 69(206), pages 233-38, September.
This item has more than 25 citations. To prevent cluttering this page, these citations are listed on a separate page. reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ().
If references are entirely missing, you can add them using this form.