The Forecasting Accuracy of Five Time Series Models: Evidence from the Portuguese Car Market
AbstractThis paper compares the out-of-sample forecasting accuracy of five classes of time series models for market shares of the six most important Portuguese car market competitors over differents horizons. As representative time series models I employ a random-walk with drift (Naive), a univariate ARIMA, a near-VAR and a general BVAR. The out-of- sample forecasts are also compared against forecasts generated from structural econometric market share models (SEM). Using four accuracy measures I find the forecasts from the near-VAR and the BVAR models really more accurate. With regard to these models, I could say that the BVAR model is the best for longer forecasts (12-steps ahead), while the n-VAR is superior over the shorter horizon of one to six steps.
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 EconWPA in its series Econometrics with number 9604002.
Length: 21 pages
Date of creation: 09 Apr 1996
Date of revision:
Note: Type of Document - Word for Windows 2.0; prepared on IBM PC ; to print on HP Laser Jet; pages: 21 ; figures: one figure and one table
Contact details of provider:
Web page: http://188.8.131.52
Accuracy measures; ARIMA; Automobile market; BVAR; Market share; Portugal; Random-walk; SEM; VAR;
Find related papers by JEL classification:
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
- C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
- C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
- C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
- C5 - Mathematical and Quantitative Methods - - Econometric Modeling
- C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
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.:
- Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1986.
"Forecasting and conditional projection using realistic prior distribution,"
93, Federal Reserve Bank of Minneapolis.
- Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1983. "Forecasting and Conditional Projection Using Realistic Prior Distributions," NBER Working Papers 1202, National Bureau of Economic Research, Inc.
- Sims, Christopher A & Stock, James H & Watson, Mark W, 1990. "Inference in Linear Time Series Models with Some Unit Roots," Econometrica, Econometric Society, vol. 58(1), pages 113-44, January.
- Brodie, Roderick J. & De Kluyver, Cornelis A., 1987. "A comparison of the short term forecasting accuracy of econometric and naive extrapolation models of market share," International Journal of Forecasting, Elsevier, vol. 3(3-4), pages 423-437.
- Hsiao, Cheng, 1979. "Causality tests in econometrics," Journal of Economic Dynamics and Control, Elsevier, vol. 1(4), pages 321-346, November.
- Danaher, Peter J. & Brodie, Roderick J., 1992. "Predictive accuracy of simple versus complex econometric market share models: Theoretical and empirical results," International Journal of Forecasting, Elsevier, vol. 8(4), pages 613-626, December.
- Cooley, Thomas F. & Leroy, Stephen F., 1985. "Atheoretical macroeconometrics: A critique," Journal of Monetary Economics, Elsevier, vol. 16(3), pages 283-308, November.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (EconWPA).
If references are entirely missing, you can add them using this form.