Multi-step sales forecasting in automotive industry based on structural relationship identification
Forecasting sales and demand over 6–24 month horizon is crucial for planning the production processes of automotive and other complex product industries (e.g., electronics and heavy equipment) where typical concept-to-release times are 12–60 month long. However, nonlinear and nonstationary evolution and dependencies with diverse macroeconomic variables hinder accurate long-term prediction of the future of automotive sales. In this paper, a structural relationship identification methodology that uses a battery of statistical unit root, weakly exogeneity, Granger-causality and cointegration tests, is presented to identify the dynamic couplings among automobile sales and economic indicators. Our empirical analysis indicates that automobile sales at segment levels have a long-run equilibrium relationship (cointegration) with identified economic indicators. A vector error correction model (VECM) of multi-segment automobile sales was estimated based on impulse response functions to quantify long-term impact of these economic indicators on sales. Comparisons of prediction accuracy demonstrate that VECM model outperforms other classical and advanced time-series techniques. The empirical results suggest that VECM can significantly improve prediction accuracy of automotive sales for 12-month ahead prediction in terms of RMSE (42.73%) and MAPE (42.25%), compared to the classical time series techniques.
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- Alan Greenspan & Darrel Cohen, 1996.
"Motor vehicle stocks, scrappage, and sales,"
Finance and Economics Discussion Series
96-40, Board of Governors of the Federal Reserve System (U.S.).
- Armstrong, J. Scott & Morwitz, Vicki G. & Kumar, V., 2000. "Sales forecasts for existing consumer products and services: Do purchase intentions contribute to accuracy?," International Journal of Forecasting, Elsevier, vol. 16(3), pages 383-397.
- Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-80, November.
- Johansen, Soren, 1992.
"Testing weak exogeneity and the order of cointegration in UK money demand data,"
Journal of Policy Modeling,
Elsevier, vol. 14(3), pages 313-334, June.
- Johansen, S., 1991. "Testing Weak Exogeneity and the Order of Cointegration in UK Money Demand Data," Papers 78, Helsinki - Department of Economics.
- Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
- Luxhoj, James T. & Riis, Jens O. & Stensballe, Brian, 1996. "A hybrid econometric--neural network modeling approach for sales forecasting," International Journal of Production Economics, Elsevier, vol. 43(2-3), pages 175-192, June.
- Samarjit Das, 2003. "Modelling money, price and output in India: a vector autoregressive and moving average (VARMA) approach," Applied Economics, Taylor & Francis Journals, vol. 35(10), pages 1219-1225.
- Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-72, June.
- Berkovec, James, 1985. "Forecasting automobile demand using disaggregate choice models," Transportation Research Part B: Methodological, Elsevier, vol. 19(4), pages 315-329, August.
- Johansen, Soren & Juselius, Katarina, 1990. "Maximum Likelihood Estimation and Inference on Cointegration--With Applications to the Demand for Money," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 52(2), pages 169-210, May.
- Danese, Pamela & Kalchschmidt, Matteo, 2011. "The role of the forecasting process in improving forecast accuracy and operational performance," International Journal of Production Economics, Elsevier, vol. 131(1), pages 204-214, May.
- Walter McManus, 2007. "The Link Between Gasoline Prices and Vehicle Sales," Business Economics, Palgrave Macmillan, vol. 42(1), pages 53-60, January.
- Vincent R. Nijs & Marnik G. Dekimpe & Jan-Benedict E.M. Steenkamps & Dominique M. Hanssens, 2001. "The Category-Demand Effects of Price Promotions," Marketing Science, INFORMS, vol. 20(1), pages 1-22, September.
- G. Dekimpe, Marnik & Hanssens, Dominique M. & Silva-Risso, Jorge M., 1998. "Long-run effects of price promotions in scanner markets," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 269-291, November.
- Granger, Clive W.J. & Huang, Bwo-Nung & Yang, Chin W., 1998.
"A Bivariate Causality between Stock Prices and Exchange Rates: Evidence from Recent Asia Flu,"
University of California at San Diego, Economics Working Paper Series
qt9bk607p6, Department of Economics, UC San Diego.
- Granger, Clive W. J. & Huangb, Bwo-Nung & Yang, Chin-Wei, 2000. "A bivariate causality between stock prices and exchange rates: evidence from recent Asianflu," The Quarterly Review of Economics and Finance, Elsevier, vol. 40(3), pages 337-354.
- Johansen, Søren, 1995.
"A Stastistical Analysis of Cointegration for I(2) Variables,"
Cambridge University Press, vol. 11(01), pages 25-59, February.
- Johansen, S., 1991. "A Statistical Analsysis of Cointegration for I(2) Variables," Papers 77, Helsinki - Department of Economics.
- Marnik G. Dekimpe & Dominique M. Hanssens, 1995. "The Persistence of Marketing Effects on Sales," Marketing Science, INFORMS, vol. 14(1), pages 1-21.
- Jan R. Landwehr & Aparna A. Labroo & Andreas Herrmann, 2011. "Gut Liking for the Ordinary: Incorporating Design Fluency Improves Automobile Sales Forecasts," Marketing Science, INFORMS, vol. 30(3), pages 416-429, 05-06.
- repec:ner:tilbur:urn:nbn:nl:ui:12-358916 is not listed on IDEAS
- Hiemstra, Craig & Jones, Jonathan D, 1994. " Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation," Journal of Finance, American Finance Association, vol. 49(5), pages 1639-64, December.
- Greenslade, Jennifer V. & Hall, Stephen G. & Henry, S. G. Brian, 2002. "On the identification of cointegrated systems in small samples: a modelling strategy with an application to UK wages and prices," Journal of Economic Dynamics and Control, Elsevier, vol. 26(9-10), pages 1517-1537, August.
- Urbain, Jean-Pierre, 1992.
"On Weak Exogeneity in Error Correction Models,"
Oxford Bulletin of Economics and Statistics,
Department of Economics, University of Oxford, vol. 54(2), pages 187-207, May.
- Mannering, Fred L. & Train, Kenneth, 1985. "Recent directions in automobile demand modeling," Transportation Research Part B: Methodological, Elsevier, vol. 19(4), pages 265-274, August.
- Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
- Chu, Ching-Wu & Zhang, Guoqiang Peter, 2003. "A comparative study of linear and nonlinear models for aggregate retail sales forecasting," International Journal of Production Economics, Elsevier, vol. 86(3), pages 217-231, December.
- Engle, Robert F & Granger, Clive W J, 1987. "Co-integration and Error Correction: Representation, Estimation, and Testing," Econometrica, Econometric Society, vol. 55(2), pages 251-76, March.
- Granger, C. W. J., 1988. "Causality, cointegration, and control," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 551-559.
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