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Multi-step sales forecasting in automotive industry based on structural relationship identification

Akkarapol Sa-ngasoongsong, Satish T.S. Bukkapatnam, Jaebeom Kim, Parameshwaran S. Iyer and R.P. Suresh

International Journal of Production Economics, 2012, vol. 140, issue 2, 875-887

Abstract: 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.

Keywords: Automobile sales forecasting; Long-run equilibrium relationship; Vector error correction model (search for similar items in EconPapers)
Date: 2012
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DOI: 10.1016/j.ijpe.2012.07.009

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Handle: RePEc:eee:proeco:v:140:y:2012:i:2:p:875-887