Integration of macro and micro level forecasting methods for volatile environments: applications in durable goods industries

International Journal of Applied Decision Sciences

Volumn 1, Issue 4, 2008, Pages 418-434

  Veral, Emre A ^a  

^a CITY UNIVERSITY OF NEW YORK   (United States)

Author keywords

forecasting; simulation; time series

Indexed keywords

EID: 84969447126     PISSN: 17558077     EISSN: 17558085     Source Type: Journal    
DOI: 10.1504/IJADS.2008.022978     Document Type: Article

References (22)

1. Bates, J.M. and Granger, C.W. (1969) ‘The combination of forecasts’, Operational Research Quarterly, Vol. 20, pp.451–468.
2. Bhansali, R.J. (1999) ‘Autoregressive model selection for multi-step prediction’, Journal of Statistical Planning and Inference, Vol. 78, pp.295–305.
3. Caton, C. (1998) ‘Forecast revision: coping with a changing future’, Journal of Business Forecasting, Fall, pp.15–20.
4. Clements, M. (1997) ‘Evaluating the rationality of fixed-event forecasts’, Journal of Forecasting, Vol. 16, pp.225–239.
5. Crato, N. and Ray, B.K. (1996) ‘Model selection and forecasting for long-range dependent processes’, Journal of Forecasting, Vol. 15, pp.107–125.
6. Findley, D.F. (1985) ‘Model selection for multi-step-ahead forecasting’, in H.A. Barker (Ed.): Identification and System Parameter Estimation 1985, 7th IFAC/IFORS Symposium, Pergamon, Oxford/New York, pp.103–1044.
7. Intriligator, M.D. (1978) Econometric Models, Techniques and Applications, Prentice Hall, NJ.
8. Kang, I.B. (2003) ‘Multi-period forecasting using different models for different horizons: an application to U.S. economic time series data’, International Journal of Forecasting, Vol. 19, pp.387–400.
9. Krajewski, L.J. and Rizman, L.P. (2005) Operations Management, 7th ed., Pearson- Prentice Hall, NJ.
10. Lawrence, M. and O’Connor, M. (1995) ‘The anchoring and adjustment heuristic in time series forecasting’, Journal of Forecasting, Vol. 14, pp.443–451.
11. Lawrence, M. and O’Connor, M. (2000) ‘Sales forecasting updates: how good are they in practice?’, International Journal of Forecasting, Vol. 16, pp.369–382.
12. Man, K.S. (2003) ‘Long memory time series and short term forecasts’, International Journal of Forecasting, Vol. 19, pp.477–491.
13. Naik, G. (2004) ‘The structural qualitative method: a promising forecasting tool for developing country markets’, International Journal of Forecasting, Vol. 20, pp.475–485.
14. Nordhaus, W. (1987) ‘Forecasting efficiency: concepts and applications’, Review of Economics and Statistics, Vol. 69, pp.667–674.
15. Swidler, S. and Ketcher, D. (1990) ‘Economic forecasts, rationality and the processing of new information over time’, Journal of Money, Credit and Banking, Vol. 22, pp.65–76.
16. Terui, N. and Kariya, T. (1997) ‘Testing Gaussianity and linearity of Japanese stock returns’, Financial Engineering and the Japanese Markets, Vol. 4, pp.1–30.
17. Terui, N. and van Dijk, H.K. (2002) ‘Combined forecasts from linear and nonlinear time series models’, International Journal of Forecasting, Vol. 18, pp.421–438.
18. Tiao, G.C. and Tsay, R.C. (1994) ‘Some advances in non-linear and adaptive modeling in time series’, Journal of Forecasting, Vol. 13, pp.109–131.
19. Tong, H. (1990) Non-linear Time Series: A Dynamical System Approach, p.425, Oxford University Press, NY.
20. Vollman, T., Berry, W. and Whybark, C. (1992) Manufacturing Planning and Control Systems, Irwin, MA, p 685.
21. Winston, W. (1991) Operations Research, PWS-Kent, MA, p.1171.
22. Zarnowitz, V. (1992) Business Cycles: Theory, History, Indicators and Forecasting, University of Chicago Press, Chicago.