Full-text resources of CEJSH and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl

PL EN


2014 | 15 | 2 | 27-36

Article title

DEMAND FORECAST WITH BUSINESS CLIMATE INDEX FOR A STEEL AND IRON INDUSTRY REPRESENTATIVE

Content

Title variants

Languages of publication

EN

Abstracts

EN
The steel and iron industry production is dedicated to serve other industries mainly. This makes the exercise of demand forecasting different than for consumer goods. The common sense says that demand fluctuations are influenced by general economic soundness. An attempt was made to address the question of improving forecast’s accuracy by adding a business cycle indicator as an input variable. The SARIMAX model was applied. Including a business climate indicator improved model’s performance, however no co integration is observed between the two series.

Year

Volume

15

Issue

2

Pages

27-36

Physical description

Dates

published
2014

Contributors

  • Collegium of Economic Analysis Warsaw School of Economics

References

  • Briffaut, J.P., Lallement, P. (2010) Volatility Forecasting of Market Demand as Aids for Planning Manufacturing Activities. Service Science & Management, 3, pp. 383-389.
  • Bielak, J. (2010) Prognozowanie ryku pracy woj. Lubelskiego z wykorzystaniem modeli ARIMA i ARIMAX. Barometr Regionalny, nr 1(19), pp. 27-44.
  • Box, G. E. P., Jenkins, G., Reinsel, G. C. (1994) Time Series Analysis: Forecasting and Control, third edition. Prentice-Hall.
  • Cieślak, M. et al. (1997) Prognozowanie gospodarcze. Metody i zastosowania. Wydawnictwo Naukowe PWN, Warszawa.
  • Crane, D. B., Crotty, J. R. (1967) A two-stage forecasting model: Exponential smoothing and multiple regression. Management Science, Vol. 13 (8), Series B, pp. 501-507.
  • Ďurka, P., Pastoreková, S. (2012) ARIMA vs. ARIMAX – which approach is better to analyze and forecast macroeconomic time series? Proceedings of 30th International Conference Mathematical Methods in Economics, Karviná, Czech Repubic.
  • Engle, R. F., Granger, C. W. J. (1987) Co-integration and error correction: Representation, estimation and testing. Econometrica, 55(2), pp. 251–276.
  • Franses, P.H., Legerstee, R. (2013) Do statistical models for SKU level data benefit from including past expert knowledge? International Journal of Forecasting 29, pp. 80–87.
  • Forbes (2013) W Europie znika stal. Dowód na wyłudzanie VAT? http://www.forbes.pl/artykuly/sekcje/wydarzenia/w-europie-znika-stal--dowod-na-wyludzanie-vat,26826,1
  • Główny Urząd Statystyczny (2013) Wskaźniki makroekonomiczne.
  • http://www.stat.gov.pl/gus/wskazniki_makroekon_PLK_HTML.htm
  • Główny Urząd Statystyczny (2013) Uwagi metodyczne.
  • http://www.stat.gov.pl/gus/5840_2794_PLK_HTML.htm
  • Grudkowska, S. (2013) JDemetra+. User Manual. National Bank of Poland.
  • Klug, F. (2011) Automotive Supply Chain Logistics: Container Demand Planning using Monte Carlo Simulation. International Journal Automotive Technology and Management, Vol. 11, No. 3, pp. 254-268.
  • Rippe, R., Wilkinson, W., Morrison, D. (1976) Industrial Market Forecasting with Anticipations Data. Management Science, Vol. 22, No. 6 (Feb., 1976), pp. 639-651.
  • Szeplewicz, K. (2011) Wskaźniki wyprzedzające koniunktury - analiza ekonometryczna. Prace i materiały Instytutu Rozwoju Gospodarczego, IRG SGH, Warszawa, nr 87, pp. 33-63.

Document Type

Publication order reference

Identifiers

YADDA identifier

bwmeta1.element.desklight-e8da9c10-2e34-44a6-9178-3603abdd0617
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.