Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2009 | 2 | nr 1 | 72--80
Tytuł artykułu

Forecasting Fluctuations of Sold Industrial Production in Poland on the Basis of Electricity Demand

Warianty tytułu
Języki publikacji
The plan of the paper is as follows: introduction, section 1 with the data and econometric methodology, section 2 with empirical analysis and the model, and the conclusions. Two independent equations have been estimated. Results show that significant leading time series of sold industrial production are electricity demand in four industrial sectors: production of string mass and paper, cars and metal final goods and production of machines and electric devices. (original abstract)
Opis fizyczny
  • Poznań University of Economics, Poland
  • 1. Baxter M., King R.G, Measuring Business Cycles: Approximate Band-Pass Filters for Macroeconomic Time Series, Review of Economics and Statistics, 1999.
  • 2. Bennett A., Closed-End Country Found Discounds and Systematic UK and US Market movements: Co-integration and Error Corrected Granger Causality Tests, Massey University, 2002.
  • 3. Diebold Francis X., The Past, Present, and Future of Macroeconomic Forecasting, University of Pennsylvania, NBER and Federal Reserve Bank of Philadelphia, 1997.
  • 4. Engle R.F., Granger C.W.J., Hallman J.J., Merging Short- and Long-run Forecasts: An Application of Seasonal Cointegration to Monthly Electricity Sales Forecasting, Journal of Econometrics, 1989.
  • 5. Engle R.F., Granger C.W.J., Ramanathan R., Short-Run Forecasts of Electricity oads and Peaks, International Journal of Forecasting, 1997.
  • 6. Evans M.K., Practical Business Forecasting, Blackwell Publishers, 2003, p. 212-215.
  • 7. Granger, C.W.J., Some Properties of Time Series Data and Their Use in Econometric Model Specification, Journal of Econometrics, 1981.
  • 8. King R.G., Rebelo S.T., Resuscitating Real Business Cycles, NBER Working Paper, 2000.
  • 9. King, R.G., Plosser, C.I., Stock, J.H., and Watson, M.W., Stochastic Trends and Economic Fluctuations, American Economic Review, 1991.
  • 10. Leth-Petersen S., Micro Econometric Modeling of Household Energy Use: Testing for Dependence between Demand for Electricity and Natural Gas, The Energy Journal, 2002.
  • 11. Marcellino M., Leading Indicators: What Have We Learned?, IEP-Bocconi University.
  • 12. Mills T.C., Modeling Trends and Cycles in Economic Time Series, Loughborough University, 2003.
  • 13. Prescott, E.C., Theory Ahead of Business Cycle Measurement, Quarterly Review, Federal Reserve Bank of Minneapolis, 1986.
  • 14. Torben Mark Pedersen, Alternative Linear and Non-Linear Detrending Techniques: A Comparative analysis based on Euro-Zone Data, Copenhagen: Minystry of Economic and Business Affairs, 2002.
  • 15. Zarnowitz V., Braun P., Major macroeconomic variables and leading indicators: some estimates of their interrelations, 1886-1982, NBER Working Paper, 1989.
Typ dokumentu
Identyfikator YADDA

Zgłoszenie zostało wysłane

Zgłoszenie zostało wysłane

Musisz być zalogowany aby pisać komentarze.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.