Notes on a Forecasting Procedure

• Autorzy: Maria Blangiewicz , Krystyna Strzała
• Języki publikacji: EN

Abstrakty

EN

The proposed forecasting procedure has been developed as a tool of integrating indicators derived from business tendency surveys with selected indicators of quantitative statistics, in order to improve the diagnosing and forecasting of the economic tendencies in Poland. Looking at business tendency surveys based on the philosophical background of the business cycle theory one must note that opinions formulated at the enterprise level are growing in significance in the economy, whose nature is microeconomic in fact (Kokocińska, Strzała, 2007). An overview of the literature reveals (Carnot, Koen, Tissot, 2005) that about 14 research institutions dealing with economic forecasting world wide include the business surveys results in their analyses and projections. The analyses focused on the Polish economic tendency indicators and their integration in the conducted surveys, including the qualitative indicators derived from the business surveys and macroeconomic time-series sourced from Statistical Bulletins and materials available from the Department of National Accounts, Central Statistical Office. (fragment of text)

Słowa kluczowe

EN

Forecasting; Business surveys; Business trends

PL

Prognozowanie; Badania koniunktury; Koniunktura gospodarcza

• Czasopismo: Dynamic Econometric Models
• Rocznik: 2008
• Tom: 8
• Strony: 75--83

Twórcy

autor

Maria Blangiewicz

• University of Gdansk, Poland

autor

Krystyna Strzała

• University of Gdansk, Poland

Bibliografia

• Atukeren, E. (2005), Measuring the Strength of Cointegration and Granger-Causality, Applied Economics, vol. 37, 1607-1614.
• Blangiewicz, M., Strzała, K. (2006), Podobieństwa i różnice struktury stochastycznej mikro- i makroekonomicznych wskaźników koniunktury gospodarczej Polski, Prace i Materiały Wydziału Zarządzania Uniwersytetu Gdańskiego, Nr 3, Wydawnictwo Uniwersytetu Gdańskiego, Gdańsk.
• Carnot, N., Koen, V., Tissot, B. (2005), Economic Forecasting, Palgrave Macmillan, London.
• Charemza, W. W., Deadman, D.F. (1997), New Directions in Econometric Practice, Edward Elgar, Aldershot.
• da Silva Lopes, A. C. B. (2003), The Order of Integration for Quarterly Macroeconomic Time-Series: a Simple Testing Strategy, Empirical Economics vol. 28, 783-794.
• Eichler, M. (2007), Granger Causality and Path Diagrams for Multivariate Time Series, Journal of Econometrics, vol. 137, 334-353.
• Franses, P. H., Hobijn, B. (1997), Critical Values for Unit Root Tests in Seasonal Time Series, Journal of Applied Statistics, vol. 24, 25-47.
• Geweke, J. (1984), Inference and Causality in Economic Time Series Models, in: Griliches, Z., Intriligator, M. D. (ed.) Handbook of Econometrics, vol. 2, North Holland,
• Granger, C. W . J. (1988) Some Recent Developments in a Concept of Causality, Journal of Econometrics, vol. 39, 199-211.
• Guilkey, D. K., Salemi, M. K. (1982), Small Sample Properties of Three Tests for Granger Causal Ordering in a Bivariate Stochastic System, Review of Economic and Statistics, vol. 64, 668-680.
• Johansen, S. (1988), Statistical Analysis of Cointegration Vectors, Journal of Economic Dynamic and Control, vol. 12, 231-254.
• Kokocińska, M., Strzała, K (2008), Using Business Tendency Surveys for Short-term Forecasting of Macro-categories, in: Prace i Materiały Instytutu Rozwoju Gospodarczego SGH, No. 79, Szkoła Główna Handlowa, Warsaw (in print).
• Leybourne, S. J. (1995), Testing for Unit Root Using Forward and Reverse Dickey-Fuller regressions, Oxford Bulletin of Economics and Statistics, vol. 57, 559-571.
• Mehra, Y. P. (1994), Wage Growth and the Inflation Process: an Empirical Approach, in: Bhaskara, B., Rao, Cointegration for the applied economist, St. Matin's Press.
• Pesaran, M., Pesaran, H. B. (1997), Working with Microfit 4.0, Oxford University Press, Oxford - New York.
• Toda, H. Y., Yamamoto, T. (1995), Statistical Inference in Vector Autoregressions with Possibly Integrated Processes, Journal of Econometrics, vol, 66, 225-250.