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2005 | nr 75 | 77--97
Tytuł artykułu

The Usefulness of Business Surveys Data for Short-Term Forecasting Raw Data vs Seasonally Adjusted and Smoothed One

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The main aim of this paper is to show how important are seasonal adjustment and smoothing the data from business tendency surveys in analyses and short-term forecasting of economic activity (a quantitative index). Using data for Poland (the business tendency surveys in manufacturing conducted by RIED, WSE), the authors try to show that the course of transition of the economy from centrally-planned regime to market one is characterised by changes of a seasonal pattern of industrial activity. This makes any conclusions drawn from an analysis of raw BTS indicators be possibly misleading and calls for application of seasonal adjustment methods which are able to model variable seasonality. In order to conduct this research the authors decompose time series of qualitative indicators into seasonal and irregular factors and a trend+cycle component, using the TRAMO-SEATS procedure. Next the authors study patterns of seasonal factors and its changes throughout the transition process and, finally, consistency and correlation of a quantitative manufacturing index with qualitative indicators resulting from the business tendency surveys, seasonally adjusted and smoothed. Using this data some short-term forecasts are made. (original abstract)
Twórcy
  • Warsaw School of Economics, Poland
  • Warsaw School of Economics, Poland
  • Warsaw School of Economics, Poland
Bibliografia
  • Adamowicz E., Dudek S., Walczyk K., (2004), The Use of Survey Data in Macroeconomic Analysis, in: Composite Indicators of Business Activity for Macroeconomic Analysisis, ed. Matkowski Z., Prace i Materiały Instytutu Rozwoju Gospodarczego, Zeszyt 74, SGH, pp. 357-376.
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  • Blake A., Kapetanios G., Weale M.R., (2000), Nowcasting EU Industrial Production and Manufacturing Output, NIESR.
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  • Demetra 2.0 - Pedagogical Manual (2002) EUROSTAT.
  • Demetra 2.0 - User manual (2002) EUROSTAT.
  • Dramais A., (2003), Forecasting Industrial Production from Business Survey Data for the Euro Area, ECOMOD conference on Policy Modeling - Instanbul - 3-5 July 2003.
  • Etter R., Graff M., (2002), Estimating and Forecasting Production and Orders in Manufacturing Industry from Business Survey Data, 26th CIRET Conference, Taipei.
  • European Commission DG ECFIN (1997), The Joint Harmonised EU Programme of Business and Consumer Surveys, Europena Economy - Raport and Studies No 6, Belgium.
  • EViews 4 Command and Programming Reference (2000), Quantitative Micro Software.
  • EViews 4 User's Guide (2000), Quantitative Micro Software.
  • Gerli M., Petrucci A., (1995), The Econometric Anticipation of the Industrial Production Index. Some Results Based on Business Survey Data, 22nd CIRET Conference, Singapore.
  • Gómez V., Maravall A., (1996), Programs TRAMO (Time series Regression with Arima Noise, Missing observations and Outliers) and SEATS (Signal Extraction in Arima Time Series). Instructions for the user, Working Paper, nr 9628, Servicio de Estudios, Banco de España.
  • Kangasniemi J., Takala K., Tsupari P., (2000), Seasonality and Smoothing of Business Survey Data, 25th CIRET Conference, Paris 2000.
  • OECD (2003), Business Tendency Surveys - A Handbook, OECD.
  • Oppenlander K.H., (2002), Business Cycle Survey Data: Definition, Importance, and Applications, 26th CIRET Conference, Taipei.
  • Rocki M., Tabeau A., (1995), Kwantyfikacja jakościowych danych ankietowych dla produkcji przemysłu w Polsce, "Roczniki Kolegium Analiz Ekonomicznych", Zeszyt nr 2, SGH, Warsaw.
  • Tomczyk E., (2001), Racjonalność oczekiwań respondentów testu koniunktury [in:] Analiza tendencji rozwojowych w polskiej gospodarce na podstawie testu koniunktury, Prace i Materiały Instytutu Rozwoju Gospodarczego, Zeszyt nr 70, Szkoła Główna Handlowa, Warsaw.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
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