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Faktoru modeļu agregēta un dezagregēta pieeja IKP prognožu precizitātes mērīšanā
[Measuring GDP forecasting accuracy using factor models: aggregated vs. disaggregated approach]

Author

Listed:
  • Bessonovs, Andrejs

Abstract

The purpose of this paper is to conduct whether the disaggregated data of GDP gives us any additional information in the sense of forecasting accuracy. To test latter hypothesis author employs Stock-Watson factor model. GDP is disaggregated both on expenditure basis and on output basis. Thus both approaches should widen overlook to comparison’s capability. In order to measure forecasting accuracy root mean squared error measure was employed. Author concludes that disaggregated approach outperforms aggregated data but at very little extent. In addition, factor model showed better results in the sense of forecasting accuracy and outperformed univariate models on average by 20-30%.

Suggested Citation

  • Bessonovs, Andrejs, 2010. "Faktoru modeļu agregēta un dezagregēta pieeja IKP prognožu precizitātes mērīšanā [Measuring GDP forecasting accuracy using factor models: aggregated vs. disaggregated approach]," MPRA Paper 30386, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:30386
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    File URL: https://mpra.ub.uni-muenchen.de/30386/1/MPRA_paper_30386.pdf
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    References listed on IDEAS

    as
    1. Dreger, Christian & Schumacher, Christian, 2002. "Estimating Large-Scale Factor Models for Economic Activity in Germany: Do They Outperform Simpler Models?," Discussion Paper Series 26321, Hamburg Institute of International Economics.
    2. Aleksejs Melihovs & Svetlana Rusakova, 2005. "Short-Term Forecasting of Economic Development in Latvia Using Business and Consumer Survey Data," Working Papers 2005/04, Latvijas Banka.
    3. Giovanni Caggiano & George Kapetanios & Vincent Labhard, 2011. "Are more data always better for factor analysis? Results for the euro area, the six largest euro area countries and the UK," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(8), pages 736-752, December.
    4. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    5. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    6. James H. Stock & Mark W. Watson, 1998. "Diffusion Indexes," NBER Working Papers 6702, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Factor model; out-of-sample forecasting; disaggregated approach; real-time database.;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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