Faktoru modeļu agregēta un dezagregēta pieeja IKP prognožu precizitātes mērīšanā
[Measuring GDP forecasting accuracy using factor models: aggregated vs. disaggregated approach]
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%.
|Date of creation:||02 Apr 2010|
|Date of revision:|
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Web page: https://mpra.ub.uni-muenchen.de
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- 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.
- Caggiano, Giovanni & Kapetanios, George & Labhard, Vincent, 2009. "Are more data always better for factor analysis? Results for the euro area, the six largest euro area countries and the UK," Working Paper Series 1051, European Central Bank.
- Christian Schumacher & Christian Dreger, 2004.
"Estimating Large-Scale Factor Models for Economic Activity in Germany: Do They Outperform Simpler Models?,"
Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik),
Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 224(6), pages 731-750, November.
- Dreger, Christian & Schumacher, Christian, 2002. "Estimating large-scale factor models for economic activity in Germany : do they outperform simpler models?," HWWA Discussion Papers 199, Hamburg Institute of International Economics (HWWA).
- James H. Stock & Mark W. Watson, 1998. "Diffusion Indexes," NBER Working Papers 6702, National Bureau of Economic Research, Inc.
- Boivin, Jean & Ng, Serena, 2006.
"Are more data always better for factor analysis?,"
Journal of Econometrics,
Elsevier, vol. 132(1), pages 169-194, May.
- Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
- 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.
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