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|
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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.
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