Survey Data as Coicident or Leading Indicators
AbstractIn this paper we propose a monthly measure for the euro area Gross Domestic Product (GDP) based on a small scale factor model for mixed frequency data, featuring two factors: the first is driven by hard data, whereas the second captures the contribution of survey variables as coincident indicators. Within this framework we evaluate both the in-sample contribution of the second survey-based factor, and the short term forecasting performance of the model in a pseudo-real time experiment. We find that the survey-based factor plays a significant role for two components of GDP: Industrial Value Added and Exports. Moreover, the two factor model outperforms in terms of out of sample forecasting accuracy the traditional autoregressive distributed lags (ADL) specifications and the single factor model, with few exceptions for Exports and in growth rates.
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Bibliographic InfoPaper provided by European University Institute in its series Economics Working Papers with number ECO2009/19.
Date of creation: 2009
Date of revision:
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More information through EDIRC
Survey data; Temporal Disaggregation. Multivariate State Space Models. Dynamic factor Models. Kalman filter and smoother. Chain-linking;
Other versions of this item:
- E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
- E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-07-03 (All new papers)
- NEP-CBA-2009-07-03 (Central Banking)
- NEP-ECM-2009-07-03 (Econometrics)
- NEP-EEC-2009-07-03 (European Economics)
- NEP-FOR-2009-07-03 (Forecasting)
- NEP-MAC-2009-07-03 (Macroeconomics)
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