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Survey Data as Coincident or Leading Indicators

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Author Info
Cecilia Frale ()
Massimiliano Marcellino ()
Gian Luigi Mazzi ()
Tommaso Proietti ()

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Abstract

In 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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File URL: http://www.dt.tesoro.it/modules/documenti_it/analisi_progammazione/working_papers/WP_n_3_2009.pdf
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Paper provided by Department of the Treasury, Ministry of the Economy and of Finance in its series Working Papers with number 3.

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Handle: RePEc:itt:wpaper:wp2009-3

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Related research
Keywords: Survey data; Forecasting; Temporal Disaggregation; Dynamic factor modes; Kalman Filter and smoother;

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Find related papers by JEL classification:
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
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications

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  1. Tommaso Proietti & Cecilia Frale, 2007. "New proposals for the quantification of qualitative survey data," CEIS Research Paper 98, Tor Vergata University, CEIS. [Downloadable!]
  2. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May. [Downloadable!] (restricted)
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  3. Robert B. Litterman, 1983. "A random walk, Markov model for the distribution of time series," Staff Report 84, Federal Reserve Bank of Minneapolis. [Downloadable!]
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  4. Altissimo, Filippo & Cristadoro, Riccardo & Forni, Mario & Lippi, Marco & Veronese, Giovanni, 2006. "New EuroCOIN: Tracking Economic Growth in Real Time," CEPR Discussion Papers 5633, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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  5. Demos, Antonis & Sentana, Enrique, 1998. "Testing for GARCH effects: a one-sided approach," Journal of Econometrics, Elsevier, vol. 86(1), pages 97-127, June. [Downloadable!] (restricted)
  6. Tommaso Proietti & Filippo Moauro, 2004. "Dynamic Factor Analysis with Nonlinear Temporal Aggregation Constraints," Econometrics 0401003, EconWPA. [Downloadable!]
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