Combining Predictive Densities Using Nonlinear Filtering with Applications to US Economics Data

Tinbergen Institute Discussion Paper No. 11-172/4

55 Pages Posted: 5 Dec 2011

See all articles by Monica Billio

Monica Billio

Ca Foscari University of Venice - Dipartimento di Economia

Roberto Casarin

University Ca' Foscari of Venice - Department of Economics

Francesco Ravazzolo

Free University of Bolzano

H. K. van Dijk

Tinbergen Institute; Econometric Institute

Date Written: November 30, 2011

Abstract

We propose a multivariate combination approach to prediction based on a distributional state space representation of the weights belonging to a set of Bayesian predictive densities which have been obtained from alternative models. Several specifications of multivariate time-varying weights are introduced with a particular focus on weight dynamics driven by the past performance of the predictive densities and the use of learning mechanisms. In the proposed approach the model set can be incomplete, meaning that all models are individually misspecified. The approach is assessed using statistical and utility-based performance measures for evaluating density forecasts of US macroeconomic time series and surveys of stock market prices. For the macro series we find that incompleteness of the models is relatively large in the 70's, the beginning of the 80's and during the recent financial crisis; structural changes like the Great Moderation are empirically identified by our model combination and the predicted probabilities of recession accurately compare with the NBER business cycle dating. Model weights have substantial uncertainty attached and neglecting this may seriously affect results. With respect to returns of the S&P 500 series, we find that an investment strategy using a combination of predictions from professional forecasters and from a white noise model puts more weight on the white noise model in the beginning of the 90's and switches to giving more weight to the left tail of the professional forecasts during the start of the financial crisis around 2008.

Keywords: density forecast combination, survey forecast, nonlinear filtering, sequential Monte Carlo

JEL Classification: C11, C15, C53, E37

Suggested Citation

Billio, Monica and Casarin, Roberto and Ravazzolo, Francesco and van Dijk, Herman K., Combining Predictive Densities Using Nonlinear Filtering with Applications to US Economics Data (November 30, 2011). Tinbergen Institute Discussion Paper No. 11-172/4. Available at SSRN: https://ssrn.com/abstract=1967435 or http://dx.doi.org/10.2139/ssrn.1967435

Monica Billio (Contact Author)

Ca Foscari University of Venice - Dipartimento di Economia ( email )

Cannaregio 873
Venice, 30121
Italy

HOME PAGE: http://www.unive.it/persone/billio

Roberto Casarin

University Ca' Foscari of Venice - Department of Economics ( email )

San Giobbe 873/b
Venice, 30121
Italy
+39 030.298.91.49 (Phone)
+39 030.298.88.37 (Fax)

HOME PAGE: http://venus.unive.it/r.casarin/

Francesco Ravazzolo

Free University of Bolzano ( email )

Bolzano
Italy

Herman K. Van Dijk

Tinbergen Institute ( email )

Gustav Mahlerplein 117
Burg. Oudlaan 50
Amsterdam/Rotterdam, 1082 MS
Netherlands
+31104088955 (Phone)
+31104089031 (Fax)

HOME PAGE: http://people.few.eur.nl/hkvandijk/

Econometric Institute ( email )

P.O. Box 1738
3000 DR Rotterdam
Netherlands
+31 10 4088955 (Phone)
+31 10 4527746 (Fax)

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