Forecast Density Combinations with Dynamic Learning for Large Data Sets in Economics and Finance

Tinbergen Institute Discussion Paper 2019-025/III

79 Pages Posted: 2 May 2019

See all articles by Roberto Casarin

Roberto Casarin

University Ca' Foscari of Venice - Department of Economics

Stefano Grassi

University of Rome Tor Vergata

Francesco Ravazzollo

affiliation not provided to SSRN

H. K. van Dijk

Tinbergen Institute; Econometric Institute

Date Written: March 31, 2019

Abstract

A flexible forecast density combination approach is introduced that can deal with large data sets. It extends the mixture of experts approach by allowing for model set incompleteness and dynamic learning of combination weights. A dimension reduction step is introduced using a sequential clustering mechanism that allocates the large set of forecast densities into a small number of subsets and the combination weights of the large set of densities are modelled as a dynamic factor model with a number of factors equal to the number of subsets. The forecast density combination is represented as a large finite mixture in nonlinear state space form. An efficient simulation-based Bayesian inferential procedure is proposed using parallel sequential clustering and filtering, implemented on graphics processing units. The approach is applied to track the Standard & Poor 500 index combining more than 7000 forecast densities based on 1856 US individual stocks that are are clustered in a relatively small subset. Substantial forecast and economic gains are obtained, in particular, in the tails using Value-at-Risk. Using a large macroeconomic data set of 142 series, similar forecast gains, including probabilities of recession, are obtained from multivariate forecast density combinations of US real GDP, Inflation, Treasury Bill yield and Employment. Evidence obtained on the dynamic patterns in the financial as well as macroeconomic clusters provide valuable signals useful for improved modelling and more effective economic and financial policies.

Keywords: Forecast Combinations, Particle Filters, Bayesian Inference, State Space Models, Sequential Monte Carlo

JEL Classification: C11, C14, C15

Suggested Citation

Casarin, Roberto and Grassi, Stefano and Ravazzollo, Francesco and van Dijk, Herman K., Forecast Density Combinations with Dynamic Learning for Large Data Sets in Economics and Finance (March 31, 2019). Tinbergen Institute Discussion Paper 2019-025/III, Available at SSRN: https://ssrn.com/abstract=3363556 or http://dx.doi.org/10.2139/ssrn.3363556

Roberto Casarin (Contact Author)

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://sites.google.com/view/robertocasarin

Stefano Grassi

University of Rome Tor Vergata ( email )

Via Cracovia 1
Rome, 00133
Italy

Francesco Ravazzollo

affiliation not provided to SSRN

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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