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Forecast Densities for Economic Aggregates from Disaggregate Ensembles

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  • Francesco Ravazzolo

    ()

  • Shaun P. Vahey

    ()

Abstract

We propose a methodology for producing forecast densities for economic aggregates based on disaggregate evidence. Our ensemble predictive methodology utilizes a linear mixture of experts framework to combine the forecast densities from potentially many component models. Each component represents the univariate dynamic process followed by a single disaggregate variable. The ensemble produced from these components approximates the many unknown relationships between the disaggregates and the aggregate by using time-varying weights on the component forecast densities. In our application, we use the disaggregate ensemble approach to forecast US Personal Consumption Expenditure inflation from 1997Q2 to 2008Q1. Our ensemble combining the evidence from 11 disaggregate series outperforms an aggregate autoregressive benchmark, and an aggregate time-varying parameter specification in density forecasting.

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Bibliographic Info

Paper provided by Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University in its series CAMA Working Papers with number 2010-10.

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Length: 31 pages
Date of creation: Apr 2010
Date of revision:
Handle: RePEc:een:camaaa:2010-10

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  1. Sangjoon Kim, Neil Shephard & Siddhartha Chib, . "Stochastic volatility: likelihood inference and comparison with ARCH models," Economics Papers, Economics Group, Nuffield College, University of Oxford W26, revised version of W, Economics Group, Nuffield College, University of Oxford.
  2. Todd E. Clark & Michael W. McCracken, 2006. "Averaging forecasts from VARs with uncertain instabilities," Research Working Paper, Federal Reserve Bank of Kansas City RWP 06-12, Federal Reserve Bank of Kansas City.
  3. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
  4. Wallis, Kenneth F., 2003. "Chi-squared tests of interval and density forecasts, and the Bank of England's fan charts," International Journal of Forecasting, Elsevier, Elsevier, vol. 19(2), pages 165-175.
  5. Berkowitz, Jeremy, 2001. "Testing Density Forecasts, with Applications to Risk Management," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 19(4), pages 465-74, October.
  6. Tae-Hwy Lee & Yong Bao & Burak Saltoğlu, 2007. "Comparing density forecast models Previous versions of this paper have been circulated with the title, 'A Test for Density Forecast Comparison with Applications to Risk Management' since October 2003;," Journal of Forecasting, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 26(3), pages 203-225.
  7. Gianni Amisano & Raffaella Giacomini, 2005. "Comparing Density Forecsts via Weighted Likelihood Ratio Tests," Working Papers, University of Brescia, Department of Economics ubs0504, University of Brescia, Department of Economics.
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Cited by:
  1. Francesco Ravazzolo & Philip Rothman, 2013. "Oil and U.S. GDP: A Real‐Time Out‐of‐Sample Examination," Journal of Money, Credit and Banking, Blackwell Publishing, Blackwell Publishing, vol. 45(2-3), pages 449-463, 03.
  2. Billio, Monica & Casarin, Roberto & Ravazzolo, Francesco & van Dijk, Herman K., 2013. "Time-varying combinations of predictive densities using nonlinear filtering," Journal of Econometrics, Elsevier, Elsevier, vol. 177(2), pages 213-232.
  3. Francesco Ravazzolo & Marco J. Lombardi, 2012. "Oil price density forecasts: Exploring the linkages with stock markets," Working Papers, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School 0008, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
  4. Todd E. Clark & Francesco Ravazzolo, 2012. "The macroeconomic forecasting performance of autoregressive models with alternative specifications of time-varying volatility," Working Paper, Norges Bank 2012/09, Norges Bank.

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