Macro modelling with many models
AbstractWe argue that the next generation of macro modellers at Inflation Targeting central banks should adapt a methodology from the weather forecasting literature known as `ensemble modelling'. In this approach, uncertainty about model specifications (e.g., initial conditions, parameters, and boundary conditions) is explicitly accounted for by constructing ensemble predictive densities from a large number of component models. The components allow the modeller to explore a wide range of uncertainties; and the resulting ensemble `integrates out' these uncertainties using time-varying weights on the components. We provide two examples of this modelling strategy: (i) forecasting inflation with a disaggregate ensemble; and (ii) forecasting inflation with an ensemble DSGE.
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Bibliographic InfoPaper provided by Norges Bank in its series Working Paper with number 2009/15.
Length: 26 pages
Date of creation: 17 Aug 2009
Date of revision:
Note: First version:
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Ensemble modelling; Forecasting; DSGE models; Density combination;
Other versions of this item:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
- E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-08-30 (All new papers)
- NEP-CBA-2009-08-30 (Central Banking)
- NEP-DGE-2009-08-30 (Dynamic General Equilibrium)
- NEP-ECM-2009-08-30 (Econometrics)
- NEP-FOR-2009-08-30 (Forecasting)
- NEP-MAC-2009-08-30 (Macroeconomics)
- NEP-MON-2009-08-30 (Monetary Economics)
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- Anthony Garratt & James Mitchell & Shaun P. Vahey & Elizabeth C. Wakerly, 2009.
"Real-time Inflation Forecast Densities from Ensemble Phillips Curves,"
Birkbeck Working Papers in Economics and Finance
0910, Birkbeck, Department of Economics, Mathematics & Statistics.
- Garratt, Anthony & Mitchell, James & Vahey, Shaun P. & Wakerly, Elizabeth C., 2011. "Real-time inflation forecast densities from ensemble Phillips curves," The North American Journal of Economics and Finance, Elsevier, vol. 22(1), pages 77-87, January.
- Anthony Garratt & James Mitchell & Shaun P. Vahey & Elizabeth C. Wakerly, 2010. "Real-time Inflation Forecast Densities from Ensemble Phillips Curves," CAMA Working Papers 2010-34, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Francesco Ravazzolo & Shaun P Vahey, 2010. "Measuring Core Inflation in Australia with Disaggregate Ensembles," RBA Annual Conference Volume, in: Renée Fry & Callum Jones & Christopher Kent (ed.), Inflation in an Era of Relative Price Shocks Reserve Bank of Australia.
- Garratt, Anthony & Mitchell, James & Vahey, Shaun P., 2014.
"Measuring output gap nowcast uncertainty,"
International Journal of Forecasting,
Elsevier, vol. 30(2), pages 268-279.
- Miladin Kovačević & Stojan Stamenković, 2010. "Methodological Basis for Macroeconomic Projections in Countries Exposed to Pressures and Shocks: Example of Serbia," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 57(2), pages 225-243, June.
- Chris McDonald & Leif Anders Thorsrud, 2011. "Evaluating density forecasts: model combination strategies versus the RBNZ," Reserve Bank of New Zealand Discussion Paper Series DP2011/03, Reserve Bank of New Zealand.
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