Prediction Using Several Macroeconomic Models
43 Pages Posted: 11 May 2013
Date Written: April 17, 2013
Abstract
Prediction of macroeconomic aggregates is one of the primary functions of macroeconometric models, including dynamic factor models, dynamic stochastic general equilibrium models, and vector autoregressions. This study establishes methods that improve the predictions of these models, using a representative model from each class and a canonical 7-variable postwar US data set. It focuses on prediction over the period 1966 through 2011. It measures the quality of prediction by the probability densities assigned to the actual values of these variables, one quarter ahead, by the predictive distributions of the models in real time. Two steps lead to substantial improvement. The first is to use full Bayesian predictive distributions rather than substitute a "plug-in" posterior mode for parameters. Across models and quarters, this leads to a mean improvement in probability of 50.4%. The second is to use an equally-weighted pool of predictive densities from the three models, which leads to a mean improvement in probability of 41.9% over the full Bayesian predictive distributions of the individual models. This improvement is much better than that afforded by Bayesian model averaging. The study uses several analytical tools, including pooling, analysis of predictive variance, and probability integral transform tests, to understand and interpret the improvements.
Keywords: Analysis of variance, Bayesian model averaging, dynamic factor model, dynamic stochastic general equilibrium model, prediction pools, probability integral transform test, vector autoregression model
JEL Classification: C11, C51 C53
Suggested Citation: Suggested Citation
Do you have negative results from your research you’d like to share?
Recommended Papers
-
Real-Time Inflation Forecasting in a Changing World
By Jan J. Groen, Richard Paap, ...
-
Bayesian Multivariate Time Series Methods for Empirical Macroeconomics
By Gary Koop and Dimitris Korobilis
-
Comparing and Evaluating Bayesian Predictive Distributions of Asset Returns
By John Geweke and Gianni Amisano
-
Forecasting Inflation Using Dynamic Model Averaging
By Gary Koop and Dimitris Korobilis
-
Real-Time Density Forecasts from VARs with Stochastic Volatility
-
Assessing the Transmission of Monetary Policy Shocks Using Dynamic Factor Models
-
Prior Selection for Vector Autoregressions
By Domenico Giannone, Michele Lenza, ...
-
Prior Selection for Vector Autoregressions
By Domenico Giannone, Michele Lenza, ...
-
Prior Selection for Vector Autoregressions
By Domenico Giannone, Michele Lenza, ...