A Nine Variable Probabilistic Macroeconomic Forecasting Model

Christopher A. Sims

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Forecasting Techniques and Applications Monetary Policy and Economic Impact Financial Risk and Volatility Modeling

Abstract

A model for U.S. macroeconomic time series that has been used for forecasting for several years is described in some detail. The model is a multivariate Bayesian autoregression, with allowance for conditional heteroskedasticity, stochastic time-variation in parameters, and non-normality of disturbances. It specifies the prior distribution in ways that improve on previous Bayesian vector autoregression specifications in realism and forecasting performance. The model's record of forecasting in recent years is displayed and discussed.

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1998Bayesian Methods for Dynamic Multivariate Models 2005Implications of Dynamic Factor Models for VAR Analysis 1996Evidence on Structural Instability in Macroeconomic Time Series Relations 2009Bayesian Multivariate Time Series Methods for Empirical Macroeconomics 2015US Monetary Policy and the Global Financial Cycle 2013Forecasting Inflation 2025The Long-Run Effects of Government Spending 2006Twenty-five years of progress, problems, and conflicting evidence in econometric forecasting. What about the next 25 years?

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