Conditional Forecasts in Dynamic Multivariate Models

Daniel F. Waggoner · Tao A. Zha

267 citations0/0 files storedThe Review of Economics and Statistics

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Forecasting Techniques and Applications Monetary Policy and Economic Impact Statistical Methods and Bayesian Inference

Abstract

In the existing literature, conditional forecasts in the vector autoregressive (VAR) framework have not been commonly presented with probability distributions. This paper develops Bayesian methods for computing the exact finite-sample distribution of conditional forecasts. It broadens the class of conditional forecasts to which the methods can be applied. The methods work for both structural and reduced-form VAR models and, in contrast to common practices, account for parameter uncertainty in finite samples. Empirical examples under both a flat prior and a reference prior are provided to show the use of these methods.

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References

1986The theory and practice of econometrics 1998Monetary Policy Shocks: What Have We Learned and to What End?1993Introduction to Multiple Time Series Analysis 1998Evaluating Density Forecasts with Applications to Financial Risk Management 1996Asymptotic Inference about Predictive Ability 1986Forecasting With Bayesian Vector Autoregressions—Five Years of Experience 1983Forecasting and Conditional Projection Using Realistic Prior Distributions 1984Forecasting and conditional projection using realistic prior distributions

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2014Real-Time Forecasting With a Mixed-Frequency VAR 2013Bayesian VARs: Specification Choices and Forecast Accuracy 2013Forecasting with Bayesian Vector Autoregression 2007Vector Autoregressive Models for Multivariate Time Series 1999Vector Autoregressions: Forecasting and Reality 2015Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections

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