Factor-Augmented VARMA Models With Macroeconomic Applications

Jean‐Marie Dufour · Dalibor Stevanović

31 citations0/0 files storedJournal of Business and Economic Statistics

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Monetary Policy and Economic Impact Forecasting Techniques and Applications Spatial and Panel Data Analysis

Abstract

We study the relationship between VARMA and factor representations of a vector stochastic process. We observe that, in general, vector time series and factors cannot both follow finite-order VAR models. Instead, a VAR factor dynamics induces a VARMA process, while a VAR process entails VARMA factors. We propose to combine factor and VARMA modeling by using factor-augmented VARMA (FAVARMA) models. This approach is applied to forecasting key macroeconomic aggregates using large U.S. and Canadian monthly panels. The results show that FAVARMA models yield substantial improvements over standard factor models, including precise representations of the effect and transmission of monetary policy.

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References

2024The Quarterly Journal of Economics 2002Determining the Number of Factors in Approximate Factor Models 2002Forecasting Using Principal Components From a Large Number of Predictors 2002Macroeconomic Forecasting Using Diffusion Indexes 1993Introduction to Multiple Time Series Analysis 2005Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach 2000The Generalized Dynamic-Factor Model: Identification and Estimation 2003Inferential Theory for Factor Models of Large Dimensions

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2019Macroeconomic forecast accuracy in a data‐rich environment 2018Bootstrap inference for impulse response functions in factor‐augmented vector autoregressions 2016Structural analysis with Multivariate Autoregressive Index models 2014Structural Vector Autoregressive Analysis in a Data Rich Environment: A Survey

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