Deep vector autoregression for macroeconomic data

Agustí, Marc; Altmeyer, Patrick; Vidal-Quadras, Ignacio

Deep vector autoregression for macroeconomic data

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Agustí, M, Altmeyer, P, Vidal-Quadras, I. Deep vector autoregression for macroeconomic data. 2021. handle: http://hdl.handle.net/10230/49204

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Abstract

Vector autoregression (VAR) models are a popular choice for forecasting of macroeconomic time series data. Due to their simplicity and success at modelling the monetary economic indicators VARs have become a standard tool for central bankers to construct economic forecasts. In light of the recent advancements in computational power and the development of advanced machine learning and deep learning algorithms we propose a simple way to integrate these tools into the VAR framework. This paper aims to contribute to the time series literature by introducing a ground-breaking methodology which we refer to as Deep Vector Autoregression (Deep VAR). By fitting each equation of the VAR system with a deep neural network, the Deep VAR outperforms the VAR in terms of in-sample fit, out-of-sample fit and point forecasting accuracy. In particular, we find that the Deep VAR is able to better capture the structural economic changes during periods of uncertainty and recession.