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Title: Macroeconomic forecasting with deep factor models Authors:  Simon Lineu Umbach - FernUniversitaet in Hagen (Germany) [presenting]
Abstract: In many macroeconomic forecasting applications, factor models are used to cope with large datasets. Variational autoencoders with macroeconomic factor modeling are aligned, and an extension is proposed to adapt this framework for forecasting exercises. Variational autoencoders are well suited for nonlinear dimensionality reduction. They estimate the distribution of the common latent variables by combining a statistical factor model with a purely data-driven neural network approach. It is demonstrated that the resulting deep factor model can be interpreted as a flexible nonlinear extension of the standard factor model. In the empirical part, it is analyzed whether factor models augmented by neural networks can achieve superior forecasting power. The results suggest significant improvements in the forecasting accuracy of four major US macroeconomic time series.