Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data

Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data

This article considers the estimation of Approximate Dynamic Factor Models with homoscedastic, cross-sectionally correlated errors for incomplete panel data. In contrast to existing estimation approaches, the presented estimation method comprises two expectation-maximization algorithms and uses cond...

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Bibliographic Details
Main Authors: Monica Defend, Aleksey Min, Lorenzo Portelli, Franz Ramsauer, Francesco Sandrini, Rudi Zagst
Format: Article
Language:English
Published: MDPI AG 2021-02-01
Series:Forecasting
Subjects:
approximate dynamic factor model
expectation-maximization algorithm
forecasting
incomplete data
mixed-frequency information
prediction interval
Online Access:https://www.mdpi.com/2571-9394/3/1/5

Internet

https://www.mdpi.com/2571-9394/3/1/5

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