Abstract
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 conditional factor moments in closed form. To determine the unknown factor dimension and autoregressive order, we propose a two-step information-based model selection criterion. The performance of our estimation procedure and the model selection criterion is investigated within a Monte Carlo study. Finally, we apply the Approximate Dynamic Factor Model to real-economy vintage data to support investment decisions and risk management. For this purpose, an autoregressive model with the estimated factor span of the mixed-frequency data as exogenous variables maps the behavior of weekly S&P500 log-returns. We detect the main drivers of the index development and define two dynamic trading strategies resulting from prediction intervals for the subsequent returns.
Highlights
This paper aims at the estimation of Approximate Dynamic Factor Models (ADFMs) for incomplete panel data in the maximum likelihood framework
It contributes to the existing estimation methodology in the following manner: First, we explicitly allow for iid cross-sectionally correlated errors similar to Jungbacker et al [26] but do not undertake any adaptations for an underlying
We develop a framework for forecasting weekly returns using the estimated factors to determine their main driving indicators of different frequencies
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. We estimate Approximate Dynamic Factor Models (ADFMs) with incomplete panel data. This paper aims at the estimation of ADFMs for incomplete panel data in the maximum likelihood framework It contributes to the existing estimation methodology in the following manner: First, we explicitly allow for iid cross-sectionally correlated errors similar to Jungbacker et al [26] but do not undertake any adaptations for an underlying. Our Matlab codes and data are available as supplementary materials
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