Estimation of FAVAR models for incomplete data with a Kalman filter for factors with observable components

Franz Ramsauer, Aleksey Min, Michael Lingauer

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


This article extends the Factor-Augmented Vector Autoregression Model (FAVAR) to mixed-frequency and incomplete panel data. Within the scope of a fully parametric two-step approach, the alternating application of two expectation-maximization algorithms jointly estimates model parameters and missing data. In contrast to the existing literature, we do not require observable factor components to be part of the panel data. For this purpose, we modify the Kalman Filter for factors consisting of latent and observed components, which significantly improves the reconstruction of latent factors according to the performed simulation study. To identify model parameters uniquely, the loadings matrix is constrained. In our empirical application, the presented framework analyzes US data for measuring the effects of the monetary policy on the real economy and financial markets. Here, the consequences for the quarterly Gross Domestic Product (GDP) growth rates are of particular importance.

Original languageEnglish
Article number31
Issue number3
StatePublished - Sep 2019


  • Expectation-maximization algorithm
  • Factor-augmented vector autoregression model
  • Forecast error variance decomposition
  • Impulse response function
  • Incomplete data
  • Kalman Filter


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