Abstract
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.
Highlights
The role of money in the case of monetary policy and its impact on the real ecomony have been thoroughly discussed in the literature
We extend the Factor-Augmented Vector Autoregression Model (FAVAR) of Bernanke et al (2005) to ragged panel data and make the following three contributions to the existing literature: First, two EMs estimate the model parameters and reconstruct missing obersations in the form of an iterative scheme
We investigate the effects of the United States (US) monetary policy on its real economy
Summary
The role of money in the case of monetary policy and its impact on the real ecomony have been thoroughly discussed in the literature. The estimation of FAVARs relies on complete panel data, whose updating frequency is either monthly (Bernanke et al 2005; Bork 2009; Wu and Xia 2014) or quarterly (Ellis et al 2014). Bańbura and Modugno (2014) as well as Bork (2015) admit time-dependent selection matrices to exclude missing data from their MLE Their state-space representations already take into account which data type each variable belongs to and so, they have a single EM instead of two. We extend the FAVAR of Bernanke et al (2005) to ragged panel data and make the following three contributions to the existing literature: First, two EMs estimate the model parameters and reconstruct missing obersations in the form of an iterative scheme. The appendices provide detailed algorithms, results of the Monte Carlo (MC) simulations, data descriptions and illustrations of the empirical study
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