Estimation of FAVAR Models for Incomplete Data with a Kalman Filter for Factors with Observable Components
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-07-01
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| Series: | Econometrics |
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| Online Access: | https://www.mdpi.com/2225-1146/7/3/31 |
| _version_ | 1817989664210944000 |
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| author | Franz Ramsauer Aleksey Min Michael Lingauer |
| author_facet | Franz Ramsauer Aleksey Min Michael Lingauer |
| author_sort | Franz Ramsauer |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T00:49:27Z |
| format | Article |
| id | doaj.art-07569dbb5d9546d08c5b0af70ab93a2e |
| institution | Directory Open Access Journal |
| issn | 2225-1146 |
| language | English |
| last_indexed | 2024-04-14T00:49:27Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Econometrics |
| spelling | doaj.art-07569dbb5d9546d08c5b0af70ab93a2e2022-12-22T02:21:51ZengMDPI AGEconometrics2225-11462019-07-01733110.3390/econometrics7030031econometrics7030031Estimation of FAVAR Models for Incomplete Data with a Kalman Filter for Factors with Observable ComponentsFranz Ramsauer0Aleksey Min1Michael Lingauer2Department of Mathematics, Technical University of Munich, 85748 Munich, GermanyDepartment of Mathematics, Technical University of Munich, 85748 Munich, GermanyDepartment of Mathematics, Technical University of Munich, 85748 Munich, GermanyThis 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.https://www.mdpi.com/2225-1146/7/3/31expectation-maximization algorithmfactor-augmented vector autoregression modelforecast error variance decompositionimpulse response functionincomplete dataKalman Filter |
| spellingShingle | Franz Ramsauer Aleksey Min Michael Lingauer Estimation of FAVAR Models for Incomplete Data with a Kalman Filter for Factors with Observable Components Econometrics expectation-maximization algorithm factor-augmented vector autoregression model forecast error variance decomposition impulse response function incomplete data Kalman Filter |
| title | Estimation of FAVAR Models for Incomplete Data with a Kalman Filter for Factors with Observable Components |
| title_full | Estimation of FAVAR Models for Incomplete Data with a Kalman Filter for Factors with Observable Components |
| title_fullStr | Estimation of FAVAR Models for Incomplete Data with a Kalman Filter for Factors with Observable Components |
| title_full_unstemmed | Estimation of FAVAR Models for Incomplete Data with a Kalman Filter for Factors with Observable Components |
| title_short | Estimation of FAVAR Models for Incomplete Data with a Kalman Filter for Factors with Observable Components |
| title_sort | estimation of favar models for incomplete data with a kalman filter for factors with observable components |
| topic | expectation-maximization algorithm factor-augmented vector autoregression model forecast error variance decomposition impulse response function incomplete data Kalman Filter |
| url | https://www.mdpi.com/2225-1146/7/3/31 |
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