Estimation and Inference of FAVAR Models

Bai, Jushan; Li, Kunpeng; Liu, Lu

doi:10.1080/07350015.2015.1111222

Cited by 50 publications

(62 citation statements)

References 43 publications

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“…The relatively small number of variables in a small VAR model may not be sufficient to properly identify shocks, which increases the risk of a biased estimate (see Bai et al, 2016, for detailed review). On the other hand, including more series in a VAR model is limited because of the loss of degrees of freedom.…”

Section: Methodsmentioning

confidence: 99%

Placing the Czech Shadow Banking Sector under the Light

Hodula¹,

Macháček²,

Melecky³

2020

Prague Economic Papers

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The size of the shadow banking sector (SBS) has more than doubled in the Czech Republic over the last decade. This places a potential burden on policy makers. On the one hand, the SBS complements regular banking by expanding access to credit and investments, enabling better risk sharing and maturity transformation, and supporting market liquidity. On the other hand, SBS activities can put the stability of the financial system at risk and amplify its procyclicality by exacerbating the buildup of leverage and asset price bubbles. We implement a FAVAR model of the Czech economy to determine the impact of macroeconomic factors on the SBS. We find that the SBS: (i) is sensitive to changes in market interest rates and term spread; (ii) exhibits great procyclicality; (iii) can act as a complement to regular banking and satisfy some additional demand for credit. We also define some potential risks of continued growth of the SBS, linked to our empirical evidence.

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Section: Methodsmentioning

confidence: 99%

Placing the Czech Shadow Banking Sector under the Light

Hodula¹,

Macháček²,

Melecky³

2020

Prague Economic Papers

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“…We start with the definition of FAVARs and show that parameter ambiguity may affect the covariance matrices of idiosyncratic shocks. At this stage, we include identification conditions from Bai et al (2015). In a next step, we modify the KF from Bork (2009) to take into account that factors are partially observable.…”

Section: Mathematical Backgroundmentioning

confidence: 99%

“…with R −1 as the inverse of matrix R. The observability of Y t imposes constraints on the shape of R and so, removes M (K + M) degrees of freedom (Bai et al 2015). Consequently, the invertible matrix R consists of the following submatrices:…”

Section: Parameter Ambiguity and Identification Restrictionsmentioning

confidence: 99%

Estimation of FAVAR Models for Incomplete Data with a Kalman Filter for Factors with Observable Components

2019

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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.

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“…Accordingly, we first estimate the unobserved factors F t nonparametrically by means of principal component analysis (PCA), and subsequently evaluate the ST-VAR conditional on the estimated factors. The two-step PC estimator does not build upon strong distributional assumptions on the factor innovations as it is the case for the two-step ML approach of Bai et al (2016) or the simultaneous estimation of observation and transition equations by means of Bayesian methods (Bernanke et al, 2005). 13 Bernanke et al (2005) document only minor performance leads of the computationally considerably more demanding Bayesian estimation by means of Gibbs sampling.…”

Section: Estimation and Identificationmentioning

confidence: 99%

Less Bang for the Buck? Assessing the Role of Inflation Uncertainty for U.S. Monetary Policy Transmission in a Data Rich Environment

Herwartz

Rohloff

2018

SSRN Journal

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We investigate the relationship between inflation uncertainty and monetary policy transmission in the U.S. economy. Monetary policy shocks are identified within the framework of nonlinear structural factor-augmented VARs which allow us to analyze several complementary hypotheses connecting IU with reduced monetary policy effectiveness. We find that the real effects of monetary policy shocks are markedly dampened conditional on high IU. This can be traced back to, inter alia, real-option and precautionary savings effects which distort the traditional interest rate channel. Moreover, policy transmission through the external finance premium and the term structure of interest rates appears strongly dependent on inflation uncertainty and contributes to the reduced policy effectiveness.

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Estimation and Inference of FAVAR Models

Cited by 50 publications

References 43 publications

Placing the Czech Shadow Banking Sector under the Light

Placing the Czech Shadow Banking Sector under the Light

Estimation of FAVAR Models for Incomplete Data with a Kalman Filter for Factors with Observable Components

Less Bang for the Buck? Assessing the Role of Inflation Uncertainty for U.S. Monetary Policy Transmission in a Data Rich Environment

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