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01-11-2014

Monetary transmission mechanism and time variation in the Euro area

Author: Kemal Bagzibagli

Published in: Empirical Economics | Issue 3/2014

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Abstract

This paper examines the monetary transmission mechanism in the Euro area (EA) for the period of single monetary policy using factor-augmented vector autoregressive (FAVAR) techniques. The aims of the paper are threefold. First, a novel dataset consisting of 120 disaggregated macroeconomic time series spanning the period 1999:M1 through 2011:M12 is gathered for the EA as an aggregate. Second, a Bayesian joint estimation technique of the FAVAR approach is applied to the European data in order to investigate the impacts of monetary policy shocks on the economy. Third, time variation in the transmission mechanism and the impact of the global financial crisis are investigated in the FAVAR context using a rolling windows technique. We find that there are considerable gains from the implementation of the Bayesian technique such as smoother impulse response functions and statistical significance of the estimates. According to our rolling estimations, consumer prices and monetary aggregates display the most time-variant responses to the monetary policy shocks in the EA.

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Here we refer to generally used standard VAR models. Throughout the literature, however, some studies, e.g. Leeper et al. (1996) and Bańbura et al. (2008) managed to employ 13–18 and up to 130 variables, respectively, using Bayesian techniques.

Introduced by Geweke (1977) and further studied by Sargent and Sims (1977), Stock and Watson (1998); Stock and Watson (1999); Stock and Watson (2002a); Stock and Watson (2002b), Giannone et al. (2004), among others.

The summaries of the techniques employed in our study are described in Sect. 2.

See Sect. 4.2.1 for details of determining the pre-crisis period.

See Sect. 2.2 for details.

Technique developed by Geman and Geman (1984), Gelman and Rubin (1992a), and Carter and Kohn (1994), and surveyed in Kim and Nelson (1999).

For further details, see BBE (2005), pp. 400–401.

See Depoutot et al. (1998) for details of the software.

When either the first or the last observation of a series is missing, Demetra+ does not provide any estimation. For this kind of occasional observations, using a MATLAB code obtained from Bańbura and Modugno (2010), we replaced the missing values by the median of the series and then applied a centred MA(3) to the replaced observations. We thank the authors for kindly sharing the replication files of their paper.

We thank Fabio Canova for suggesting this scaling during the presentation of the paper at 2011 Royal Economic Society Easter School held at the University of Birmingham.

We thank Schumacher and Breitung (2008) for making the replication files of their paper publicly available, and also thank Christian Schumacher for sharing the files and his comments with us. Our tests are based on the replication files of the paper.

Eickmeier (2009, p. 939). See also Marcellino et al. (2000), Altissimo et al. (2001), Eickmeier and Breitung (2006), Altissimo et al. (2011).

For software details, see Lütkepohl and Krätzig (2004).

Capacity utilisation rate, gross domestic product, final consumption expenditure, gross fixed capital formation.

Total employment, total employees, total self-employed, real labour productivity per person employed, real unit labour cost.

Earnings per employee, wages and salaries.

Current, capital and financial accounts.

A similar approach has been used by Soares (2011) for the EA in order to have a panel of monthly macroeconomic time series consisting of the variables we have interpolated for our own dataset.

See Sect. 4.2 for details.

The estimation results are found to be robust to the number of Gibbs iterations.

Unsurprisingly, short-term interest rates follow the responses of the policy variable, which, if we recall, is the only observable factor in the transition Equation (2), and its impulse responses can also be calculated in standard ways.

To illustrate, see BBE (2005), Uhlig (2005), Belviso and Milani (2006), McCallum and Smets (2007), Ahmadi and Uhlig (2007), Boivin et al. (2008), Blaes (2009), Bork (2009), among others.

The failure of the negative correlations between nominal interest rates and the money stock expected to be created by monetary policy disturbances. See Kelly et al. (2011).

i.e. \(\hat{\varLambda }^f \hat{F}_t + \hat{\varLambda }^y Y_t\) in the observation Eq. 1.

See Boivin et al. (2008, p. 2).

We thank Gary Koop for valuable discussions and comments during the presentation of the paper at the \(6\mathrm{th}\) annual Bayesian econometrics workshop organised by the Rimini Centre for Economic Analysis (RCEA) in Rimini, Italy in 2011.

Additionally, despite many and quite long trials with the replication files of Koop and Korobilis (2009) to fit both one- and two-step TVP-FAVAR models to our relatively short dataset, we could not obtain any reasonable results. We anyway thank the authors for making the files available to the public.

See Korobilis (2012), Barnett et al. (2012), and references therein.

Still with four factors and two lags.

Only 6-month rolling is estimated by the one-step method. For details, see below.

First three observations are lost due to data transformations explained above in Sect. 3.1.

See Appendix 2.1.

Estimating our FAVAR model window by window means identification of a new 25 basis-point shock specific to that particular sample.

i.e. the monetary authority of the EA which consists of the ECB and national central banks of the countries in the monetary union.

And Figs. 5, 6 and 7 in Appendix 2.1.

Remember, first 2,000 iterations are discarded in order to eliminate the influence of our choice of starting values.

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Title: Monetary transmission mechanism and time variation in the Euro area
Author: Kemal Bagzibagli
Publication date: 01-11-2014
Publisher: Springer Berlin Heidelberg
Published in: Empirical Economics / Issue 3/2014
Print ISSN: 0377-7332
Electronic ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-013-0768-4

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