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National information and euro area monetary policy: a generalized ordered choice approach

Empirical Economics volume 54, pages 501–522 (2018)Cite this article

Abstract

We empirically analyze the influence of inflationary pressure originating from persistent national misalignments on the ECB’s interest rate decisions between 2000 and mid-2010. To do so, we introduce an indicator that summarizes the threat to euro area price stability originating from self-reinforcing expected inflation differentials. The indicator is computed based on persistent deviations of national expected inflation and GDP growth rates from the corresponding euro area aggregate. It thereby captures area-wide excess demand pressure on the euro area inflation rate. In order to determine the information content of this indicator, we add it to an empirical monetary policy reaction function. We then analyze this reaction function in the framework of a generalized ordered choice model that fits the data a lot better than its commonly used, more restricted counterpart. Within this empirical framework, we find that after controlling for several area-wide aggregates, national information does not provide additional information that is indicative of the ECB’s policy rate decision.

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Notes

  1. See, e.g., Beck et al. (2009) or Angeloni and Ehrmann (2007) for a discussion on the causes and properties of euro area inflation differentials.

  2. Note that it is not the responsibility of the ECB to address the cause of differentials that originate from factors outside the control of monetary policy such as for example structural differences in labor markets. A discussion on the underlying causes of inflation differentials in the euros area is provided in ECB (2012).

  3. A similar line of argument can be found in the model of Angeloni and Ehrmann (2007) who explain differentials in actual inflation rates.

  4. Alternatively, we also consider a slightly shorter horizon of six months but do not find that it changes the results of our study.

  5. One-year-ahead inflation forecasts, for example, are computed as follows: \(F_{m,j+1}^{\pi }= \frac{13-m}{12}F_{m,12,j}^{\pi }+\frac{m-1}{12}F_{m,12,j+1}^{\pi }\), where \(F_{m,j+1}^{\pi }\) denotes the individual one-year-ahead inflation forecast for month m, with \(m=1,\dots , 12\) of year \(j+1\) and \(F_{m,12,j}\) and \(F_{m,12,j+1}\) are the inflation forecasts issued in month m for year j and \(j+1\), respectively. Thus, our forecast \(F_{m,j+1}^{\pi }\) is the weighted average of the forecasts for the current as well as the subsequent year.

  6. We start to determine the persistence of the differential 1 year prior to the time they are displayed in the figure. That is we start to determine persistence as of January 1999 for all countries but Greece and as of January 2000 for Greece as it joined the union in 2001.

  7. See https://www.ecb.europa.eu/stats/prices/hicp/html/hicp_coicop_inw_2015.en.html. The same weights are also used by the ECB to aggregate the national figures into the euro area-wide inflation rate. We use the shares as of 1999 for the period prior to 2001, i.e., before Greece joined. For the period thereafter, we use weights as of 2006 which is the latest date that depicts the same country constellation within the euro area as given in this study. We neglect the weight of Luxembourg for which we do not have data.

  8. The MRO rate is taken from the ECB’s website, while all other data we use are obtained from the ECB real-time database unless stated otherwise. We take the data for the MRO rate as of the 28th of each month. This allows us to incorporate the interest rate decision even when it has not been made at the first monthly meeting of the ECB’s Governing Council. However, the interest rate change on August 31, 2001, has therefore not been taken into account.

  9. For the derivation of this argument see Svensson (2003). Gorter et al. (2008) provide an empirical application of using expectations in the context of the ECB’s policy rate setting.

  10. While the classical monetary policy reaction function contains the level of the output gap, we use GDP growth forecasts given that the ECB has assumed an average growth rate of 2.0–2.5% for potential output when formulating its reference value for monetary growth.

  11. Gerlach (2007) motivates the choice of the variables used in the reaction function by the ECB’s reference to those variables in the editorials of its Monthly Bulletin. Thus, even though the specification of our reaction function is rather ad hoc, the choice of variables is guided by the ECB’s statements.

  12. This approach of directly plugging variables other than inflation or economic activity in the monetary policy reaction function to test their significance has been frequently applied in the literature; see, e.g., Clarida et al. (1998), Carstensen (2006) or Gerlach (2007).

  13. The yields are taken from Eurostat.

  14. As a consequence, we also define \(\Delta i_{t-1}\) in our \({\varvec{x}}\) vector as the direction of the previous interest rate decision.

  15. For an application of generalized ordered threshold models, see, e.g., Williams (2006) or Boes and Winkelmann (2004).

  16. If we assume in the generalized framework that \(\tilde{{\varvec{\beta }}}_{1} = \tilde{{\varvec{\beta }}}_{2}\), we obtain the (restricted) ordered model.

  17. Since we estimate our model according to Williams (2006), we in fact estimate the following equations: \(\Pr (\Delta i_{t}<0)=1-F({\tilde{\alpha }}_{1}+\tilde{{\varvec{\beta }}}_{1}^{\prime } {\varvec{x}}_{t})\); \(\Pr (\Delta i_{t}=0) = F({\tilde{\alpha }}_{1}+\tilde{{\varvec{\beta }}}_{1}^{\prime } {\varvec{x}}_{t})-F({\tilde{\alpha }}_{2}+\tilde{{\varvec{\beta }}}_{2}^{\prime }{\varvec{x}}_{t})\); \(\Pr (\Delta i_{t}> 0)=F({\tilde{\alpha }}_{2}+\tilde{{\varvec{\beta }}}_{2}^{\prime }{\varvec{x}}_{t})\). This does not affect our estimates for \(\varvec{\beta }_{i}\), however, instead of obtaining an estimate for \(\hat{\alpha }_{i}\); this parameterization yields \(-\hat{\alpha }_{i}\) according to the notation of Eq. (6).

  18. An erroneous forecast at the very beginning of the sample period might therefore render the development of the predicted rate out of line with the evolution of the true MRO rate, even if all other predictions were correct.

  19. We choose this date because Gerlach (2011) suggests a regime shift in the ECB’s reaction function in June 2008. We could have of course also taken a different break point.

  20. We also include the dummy variable itself to estimate the level effect.

  21. Note that we drop the lagged level of the MRO rate from this entire exercise given that it is not possible in the simulation to model the level of the interest rate.

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Acknowledgements

The authors thank three anonymous referees as well as Falk Bräuning, Jan Willem van den End, Marco Hoeberichts and participants of the 2015 INFER Workshop, Cologne and the 2015 Macroeconomic Workshop, Strasbourg for valuable comments and suggestions. This paper is a revised version of a chapter of Christina Bräuning’s Ph.D. thesis. The views expressed in this paper do not necessarily reflect the views of De Nederlandsche Bank.

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Authors and Affiliations

  1. De Nederlandsche Bank, P.O. Box 98, 1000 AB, Amsterdam, The Netherlands

    Christina Bräuning

  2. WHU - Otto Beisheim School of Management, Burgplatz 2, 56179, Vallendar, Germany

    Ralf Fendel

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  1. Christina Bräuning
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  2. Ralf Fendel
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Correspondence to Christina Bräuning.

Appendix

Appendix

Table 3 Ordered and generalized ordered logit estimates
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Fig. 5
figure 5

Yields on 10-year sovereign bonds of selected euro area member states. The figure displays bond yields of selected euro area member states. It shows the German bond yield (green solid line), the Italian bond yield (yellow short-dashed line), the Portuguese bond yield (red dashed-dotted line), the Irish bond yield (black dotted line) and the Greek bond yield (blue long-dashed line). (Color figure online)

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Bräuning, C., Fendel, R. National information and euro area monetary policy: a generalized ordered choice approach. Empir Econ 54, 501–522 (2018). https://doi.org/10.1007/s00181-017-1238-1

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