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Notes

  1. 1.

    Note that the case for having intermediate variables in monetary policy rests on the argument of the existence of time lags per se, not because there is uncertainty about the actually length and variability of time lags.

  2. 2.

    Note that individual disturbances cannot be observed by the policy maker.

    If \(m_t\) can be observed, \(i_t\) can be adjusted.

  3. 3.

    The monetarism paradigm has later on also spread to Germany. Founded by the late Karl Brunner in 1970, the Konstanz Seminar celebrated its thirtieth anniversary in 1999. Brunner started the Seminar with two objectives, to close the gap in the quality of research and teaching of economics between the United States and Europe, Germany and Switzerland in particular, and to provide an alternative to the dominant Keynesian paradigm to European monetary policy makers. At its beginnings, the Konstanz Seminar was at the fringe of the economics profession; today it is part of the mainstream. Fratianni and von Hagen (2001) review the academic and policy accomplishments of the Konstanz Seminar.

  4. 4.

    The calculus-minded reader knows that d (ln X) d X = 1/ X or d (ln X)= dX / X, that is for infinitesimal small changes a change in ln X is equal to the relative or proportional change in X.

  5. 5.

    For a detailed survey of German policy of monetary targeting at the times of the Bundesbank see, for instance, Neumann and von Hagen (1993). See also von Hagen (1999).

  6. 6.

    “Extracting the stable long-run relationship (…) from shorter term developments in M3 is, in essence, the filtering of signals from noise. This analysis is demanding, since it requires both a strong command of economic theory and a detailed knowledge of the institutional environment”, Issing (2001, p. 6).

  7. 7.

    The declining trend of income velocity can be explained by the omitted wealth effect. As market agents do not only demand real balances for financing actual output but also already existing wealth, nominal money growth might exceed nominal output growth. See Gerdesmeier (1996) for more details.

  8. 8.

    See, European Central Bank (2004), p. 45. To our knowledge, the ECB introduced the real money gap in its June 2001 Bulletin, p. 8. For the results of the latest strategy revision see ECB (2003).

  9. 9.

    See Hallman, Porter, and Small (1991) for more details. At roughly the same time, for price level stability purposes, Irving Fisher (1935) proposed an automatic variation in the stock of money to correct deviations from target.

  10. 10.

    For an extension of the model for small and open economies (especially in view of fixed exchange rates) see Clemens and Tatom (1994).

  11. 11.

    For calculating the price gap, one may approximate v ** and y * applying the Hodrick-Prescott-Filter (HP-Filter), which has become a standard procedure in many applied econometric work (Orr, Edey & Kennedy, 1995; Martins & Scarpetta, 1999), to v and y.

  12. 12.

    Here a = ln(1+ A) and r = ln(1 + R), where A and R represent the growth rates in percentage points divided by 100.

  13. 13.

    This result, however, crucially hinges on the question whether the long-run relationship is found to be weakly exogenous with respect to real income and interest rates.

  14. 14.

    In view of extraordinary portfolio shifts, the ECB constructed a monetary aggregate called “M3 corrected” (ECB, 2004, p. 43.). It basically represents the monthly M3 series excluding the net purchases of non-monetary securities by the money-holding sector from MFIs and nonresidents.

  15. 15.

    For the following, see Masuch, Pill, and Willeke (2001).

  16. 16.

    See Heikensten and Vredin (2002, p. 8). For a detailed discussion of how IT was put into practise in Sweden, see Svensson (1999, 2003). More generally on IT, see also Baltensperger (2000), Bernanke (2003), Bernanke, Laubach, Mishkin, & Posen (1999), Neumann and von Hagen (2002, p. 127ff) and Kuttner (2004).

  17. 17.

    For instance, the Riksbank’s inflation forecasts for inflation two-years ahead come reasonably close to their stated targets, at least since 1997. This observation, along with the well-known instability problems associated with constant-interest-rate rules, led Vredin (2003) to question whether these central banks’ forecasts represent true constant-interest-rate forecasts.

  18. 18.

    Of course, if there was no uncertainty about the transmission mechanism and parameter constancy, there would be no need for an intermediate variable approach. For a discussion of intermediate targets see Neumann (1974). The debate about the best choice of an intermediate target has been revived in the 1990s following the adoption of IT in a number of countries; see Leidermann and Svensson (1995).

  19. 19.

    It should be noted at this juncture that, for instance, Haldane (1995) notes that the Bank of England’s inflation forecast satisfies criteria (a–c).

  20. 20.

    Svensson (2003, p. 450) argues that forecast targeting “does not imply that forecasts must be exclusively model-based”, which, of course, suggests a rather pronounced degree of discretion in formulating the inflation forecast. The same view is expressed by, for instance, the Sachverständigenrat (2003), p. 26.

  21. 21.

    On the determinants of central bank credibility see ECB Observer (2001).

  22. 22.

    For an exposition of the circularity problem induced by monetary policy which mechanically responds to private inflation forecasts see Woodford (1994) and Bernanke and Woodford (1997). See also Carlstrom and Fuerst (2001).

  23. 23.

    See Gerlach and Schnabel (1999, p. 1).

  24. 24.

    The monetary base is the sum of currency held by the non-bank public and bank reserves. As the latter is recorded on the liability side of its balance sheet, the central bank can monitor it on a daily basis and make adjustments as needed to keep it at any desired level on average over (say) a week.

  25. 25.

    In contrast to our illustration, the Federal Reserve Bank of St. Louis uses a modified money base, including an estimate of the effect of sweep programs: To adjust the monetary base for the effect of retail-deposit sweep programs, the bank adds to the monetary base an amount equal to 10 percent of the total amount swept, as estimated by the Federal Reserve Board staff. These estimates are imprecise, at best. Sweep program data are found at research.stlouisfed.org/aggreg/swdata.html.

  26. 26.

    In this case, however, we have to take into account that a change in base money supply might not be accompanied by a predictable change in a more broadly defined monetary aggregate – as the money multiplier might not be stable.

  27. 27.

    For instance, the Bank of Canada used the MCI as a short-term operational reference variable; however, the bank has discontinued the calculation of the MCI as from 31 December 2006 and has not used it as an input into its monetary policy decisions for some time. See in this context Freedman (1994, 1995, 1996). For the use of the MCI in New Zealand see Reserve Bank of New Zealand (1996).

  28. 28.

    See Carstensen and Colavecchio (2004). For the estimation of monetary policy reaction functions in general see, e.g., Huang and Lin (2006), Florio (2005) and Altavilla and Landolfo (2005). For an application to regime shifts in reaction functions see, for instance, Valente (2003).

  29. 29.

    See, e.g. Clausen and Hayo (2005), Faust et al. (2001), and Smant (2002) for the first approach and e.g. Clausen and Hayo (2005) and Gerlach-Kristen (2003) for the latter. For a good survey see Sauer and Sturm (2003).

  30. 30.

    See, for instance, Clarida et al. (1998), Clausen and Hayo (2005), Faust et al. (2001), Peersman and Smets (1998) and Smant (2002).

  31. 31.

    See, e.g., Clausen and Hayo (2005), Gerlach-Kristen (2003), Gerlach and Schnabel (2000), Peersman and Smets (1998) and Ullrich (2003).

  32. 32.

    A further example is Surico (2003a) who comes up with the following estimates: β1=0.77 and β2=0.47 for the sample 1997:07-2002:10.

  33. 33.

    However, in the simulations part of this paper, we complementarily use monthly data (Belke & Gros, 2005). Since our measure of the output gap based on industrial production is much more volatile than Taylor’s (1993) original GDP-based output gap, the results might be biased and we mainly focus on the results based on GDP series and quarterly data, as is also sometimes preferred in the literature (see, e.g. the survey by Ullrich, 2003).

  34. 34.

    We used a wide spectrum of unit root tests, among others, e.g., the ADF-test, the Elliott-Rothenberg-Stock DF-GLS test and the Kwiatkowski-Phillips-Schmidt-Shin test. The results are available on request.

  35. 35.

    Despite the increasing share of services in the overall economy, it is still commonly assumed that the industrial sector is the ‘cycle maker’ and that it leads significant parts of the economy. See Sauer and Sturm (2003).

  36. 36.

    For a detailed analysis of the effects of the stock market downswing and the accompanying financial uncertainty on EMU money demand and on measures of excess liquidity derived from money demand, see Carstensen (2004) and Greiber and Lemke (2005).

  37. 37.

    Giannone, Reichlin, and Sala (2002, p. 11), deliver a third competing argument. They argue that the reaction function used here is not conditioned on shocks like demand or technology shocks but on the variables themselves. The use of a reaction function not conditioned on shocks might result in a coefficient smaller than unity depending on the ratio of inflation variance caused by demand to inflation variance caused by technology. A low value of this ratio causes a small coefficient. For a similar argument see also Ullrich (2003, p. 10).

  38. 38.

    Taylor (1999a) arrives at values of β1 = 0.25 and β2 = 0.81 with ex-post data for the US for that period, while Orphanides (2001) estimates a forward-looking rule with real-time data and reports β1= 0.57 and β2 =1.64.

  39. 39.

    Inoue and Kilian (2002) show that in-sample tests of predictability are at least as credible as the results of out-of-sample tests. Hence, there is no reason to emphasize only one type of forecasts a priori.

  40. 40.

    See, however, Österholm (2005) who conjectures that the Taylor rule appears to be a questionable tool for evaluation of the Federal Reserve during the investigated samples.

  41. 41.

    See Belke and Gros (2003). For a more formalized treatment of monetary policy effectiveness under uncertainty based on the real option approach see Belke and Goecke (2003).

  42. 42.

    Wyplosz (2001, p. 1), reports clear anecdotic evidence of the demand by the markets, the media and even by Finance Ministers to cut rates during this period.

  43. 43.

    The ECB has been criticised for making reactive, rather than proactive monetary policy decisions in the wake of September 11th. Perhaps, the ECB and other central banks would have waited longer before assessing the outcome of the terrorist strikes, and formulating and timing policy responses without the lead of the Fed. However, the decision by the ECB to follow the Fed’s lead represents a shift to a more pre-emptive policy stance. In cutting interest rates without a clear indication of the likely economic impact of events in the US and the impact on oil prices, the ECB took a risk that cheaper money in Euroland may induce greater price pressures over the medium term that could threaten its 2% inflation target. However, according to our option value-argument (Gros & Belke, 2003), the ECB was correct in its assessment of the risks to EU growth, and the decision to cut its interest rate has contributed to enhancing the ECB’s credibility as a global monetary authority.

  44. 44.

    In the spring of 1999, the ECB somewhat belatedly reacted to fears of deflation triggered by the LTCM crisis as another exceptional event in our sample.

  45. 45.

    Begg et al. (2002, p. 42) and Breuss (2002, p. 13) see a time lag between Fed and ECB interest rate decisions. They attribute the reason for the Fed’s moving first to the US cycle leading the euro zone’s.

  46. 46.

    See BBC-News (2001).

  47. 47.

    We used first differences, i.e. changes in interest rates, when the level series seemed to contain a unit root.

  48. 48.

    Daily data refer to working days only. Weekends and holidays were eliminated.

  49. 49.

    The ECB also provides some funds at longer maturities (three months).

  50. 50.

    See Ulrich 2003, p. 7, and Wyplosz 2001, p. 6 f. Perez-Quiros and Sicilia (2002), p. 7 ff., argue that policy announcements made on Council meetings should not trigger any reaction of asset prices in case of full predictability, since market participants have already correctly anticipated these policy decisions on the day when the central bank rate is changed. However, in a world of uncertainty, collective decisions on monetary policy and lack of transparency, effective communication and active guidance to the markets, predictability and the anticipation of the exact timing of interest rate changes might not be fully attainable. This justifies the use of the refi and the targeted funds rate in our case.

  51. 51.

    For instance, the Governing council of the ECB decided to lower the minimum bid rate on the main refinancing operations by 0.25 percentage points to 2.50%, starting from the operation to be settled on March 12th, 2003. In this case, we assigned the change already to March 6th, 2003.

  52. 52.

    The level series does not fluctuate around a constant mean and its variance is not constant and finite.

  53. 53.

    Overall, our new results confirm those contained in the studies by Belke and Gros (2002a) and Gros et al. (2000) which were based on an investigation of monthly realisations of the 3-month LIBOR interest rates and a smaller sample: In no case does one have to reject the null hypothesis that the US interest rate does not ‘Granger cause’ the euro interest rate and at the same time not reject the null hypothesis that the euro interest rate does not Granger cause the US rate.

  54. 54.

    Belke and Gros (2006) address two additional questions within the Johansen-framework of a cointegrating VAR analysis (Johansen 1991, 1995). Is there a stochastic co-movement between the US and the euro interest rate? And: If there is a co-movement, is it driven by the US interest rate? Hence, we checked for cointegration between the US and the euro interest rates and for weak exogeneity of the US interest rate in the cointegrating VAR. In the preceding chapter, we argued that due to the non-stationary character of the time series, we had to conduct our GC analysis in first differences for the 1-month LIBOR rates. This clearly needs not be the case if the US and the euro zone time series are cointegrated. In this case, we can additionally use level information in order to identify the nature of the relationship of the two series in the data-generating process. The results are available on request.

  55. 55.

    For a general discussion of interest rate decisions in an uncertain environment see Begg et al. (2002, p. 31ff).

  56. 56.

    The tests for the remaining variables display a similar pattern and are available on request.

  57. 57.

    Begg et al. (2002) report in their MECB 4 update that since September 2001, the economic environment has become dramatically more uncertain. In this respect, they refer to, e.g. the sharp drop in stock markets, the large swings in HICP inflation largely unconnected with food or energy prices. See also Belke and Goecke (2008).

  58. 58.

    The ECB has, understandably, not taken any position on this issue. But a recent ECB Working Document (Ehrmann & Fratzscher, 2002) analyses one part of this issue, namely the extent to which the US influences the euro area. The conclusions are interesting: “There are in particular some US macroeconomic announcements to which European markets react significantly, especially in times of increased uncertainty, like the initial period of EMU or the 2001 recession” and, “throughout a learning process, the importance given to US news has declined”. As will become clearer below our own results are consistent with the first quote, but go in the opposite direction of the second quote.

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Authors and Affiliations

  1. Chair for Macroeconomics, University Duisburg-Essen Faculty of Economics and Business Administration, Universitätsstr. 12, 45117 Essen, Germany

    Prof.Dr Ansgar Belke

  2. Frankfurt School of Finance & Management, Sonnemannstr. 9-11, 60314 Frankfurt am Main, Germany

    Honorary Prof. Dr. Thorsten Polleit

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  1. Prof.Dr Ansgar Belke
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  2. Honorary Prof. Dr. Thorsten Polleit
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Correspondence to Ansgar Belke .

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© 2009 Springer-Verlag Berlin Heidelberg

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Belke, A., Polleit, T. (2009). Monetary Policy Strategies. In: Monetary Economics in Globalised Financial Markets. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71003-5_8

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  • DOI: https://doi.org/10.1007/978-3-540-71003-5_8

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