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Asset Prices as Indicators of Euro Area Monetary Policy: An Empirical Assessment of Their Role in a Taylor Rule


This paper estimates forward-looking and forecast-based Taylor rules for France, Germany, Italy, and the euro area. Performing extensive tests for over-identifying restrictions and instrument relevance, we find that asset prices can be highly relevant as instruments in policy rules. While asset prices improve Taylor rule estimates, different assets prove most relevant across countries and this result could be seen as complicating the tasks of the European Central Bank. Encompassing tests show that forecast-based outperform forward-looking Taylor rules. A policy implication is that central banks ought to release their own forecasts and the basis upon which they are generated.

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Fig. 1


  1. 1.

    Bernanke (2005) suggests that “Central bankers naturally play close attention to interest rates and asset prices, ... [they] are potentially valuable sources of timely information about economic and financial conditions ... [and] should embody a great deal of investors’ collective information and beliefs about the future course of the economy.” Others, including Alan Greenspan, have suggested either that asset prices have only an indirect effect on interest rates or were largely ignored in the past (Norris 2005). Greithner (2006), President of the New York Fed, is somewhat more emphatic about the role of asset prices in monetary policy: “... monetary policy still has to take into account the impact of significant movements in asset values on output and inflation.”

  2. 2.

    “It is far from obvious that bubbles, even if identified early, can be preempted at a lower cost than a substantial economic contraction and possible financial destabilization—the very outcomes we would be seeking to avoid.” (Greenspan 2004). Gruen et al. (2005) show that the informational requirements needed to support an activist response to asset price bubbles are quite substantial.

  3. 3.

    We are not, of course, the first or the only authors of a study to consider asset prices as instruments. For example, see Chadha et al. (2004).

  4. 4.

    Policy rules for smaller euro area members including Austria, Belgium, Finland, and the Netherlands were also examined. As these did not materially affect the conclusions reported below, we do not discuss them any further. Also see Eleftheriou et al. (2006). The real GDP of the three core area countries considered in this paper accounts for roughly three-quarters of euro area-wide real GDP.

  5. 5.

    Since 2003, the ECB publishes the Survey of Professional Forecasters The data only go back to 1999. Note that these are not the ECB’s forecasts. When using central bank forecasts there is potentially an additional complication for the consumers of such forecasts (Goodhart 2005). Also published semi-annually are the Eurosystem staff macroeconomic projections for the euro are

  6. 6.

    While part of the debate has turned on whether the monetary authority should target asset price developments, central banks have argued against this position because they treat asset prices as forward-looking indicators of inflation and/or the output gap.

  7. 7.

    Still another alternative, not considered in this paper, is to broaden the definition of inflation. Goodhart (2001), for example, advocates a measure of inflation that goes beyond merely incorporating the effects of changing prices for goods and services to also include the impact of equity and housing prices.

  8. 8.

    In the case of equity and possibly housing prices, matters are complicated still further because there is possibly an element of “irrational exuberance” or a “bubble” component that is difficult to measure empirically. This paper does not address all of these issues.

  9. 9.

    Gerdesmeier and Roffia (2004) perform an extensive comparison of forward- versus backward-looking Taylor rules for the euro area.

  10. 10.

    In an optimizing framework (Clarida et al. 2000), these weights also reflect to some extent the underlying structure of the economy and the persistence of economic shocks.

  11. 11.

    In Eq. (2) the inflation target has been normalized to zero. Equation (1) is then derived from the relation \( i_{t} = \rho i_{{t - 1}} + {\left( {1 - \rho } \right)}i^{ * }_{t} + \upsilon _{{\text{t}}} \). Moreover, while there is no requirement that j = k, this is the general practice followed in empirical work.

  12. 12.

    Goodhart (2005); Jansson and Vredin (2003); Siklos (2002), and Siklos and Wohar (2006) also estimate policy rules relying on central bank forecasts.

  13. 13.

    Siklos et al. (2004) consider the estimation of extended Taylor rules with asset prices as a separate determinant of nominal interest rates. In this paper we do not follow this estimation strategy primarily because, according to many central bankers, asset prices are best thought of as indicators of future inflation or output rather than variables they might directly target. They also consider the implications for Taylor rules using real-time data for Germany and the euro area, as opposed to the revised data as done in the present study. Unfortunately, real-time data for France and Italy are not of the same caliber as what is available for Germany and the euro area. Hence, we do not address the relevant issues any further in what follows. See, however, Eleftheriou et al. (2006) for empirical evidence dealing with some of the relevant issues for the pre-euro era.

  14. 14.

    Fuhrer and Tootell (2004) point out that if the correlation between the contemporaneous asset price and lagged interest rate changes is significant, this will tend to bias the coefficient on the asset price variable in the estimated policy rule away from zero. Goodhart (2005) shows for UK data that using forecasts conditioned on known interest rate decisions, results in a potentially serious misspecification.

  15. 15.

    It is conceivable that nominal interest rates have a unit root. Siklos and Wohar (2006) consider the implications for estimating Taylor rules. The unit root property suggests the possibility of cointegration but no such property was found in the data, again likely because of the span of the sample. Estimates of Eq. (3b) include a constant. Variants of Eq. (3b) were also estimated with lags of various asset prices. See the discussion in the next section.

  16. 16.

    We used an HP filter with a standard smoothing parameter (1600) as well as a larger smoothing parameter (4800). In addition, we also estimated an inflation target evaluated as the mid-point of the spread between the average annual inflation rate in the euro area countries and the average annual inflation rate in the three lowest inflation rate countries in the euro area plus 1.5%, as specified in the Maastricht Treaty. The conclusions are robust to all these alternatives.

  17. 17.

    Stock and Watson (2003) recommend a one-sided HP filter. Much of the literature uses a two-sided HP filter for convenience, or an alternative measure of the economy’s capacity, but comparable time series are not available for the vast majority of euro area countries. We also generate, but do not report here, estimates of the output gap based on a Blanchard–Quah type decomposition with no impact on our conclusions.

  18. 18.

    Some authors rely on the J-test to determine the horizon used by the policy makers. As we shall see, it is difficult to reject the null of the validity of chosen instrument sets and difficult to discriminate among competing versions of the same estimated policy rule.

  19. 19.

    While the test for instrument relevance is based on TSLS it has the advantage that it can accommodate more than one endogenous variable and does not rely on GMM. Recent tests for instrument relevance are more complex when there is more than one endogenous variable. Jim Stock’s weak instruments web page updates information on this topic (

  20. 20.

    An objection that can be raised is that GMM is a non-linear estimation technique while the tests of instrument relevance used here are based on TSLS estimates. Since GMM is the estimation technique of choice we retain its use. Furthermore, relying on other tests of instrument relevance (e.g., the F-test as in Stock et al. 2002) we obtain comparable results. Gerdesmeier and Roffia (2004) report few substantive differences between their GMM and TSLS estimates for the euro area. We also consider whether the volatility of asset prices serve as superior instruments in forward-looking rules (results not shown). Our conclusions are unchanged.

  21. 21.

    Data were obtained from the Bank of Finland. However, members of the Euro Area Business Cycle Network at may also access the relevant time series.

  22. 22.

    The empirical results rely on the nominal measure. The BIS asset price index is essentially a weighted average of equity, residential and commercial property prices, where the weights are their respective shares in private sector wealth. The calibration of weights has changed over time (Borio and Lowe 2002; Borio et al. 1994).

  23. 23.

    The estimated weights reported in Goodhart and Hofmann (2000) are used. The financial conditions index can be thought of as an extension of the monetary conditions index, representing a linear combination of interest rates and exchange rates, to include housing and equity prices.

  24. 24.

    Other than for the OECD, where forecasts of the output gap are available, forecasts are for real GDP growth. Whether Eq. (3) uses GDP growth or a proxy for the output gap does not change the outcome of the forecast efficiency tests.

  25. 25.

    Sample begins in 1991 (1996 for the euro area) due to data limitations in the forecast data.


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Both authors like to thank seminar participants at the Bundesbank, the Universidade Católica de Brasilia, Queensland University of Technology, the Oesterreichische Nationalbank, the Norges Bank, the European Central Bank, and the CIRANO Workshop on Macroeconomic Forecasting, Analysis and Policy with Data Revisions, and the 2007 Money, Macro and Finance Research Group Conference where earlier versions of this paper were presented. Both authors are grateful for financial support from the Alexander von Humboldt Foundation, to Claudio Borio of the Bank for International Settlements, and the Bank of Finland for some of the data used in the paper. Helpful comments by Andy Filardo, Frank Smets, and an anonymous referee, also helped improve the paper. Results not shown in the paper are relegated to an appendix available from the first author on request.

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Correspondence to Pierre L. Siklos.

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The research for this paper was partly conducted while the authors were visiting the Deutsche Bundesbank and the Bank of Finland.

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Siklos, P.L., Bohl, M.T. Asset Prices as Indicators of Euro Area Monetary Policy: An Empirical Assessment of Their Role in a Taylor Rule. Open Econ Rev 20, 39–59 (2009).

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  • Monetary policy reaction functions
  • Asset prices
  • Instruments
  • European Central Bank

JEL Classification

  • E52
  • E58
  • C52