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How important is the financial sector to price indices in an inflation targeting regime? An empirical analysis of the UK and the US

Abstract

This paper investigates whether there are benefits in terms of higher economic stability from incorporating stock prices into the price index targeted by the central banks. It also looks into the question of whether central banks should use stock prices as a component of the output stability index and how the index can be constructed. An optimization technique is employed to estimate weights for the various sectoral prices. The obtained weights, which depend on sectoral parameters, differ from those used in the construction of the consumer price index, CPI. Using data from the UK and the US, our analysis demonstrates that in comparison to the CPI, our measure of inflation leads to a higher output stability. Thus, in an inflation-targeting monetary policy environment, it is important to adopt a broader inflation benchmark than the CPI for the general macroeconomic stability.

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Fig. 1
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Notes

  1. 1.

    In Svensson (2002) for example, the central bank minimizes a quadratic loss function in inflation and output gap.

  2. 2.

    This view is supported by a number of authors such as Alchian and Klein (1973), Goodhart and Hofmann (2000), Goodhart (2001), Matalík et al. (2005) and Schwartz (2003).

  3. 3.

    An asset price bubble is that part of asset price movement that remains unexplained.

  4. 4.

    See Romer (2012) for details.

  5. 5.

    See Mankiw and Reis (2003) for the full discussion of the theoretical details.

  6. 6.

    Theoretical justification for the existence of sluggish and flexible prices within a system stems from the New Keynesian literature.

  7. 7.

    This assumption is used to obtain a straightforward theoretical solution. However, the empirical analysis uses a more relaxed assumption, and therefore estimates the target weights for sector prices with both correlated and uncorrelated shocks.

  8. 8.

    The deviation from the sector prices represents the unexpected shocks as a function of the sector parameters and idiosyncratic shocks.

  9. 9.

    This condition is to simplify the presentation of otherwise a long mathematical equation. However, in the empirical work, the paper uses both correlated and uncorrelated terms, which have been reported in Tables 2, 3 and 4 as well as in the “Appendix”.

  10. 10.

    See Mankiw and Reis (2003) for the full discussion.

  11. 11.

    http://stats.oecd.org.

  12. 12.

    https://stats.ukdataservice.ac.uk/index.aspx?r=668730&DataSetCode=IFS#.

  13. 13.

    The UK Share Price used is the FTSE All-Share Index, which is a market capitalization weighted index representing the performance of all eligible companies listed on the London Stock Exchange's main market.

  14. 14.

    See Bernanke and Gertler (1999, 2001) for details.

  15. 15.

    Andersen (1996) separate stock prices into fundamental and speculative components and found that only the former has an impact on investment.

  16. 16.

    Refer to Hodrick and Prescott (1997) for full discussion of the technique that estimates an unobservable time trend (growth) component of given time series variable.

  17. 17.

    These target weights depend on the sector parameters.

  18. 18.

    See Charemza and Husssain Shah (2013) for detailed discussion of the methodology.

  19. 19.

    We used 1 lag for both countries to estimate the VAR as suggested by the Akaike information criteria.

  20. 20.

    We use relative sector prices not individual price level. This addresses the issue of non-stationarity.

  21. 21.

    Results for the unit root test are not reported, but available on request.

  22. 22.

    Empirical estimations have been done using optimization module OPTMUM of GAUSS, version 13.

  23. 23.

    See Table 3.

  24. 24.

    The strong assumption yields results that are fundamentally similar to the ones obtained with the assumption of \(\Psi _{m} = 0.5\) for stock prices. This corroborates the robustness of the obtained results.

  25. 25.

    Although both countries implement IT regimes, they do differ in some economic policies and relative importance of the stock market to their GDP. For example, the level of their central banks’ independence varies. The Fed has both target and instrument independence while the Bank of England has only instrument independence as the target is defined by the government. In addition, some sectors also differ as Dubois et al. (2014) document, in that some of the countries’ commodity demand structures varies, in particular, their food demand structure. This seems credible as it is obvious that their sector prices respond differently to the output gap changes.

  26. 26.

    In contrast, if asset prices were decreasing faster than the other prices then the central bank could have reacted by cutting down the interest rate.

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Acknowledgments

We are indebted to the anonymous reviewer and the editor, C. F. Lee for the constructive and helpful comments. We are grateful to Wojciech Charemza and Carlos Diaz Vela for assistance in programming. We also acknowledge the helpful comments received from Ricardo Reis, John Hudson and Bruce Morley.

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Correspondence to Imran Hussain Shah.

Appendix

Appendix

See Tables 5 and 6.

Table 5 Correlation matrix of shock for UK
Table 6 Correlation matrix of shock for US

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Shah, I.H., Ahmad, A.H. How important is the financial sector to price indices in an inflation targeting regime? An empirical analysis of the UK and the US. Rev Quant Finan Acc 48, 1063–1082 (2017). https://doi.org/10.1007/s11156-016-0578-9

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Keywords

  • Stock prices
  • Output stability index
  • Inflation targeting
  • Fundamental
  • Bubbles

JEL Classification

  • D53
  • E31
  • E58
  • G12