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Forecasting inflation in an inflation-targeting regime: A role for informative steady-state priors

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  • Beechey, Meredith
  • Österholm, Pär

Abstract

Inflation targeting as a monetary-policy regime is widely associated with an explicit numerical target for the rate of inflation. This paper investigates whether the forecasting performance of Bayesian autoregressive models can be improved by incorporating information about the target. We compare a mean-adjusted specification, which allows an informative prior on the distribution for the steady state of the process, to traditional methodology. We find that the out-of-sample forecasts of the mean-adjusted autoregressive model outperform those of the traditional specification, often by non-trivial amounts, for five early adopters of inflation targeting. It is also noted that as the sample lengthens, the posterior distribution of steady-state inflation narrows more for countries with explicit point targets.

Suggested Citation

  • Beechey, Meredith & Österholm, Pär, 2010. "Forecasting inflation in an inflation-targeting regime: A role for informative steady-state priors," International Journal of Forecasting, Elsevier, vol. 26(2), pages 248-264, April.
  • Handle: RePEc:eee:intfor:v:26:y::i:2:p:248-264
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Stockhammar, Pär & Österholm, Pär, 2016. "Do Inflation Expectations Granger Cause Inflation?," Working Papers 2016:4, Örebro University, School of Business.
    2. Karlsson, Sune, 2012. "Forecasting with Bayesian Vector Autoregressions," Working Papers 2012:12, Örebro University, School of Business.
    3. Mossfeldt, Marcus & Stockhammar, Pär, 2016. "Forecasting Goods and Services Inflation in Sweden," Working Papers 146, National Institute of Economic Research.
    4. Österholm, Pär, 2012. "The limited usefulness of macroeconomic Bayesian VARs when estimating the probability of a US recession," Journal of Macroeconomics, Elsevier, pages 76-86.
    5. Karlsson, Sune, 2013. "Forecasting with Bayesian Vector Autoregression," Handbook of Economic Forecasting, Elsevier.
    6. Thomas Jonsson & Pär Österholm, 2012. "The properties of survey-based inflation expectations in Sweden," Empirical Economics, Springer, pages 79-94.
    7. Andrés González & Kirstin Hubrich & Timo Teräsvirta, 2009. "Forecasting inflation with gradual regime shifts and exogenous information," CREATES Research Papers 2009-03, Department of Economics and Business Economics, Aarhus University.
    8. Stockhammar, Pär & Österholm, Pär, 2016. "Do Inflation Expectations Granger Cause Inflation?," Working Papers 145, National Institute of Economic Research.
    9. Gustafsson, Peter & Stockhammar, Pär & Österholm, Pär, 2016. "Macroeconomic effects of a decline in housing prices in Sweden," Journal of Policy Modeling, Elsevier, pages 242-255.
    10. Pär Stockhammar & Pär Österholm, 2017. "The Impact of US Uncertainty Shocks on Small Open Economies," Open Economies Review, Springer, pages 347-368.
    11. Thomas Jonsson & Pär Österholm, 2012. "The properties of survey-based inflation expectations in Sweden," Empirical Economics, Springer, pages 79-94.
    12. Pär Stockhammar & Pär Österholm, 2016. "Effects of US policy uncertainty on Swedish GDP growth," Empirical Economics, Springer, pages 443-462.
    13. Jan-Erik Antipin & Farid Jimmy Boumediene & Pär Österholm, 2014. "Forecasting Inflation Using Constant Gain Least Squares," Australian Economic Papers, Wiley Blackwell, pages 2-15.
    14. Gustafsson, Peter & Stockhammar, Pär & Österholm, Pär, 2016. "Macroeconomic effects of a decline in housing prices in Sweden," Journal of Policy Modeling, Elsevier, pages 242-255.
    15. Raoufina, Karine, 2016. "Forecasting Employment Growth in Sweden Using a Bayesian VAR Model," Working Papers 144, National Institute of Economic Research.
    16. Pär Stockhammar & Pär Österholm, 2016. "Effects of US policy uncertainty on Swedish GDP growth," Empirical Economics, Springer, pages 443-462.

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