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A Bayesian evaluation of alternative models of trend inflation

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  • Todd E. Clark
  • Taeyoung Doh

Abstract

With the concept of trend inflation now widely understood as to be important as a measure of the public's perception of the inflation goal of the central bank and important to the accuracy of longer-term inflation forecasts, this paper uses Bayesian methods to assess alternative models of trend inflation. Reflecting models common in reduced-form inflation modeling and forecasting, we specify a range of models of inflation, including: AR with constant trend; AR with trend equal to last period's inflation rate; local level model; AR with random walk trend; AR with trend equal to the long-run expectation from the Survey of Professional Forecasters; and AR with time-varying parameters. We consider versions of the models with constant shock variances and with stochastic volatility. We first use Bayesian metrics to compare the fits of the alternative models. We then use Bayesian methods of model averaging to account for uncertainty surrounding the model of trend inflation, to obtain an alternative estimate of trend inflation in the U.S. and to generate medium-term, model-average forecasts of inflation. Our analysis yields two broad results. First, in model fit and density forecast accuracy, models with stochastic volatility consistently dominate those with constant volatility. Second, for the specification of trend inflation, it is difficult to say that one model of trend inflation is the best. Among alternative models of the trend in core PCE inflation, the local level specification of Stock and Watson (2007) and the survey-based trend specification are about equally good. Among competing models of trend GDP inflation, several trend specifications seem to be about equally good.

Suggested Citation

  • Todd E. Clark & Taeyoung Doh, 2011. "A Bayesian evaluation of alternative models of trend inflation," Working Paper 1134, Federal Reserve Bank of Cleveland.
  • Handle: RePEc:fip:fedcwp:1134
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    References listed on IDEAS

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    1. Clark, Todd E. & McCracken, Michael W., 2015. "Nested forecast model comparisons: A new approach to testing equal accuracy," Journal of Econometrics, Elsevier, vol. 186(1), pages 160-177.
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    Cited by:

    1. Clark, Todd & McCracken, Michael, 2013. "Advances in Forecast Evaluation," Handbook of Economic Forecasting, Elsevier.
    2. Gefang, Deborah & Koop, Gary & Potter, Simon M., 2012. "The dynamics of UK and US inflation expectations," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3120-3133.
    3. Chan, Joshua C.C., 2013. "Moving average stochastic volatility models with application to inflation forecast," Journal of Econometrics, Elsevier, vol. 176(2), pages 162-172.
    4. Faust, Jon & Wright, Jonathan H., 2013. "Forecasting Inflation," Handbook of Economic Forecasting, Elsevier.
    5. Joshua C. C. Chan & Gary Koop & Simon M. Potter, 2013. "A New Model of Trend Inflation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 94-106, January.
    6. Davide Delle Monache & Ivan Petrella, 2014. "Adaptive Models and Heavy Tails," Working Papers 720, Queen Mary University of London, School of Economics and Finance.
    7. Taeyoung Doh, 2011. "Is unemployment helpful in understanding inflation?," Economic Review, Federal Reserve Bank of Kansas City, issue Q IV, pages 5-26.
    8. Henzel, Steffen R., 2013. "Fitting survey expectations and uncertainty about trend inflation," Journal of Macroeconomics, Elsevier, vol. 35(C), pages 172-185.

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    Keywords

    Bayesian statistical decision theory ; Inflation (Finance) - Mathematical models ; Forecasting;

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