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A New Model Of Trend Inflation

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  • Chan, Joshua
  • Koop, Gary
  • Potter, Simon

Abstract

This paper introduces a new model of trend (or underlying) inflation. In contrast to many earlier approaches, which allow for trend inflation to evolve according to a random walk, ours is a bounded model which ensures that trend inflation is constrained to lie in an interval. The bounds of this interval can either be fixed or estimated from the data. Our model also allows for a time-varying degree of persistence in the transitory component of inflation. The bounds placed on trend inflation mean that standard econometric methods for estimating linear Gaussian state space models cannot be used and we develop a posterior simulation algorithm for estimating the bounded trend inflation model. In an empirical exercise with CPI inflation we find the model to work well, yielding more sensible measures of trend inflation and forecasting better than popular alternatives such as the unobserved components stochastic volatility model.

Suggested Citation

  • Chan, Joshua & Koop, Gary & Potter, Simon, 2012. "A New Model Of Trend Inflation," SIRE Discussion Papers 2012-12, Scottish Institute for Research in Economics (SIRE).
  • Handle: RePEc:edn:sirdps:315
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    File URL: http://hdl.handle.net/10943/315
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    References listed on IDEAS

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    1. Peter N. Ireland, 2007. "Changes in the Federal Reserve's Inflation Target: Causes and Consequences," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(8), pages 1851-1882, December.
    2. Todd E. Clark & Taeyoung Doh, 2011. "A Bayesian evaluation of alternative models of trend inflation," Working Paper 1134, Federal Reserve Bank of Cleveland.
    3. Koop, Gary & Potter, Simon M., 2011. "Time varying VARs with inequality restrictions," Journal of Economic Dynamics and Control, Elsevier, vol. 35(7), pages 1126-1138, July.
    4. Chan, Joshua & Strachan, Rodney, 2012. "Estimation in Non-Linear Non-Gaussian State Space Models with Precision-Based Methods," MPRA Paper 39360, University Library of Munich, Germany.
    5. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    6. Gary Koop & Simon M. Potter, 2007. "Estimation and Forecasting in Models with Multiple Breaks," Review of Economic Studies, Oxford University Press, vol. 74(3), pages 763-789.
    7. Timothy Cogley & Giorgio E. Primiceri & Thomas J. Sargent, 2010. "Inflation-Gap Persistence in the US," American Economic Journal: Macroeconomics, American Economic Association, vol. 2(1), pages 43-69, January.
    8. Todd E. Clark & Troy A. Davig, 2008. "An empirical assessment of the relationships among inflation and short- and long-term expectations," Research Working Paper RWP 08-05, Federal Reserve Bank of Kansas City.
    9. John C. Williams, 2009. "The risk of deflation," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, issue mar27.
    10. James H. Stock & Mark W. Watson, 2007. "Erratum to "Why Has U.S. Inflation Become Harder to Forecast?"," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
    11. John Geweke, 2010. "Complete and Incomplete Econometric Models," Economics Books, Princeton University Press, edition 1, number 9218, June.
    12. Frank Smets & Raf Wouters, 2003. "An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area," Journal of the European Economic Association, MIT Press, vol. 1(5), pages 1123-1175, September.
    13. Timothy Cogley & Argia M. Sbordone, 2008. "Trend Inflation, Indexation, and Inflation Persistence in the New Keynesian Phillips Curve," American Economic Review, American Economic Association, vol. 98(5), pages 2101-2126, December.
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    Citations

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    Cited by:

    1. Claudiu T. Albulescu & Aviral Kumar Twari & Stephen M. Miller & Rangan Gupta, 2015. "Time-Frequency Relationship between Inflation and Inflation Uncertainty for the U.S.: Evidence from Historical Data," Working Papers 201591, University of Pretoria, Department of Economics.
    2. Delle Monache, Davide & Petrella, Ivan, 2017. "Adaptive models and heavy tails with an application to inflation forecasting," International Journal of Forecasting, Elsevier, pages 482-501.
    3. Davide Delle Monache & Ivan Petrella, 2014. "Adaptive Models and Heavy Tails," Working Papers 720, Queen Mary University of London, School of Economics and Finance.
    4. Cogley, Timothy & Sargent, Thomas J. & Surico, Paolo, 2015. "Price-level uncertainty and instability in the United Kingdom," Journal of Economic Dynamics and Control, Elsevier, vol. 52(C), pages 1-16.
    5. Eric Eisenstat & Rodney W. Strachan, 2016. "Modelling Inflation Volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(5), pages 805-820, August.
    6. Benjamin Wong, 2015. "Do Inflation Expectations Propagate the Inflationary Impact of Real Oil Price Shocks?: Evidence from the Michigan Survey," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(8), pages 1673-1689, December.
    7. Chan, Joshua C.C. & Grant, Angelia L., 2016. "Fast computation of the deviance information criterion for latent variable models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 847-859.
    8. Dany-Knedlik, Geraldine & Holtemöller, Oliver, 2017. "Inflation dynamics during the financial crisis in Europe: Cross-sectional identification of long-run inflation expectations," IWH Discussion Papers 10/2017, Halle Institute for Economic Research (IWH).
    9. Joshua C. C. Chan, 2017. "The Stochastic Volatility in Mean Model With Time-Varying Parameters: An Application to Inflation Modeling," Journal of Business & Economic Statistics, Taylor & Francis Journals, pages 17-28.
    10. Sbrana, Giacomo & Silvestrini, Andrea & Venditti, Fabrizio, 2017. "Short-term inflation forecasting: The M.E.T.A. approach," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1065-1081.
    11. Markku Lanne & Jani Luoto, 2016. "Noncausal Bayesian Vector Autoregression," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1392-1406, November.
    12. Joshua C.C. Chan & Angelia L. Grant, 2014. "Issues in Comparing Stochastic Volatility Models Using the Deviance Information Criterion," CAMA Working Papers 2014-51, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    13. 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.
    14. Chan, Joshua C C & Clark, Todd E. & Koop, Gary, 2015. "A New Model of Inflation, Trend Inflation, and Long-Run Inflation Expectations," Working Paper 1520, Federal Reserve Bank of Cleveland.
    15. Andreza A Palma, 2016. "Natural interest rate in Brazil: further evidence frThe main objective of this study is to estimate the natural interest rate for Brazil using a parsimonious AR-trend-bound model proposed by Chan, Koo," Economics Bulletin, AccessEcon, pages 1306-1314.
    16. Clark, Todd E. & Doh, Taeyoung, 2014. "Evaluating alternative models of trend inflation," International Journal of Forecasting, Elsevier, vol. 30(3), pages 426-448.
    17. Markku Lanne & Jani Luoto & Henri Nyberg, 2014. "Is the Quantity Theory of Money Useful in Forecasting U.S. Inflation?," CREATES Research Papers 2014-26, Department of Economics and Business Economics, Aarhus University.
    18. Günes Kamber & Benjamin Wong, 2016. "Testing an Interpretation of Core Inflation Measures in New Zealand," Reserve Bank of New Zealand Analytical Notes series AN2016/06, Reserve Bank of New Zealand.
    19. Chew Lian Chua & Sarantis Tsiaplias, 2014. "A Bayesian Approach to Modelling Bivariate Time-Varying Cointegration and Cointegrating Rank," Melbourne Institute Working Paper Series wp2014n27, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    20. Selien De Schryder & Gert Peersman & Joris Wauters, 2014. "Wage Indexation and the Monetary Policy Regime," CESifo Working Paper Series 5107, CESifo Group Munich.
    21. Timothy Cogley & Thomas J. Sargent, 2014. "Measuring Price-Level Uncertainty and Instability in the U.S., 1850-2012," Working Papers 2014-33, Economic Research Institute, Bank of Korea.
    22. Delle Monache, Davide & Petrella, Ivan & Venditti, Fabrizio, 2016. "Adaptive state space models with applications to the business cycle and financial stress," CEPR Discussion Papers 11599, C.E.P.R. Discussion Papers.

    More about this item

    Keywords

    Constrained inflation; non-linear state space model; underlying inflation; inflation targeting; inflation forecasting; Bayesian;

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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