IDEAS home Printed from https://ideas.repec.org/a/gam/jecnmx/v6y2018i2p23-d143630.html
   My bibliography  Save this article

Forecasting Inflation Uncertainty in the G7 Countries

Author

Listed:
  • Mawuli Segnon

    () (Department of Economics (CQE), Westfälische Wilhelms-Universität Münster, Am Stadtgraben 9, 48143 Münster, Germany)

  • Stelios Bekiros

    () (Department of Accounting and Finance, Athens University of Economics and Business, Trias 2, GR 11362 Athens, Greece
    Department of Economics, European University Institute, Via delle Fontanelle 18, I-50014 Florence, Italy)

  • Bernd Wilfling

    () (Department of Economics (CQE), Westfälische Wilhelms-Universität Münster, Am Stadtgraben 9, 48143 Münster, Germany)

Abstract

There is substantial evidence that inflation rates are characterized by long memory and nonlinearities. In this paper, we introduce a long-memory Smooth Transition AutoRegressive Fractionally Integrated Moving Average-Markov Switching Multifractal specification [ STARFIMA ( p , d , q ) - MSM ( k ) ] for modeling and forecasting inflation uncertainty. We first provide the statistical properties of the process and investigate the finite sample properties of the maximum likelihood estimators through simulation. Second, we evaluate the out-of-sample forecast performance of the model in forecasting inflation uncertainty in the G7 countries. Our empirical analysis demonstrates the superiority of the new model over the alternative STARFIMA ( p , d , q ) - GARCH -type models in forecasting inflation uncertainty.

Suggested Citation

  • Mawuli Segnon & Stelios Bekiros & Bernd Wilfling, 2018. "Forecasting Inflation Uncertainty in the G7 Countries," Econometrics, MDPI, Open Access Journal, vol. 6(2), pages 1-25, April.
  • Handle: RePEc:gam:jecnmx:v:6:y:2018:i:2:p:23-:d:143630
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2225-1146/6/2/23/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2225-1146/6/2/23/
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. N. Bloom., 2016. "Fluctuations in uncertainty," VOPROSY ECONOMIKI, N.P. Redaktsiya zhurnala "Voprosy Economiki", vol. 4.
    2. Lux, Thomas, 2008. "The Markov-Switching Multifractal Model of Asset Returns: GMM Estimation and Linear Forecasting of Volatility," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 194-210, April.
    3. Dick van Dijk & Timo Terasvirta & Philip Hans Franses, 2002. "Smooth Transition Autoregressive Models — A Survey Of Recent Developments," Econometric Reviews, Taylor & Francis Journals, vol. 21(1), pages 1-47.
    4. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    5. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    6. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
    7. Calvet, Laurent & Fisher, Adlai, 2001. "Forecasting multifractal volatility," Journal of Econometrics, Elsevier, vol. 105(1), pages 27-58, November.
    8. Liu, Ruipeng & Di Matteo, T. & Lux, Thomas, 2007. "True and apparent scaling: The proximity of the Markov-switching multifractal model to long-range dependence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 383(1), pages 35-42.
    9. Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2015. "Measuring Uncertainty," American Economic Review, American Economic Association, vol. 105(3), pages 1177-1216, March.
    10. repec:taf:emetrv:v:36:y:2017:i:10:p:1081-1110 is not listed on IDEAS
    11. Nicholas Bloom, 2009. "The Impact of Uncertainty Shocks," Econometrica, Econometric Society, vol. 77(3), pages 623-685, May.
    12. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    13. Michael P. Clements, 2014. "Forecast Uncertainty- Ex Ante and Ex Post : U.S. Inflation and Output Growth," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(2), pages 206-216, April.
    14. Jonathan H. Wright, 2011. "Term Premia and Inflation Uncertainty: Empirical Evidence from an International Panel Dataset," American Economic Review, American Economic Association, vol. 101(4), pages 1514-1534, June.
    15. S. Fountas & A. Ioannidis & M. Karanasos, 2004. "Inflation, Inflation Uncertainty and a Common European Monetary Policy," Manchester School, University of Manchester, vol. 72(2), pages 221-242, March.
    16. Refet S. Gürkaynak & Jonathan H. Wright, 2012. "Macroeconomics and the Term Structure," Journal of Economic Literature, American Economic Association, vol. 50(2), pages 331-367, June.
    17. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, Oxford University Press, vol. 131(4), pages 1593-1636.
    18. Filip Žikeš & Jozef Baruník & Nikhil Shenai, 2017. "Modeling and forecasting persistent financial durations," Econometric Reviews, Taylor & Francis Journals, vol. 36(10), pages 1081-1110, November.
    19. Menelaos Karanasos & Stefanie Schurer, 2008. "Is the Relationship between Inflation and Its Uncertainty Linear?," German Economic Review, Verein für Socialpolitik, vol. 9, pages 265-286, August.
    20. Ben S. Bernanke, 1983. "Irreversibility, Uncertainty, and Cyclical Investment," The Quarterly Journal of Economics, Oxford University Press, vol. 98(1), pages 85-106.
    21. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. " On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    22. Enrique Sentana, 1995. "Quadratic ARCH Models," Review of Economic Studies, Oxford University Press, vol. 62(4), pages 639-661.
    23. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    24. repec:bla:ecinqu:v:56:y:2018:i:1:p:637-652 is not listed on IDEAS
    25. Laurent E. Calvet, 2004. "How to Forecast Long-Run Volatility: Regime Switching and the Estimation of Multifractal Processes," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(1), pages 49-83.
    26. repec:bla:ecinqu:v:55:y:2017:i:2:p:843-877 is not listed on IDEAS
    27. Baillie, Richard T & Chung, Ching-Fan & Tieslau, Margie A, 1996. "Analysing Inflation by the Fractionally Integrated ARFIMA-GARCH Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(1), pages 23-40, Jan.-Feb..
    28. Lux, Thomas & Segnon, Mawuli & Gupta, Rangan, 2016. "Forecasting crude oil price volatility and value-at-risk: Evidence from historical and recent data," Energy Economics, Elsevier, vol. 56(C), pages 117-133.
    29. Steffen R. Henzel & Malte Rengel, 2017. "Dimensions Of Macroeconomic Uncertainty: A Common Factor Analysis," Economic Inquiry, Western Economic Association International, vol. 55(2), pages 843-877, April.
    30. Wang, Yudong & Wu, Chongfeng & Yang, Li, 2016. "Forecasting crude oil market volatility: A Markov switching multifractal volatility approach," International Journal of Forecasting, Elsevier, vol. 32(1), pages 1-9.
    31. He, Changli & Terasvirta, Timo, 1999. "Properties of moments of a family of GARCH processes," Journal of Econometrics, Elsevier, vol. 92(1), pages 173-192, September.
    32. Eric Hillebrand & Marcelo C. Medeiros, 2016. "Nonlinearity, Breaks, and Long-Range Dependence in Time-Series Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 23-41, January.
    33. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
    34. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    inflation uncertainty; smooth transition; multifractal processes; GARCH processes;

    JEL classification:

    • B23 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Econometrics; Quantitative and Mathematical Studies
    • C - Mathematical and Quantitative Methods
    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jecnmx:v:6:y:2018:i:2:p:23-:d:143630. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (XML Conversion Team). General contact details of provider: https://www.mdpi.com/ .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.