IDEAS home Printed from https://ideas.repec.org/a/kea/keappr/ker-20230101-39-1-09.html
   My bibliography  Save this article

Predictive Abilities of Inflation Expectations and Implications on Monetary Policy in Korea

Author

Listed:
  • Dongchul Cho

    (KDI School of Public Policy and Management)

  • Wankeun Oh

    (Hankuk University of Foreign Studies)

Abstract

This paper examines the predictive abilities of various inflation expectation indicators for inflation in Korea. We conducted real-time out-of-sample forecasting experiments utilizing three inflation expectation indicators – the general public’s expectation, professional forecasters’ expectation, and break-even inflation (BEI). The results can be summarized as follows: (i) BEI is at least as useful as the other expectation indicators in forecasting inflation; (ii) regression-based models using industrial production, oil price, and exchange rate do not help out-of-sample inflation forecasting in general; (iii) the policy interest rate, in contrast, can significantly reduce the forecasting errors; and (iv) a one percent-point increase in the policy interest rate is estimated to suppress inflation for the subsequent 12 months by around one percent-point. These results suggest that monetary policy is effective for controlling inflation and a simple model using the policy interest rate and an inflation expectation indicator may be preferred for inflation forecasting.

Suggested Citation

  • Dongchul Cho & Wankeun Oh, 2023. "Predictive Abilities of Inflation Expectations and Implications on Monetary Policy in Korea," Korean Economic Review, Korean Economic Association, vol. 39, pages 257-276.
  • Handle: RePEc:kea:keappr:ker-20230101-39-1-09
    as

    Download full text from publisher

    File URL: http://keapaper.kea.ne.kr/RePEc/kea/keappr/KER-20230101-39-1-09.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Duncan, Roberto & Martínez-García, Enrique, 2019. "New perspectives on forecasting inflation in emerging market economies: An empirical assessment," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1008-1031.
    2. Ang, Andrew & Bekaert, Geert & Wei, Min, 2007. "Do macro variables, asset markets, or surveys forecast inflation better?," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1163-1212, May.
    3. Andrew Atkeson & Lee E. Ohanian, 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 25(Win), pages 2-11.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Carlos Medel, 2017. "Forecasting Chilean inflation with the hybrid new keynesian Phillips curve: globalisation, combination, and accuracy," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 20(3), pages 004-050, December.
    2. Gary Koop & Dimitris Korobilis, 2012. "Forecasting Inflation Using Dynamic Model Averaging," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(3), pages 867-886, August.
    3. Salisu, Afees A. & Ademuyiwa, Idris & Isah, Kazeem O., 2018. "Revisiting the forecasting accuracy of Phillips curve: The role of oil price," Energy Economics, Elsevier, vol. 70(C), pages 334-356.
    4. Kevin Lansing, 2009. "Time Varying U.S. Inflation Dynamics and the New Keynesian Phillips Curve," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 12(2), pages 304-326, April.
    5. Vijay VICTOR & Maria FEKETE FARKAS & Florence JEESON, 2018. "Inflation unemployment dynamics in Hungary – A structured cointegration and vector error correction model approach," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(2(615), S), pages 195-204, Summer.
    6. Lillian Kamal, 2014. "Do GAP Models Still have a Role to Play in Forecasting Inflation?," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 8(3), pages 1-12.
    7. Szafranek, Karol, 2017. "Flattening of the New Keynesian Phillips curve: Evidence for an emerging, small open economy," Economic Modelling, Elsevier, vol. 63(C), pages 334-348.
    8. Matei Demetrescu & Christoph Hanck & Robinson Kruse‐Becher, 2022. "Robust inference under time‐varying volatility: A real‐time evaluation of professional forecasters," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 1010-1030, August.
    9. Tule, Moses K. & Salisu, Afees A. & Chiemeke, Charles C., 2019. "Can agricultural commodity prices predict Nigeria's inflation?," Journal of Commodity Markets, Elsevier, vol. 16(C).
    10. Ivan Kitov, 2007. "Inflation, Unemployment, Labor Force Change in European Counties," Mechonomics mechonomics7, Socionet.
    11. Szafranek, Karol, 2019. "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1042-1059.
    12. Tumala, Mohammed M & Olubusoye, Olusanya E & Yaaba, Baba N & Yaya, OlaOluwa S & Akanbi, Olawale B, 2017. "Forecasting Nigerian Inflation using Model Averaging methods: Modelling Frameworks to Central Banks," MPRA Paper 88754, University Library of Munich, Germany, revised Feb 2018.
    13. Pablo M. Pincheira & Carlos A. Medel, 2016. "Forecasting with a Random Walk," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(6), pages 539-564, December.
    14. Faust, Jon & Wright, Jonathan H., 2009. "Comparing Greenbook and Reduced Form Forecasts Using a Large Realtime Dataset," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 468-479.
    15. Unn Lindholm & Marcus Mossfeldt & Pär Stockhammar, 2020. "Forecasting inflation in Sweden," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 37(1), pages 39-68, April.
    16. Hossein Hassani & Abdol S. Soofi & Anatoly Zhigljavsky, 2013. "Predicting inflation dynamics with singular spectrum analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(3), pages 743-760, June.
    17. Berge, Travis J., 2018. "Understanding survey-based inflation expectations," International Journal of Forecasting, Elsevier, vol. 34(4), pages 788-801.
    18. Jan Prüser, 2021. "Forecasting US inflation using Markov dimension switching," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 481-499, April.
    19. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2013. "Should Macroeconomic Forecasters Use Daily Financial Data and How?," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 240-251, April.
    20. Marlene Amstad & Simon M. Potter & Robert W. Rich, 2014. "The FRBNY staff underlying inflation gauge: UIG," Staff Reports 672, Federal Reserve Bank of New York.

    More about this item

    Keywords

    Inflation; Forecasting; BEI; Monetary Policy; Korea;
    All these keywords.

    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
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

    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:kea:keappr:ker-20230101-39-1-09. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: KEA (email available below). General contact details of provider: https://edirc.repec.org/data/keaaaea.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.