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Forecasting Korean inflation

  • In Choi

    ()

    (Department of Economics, Sogang University, Seoul)

  • Seong Jin Hwang

    ()

Registered author(s):

This paper studies the performance of various forecasting models for Ko- rean inflation rates. The models studied in this paper are the AR(p) model, the dynamic predictive regression model with such exogenous variables as the un- employment rate and the term spread, the inflation target model, the random- walk model, and the dynamic predictive regression model using estimated fac- tors along with the unemployment rate and the term spread. The sampling period studied in this paper is 2000M11-2011M06. Among the studied models, the dynamic predictive regression model using estimated factors along with the unemployment rate and the term spread tends to perform best at the 6-month horizon when the factors are extracted from I(0) series and the variables for the factor extraction are selected by the criterion of the correlation of each variable with the inflation rate. The dynamic predictive regression models with the unemployment rate and the term spread also work well at shorter horizons.

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File Function: First version, 2012
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Paper provided by Research Institute for Market Economy, Sogang University in its series Working Papers with number 1202.

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Length: 29 pages
Date of creation: Feb 2012
Date of revision:
Handle: RePEc:sgo:wpaper:1202
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Web page: http://home.sogang.ac.kr/sites/sgrime
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  1. Estrella, Arturo & Mishkin, Frederic S., 1997. "The predictive power of the term structure of interest rates in Europe and the United States: Implications for the European Central Bank," European Economic Review, Elsevier, vol. 41(7), pages 1375-1401, July.
  2. Jonas D. M. Fisher & Chin Te Liu & Ruilin Zhou, 2002. "When can we forecast inflation?," Economic Perspectives, Federal Reserve Bank of Chicago, issue Q I, pages 32-44.
  3. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2003. "Do financial variables help forecasting inflation and real activity in the euro area?," Journal of Monetary Economics, Elsevier, vol. 50(6), pages 1243-1255, September.
  4. Bai, Jushan, 2004. "Estimating cross-section common stochastic trends in nonstationary panel data," Journal of Econometrics, Elsevier, vol. 122(1), pages 137-183, September.
  5. 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.
  6. Sandra Eickmeier & Christina Ziegler, 2008. "How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 237-265.
  7. Dotsey, Michael & Fujita, Shigeru & Stark, Tom, 2011. "Do Phillips curves conditionally help to forecast inflation?," Working Papers 11-40, Federal Reserve Bank of Philadelphia.
  8. Jörg Breitung & Sandra Eickmeier, 2006. "Dynamic factor models," AStA Advances in Statistical Analysis, Springer, vol. 90(1), pages 27-42, March.
  9. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
  10. Marie Diron & Benoît Mojon, 2008. "Are inflation targets good inflation forecasts?," Economic Perspectives, Federal Reserve Bank of Chicago, issue Q II, pages 33-45.
  11. Inoue, Atsushi & Kilian, Lutz, 2008. "How Useful Is Bagging in Forecasting Economic Time Series? A Case Study of U.S. Consumer Price Inflation," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 511-522, June.
  12. In Choi, 2007. "Efficient Estimation of Factor Models," Working Papers 0701, Research Institute for Market Economy, Sogang University, revised Dec 2010.
  13. Sharon Kozicki, 1997. "Predicting real growth and inflation with the yield spread," Economic Review, Federal Reserve Bank of Kansas City, issue Q IV, pages 39-57.
  14. Connor, Gregory & Korajczyk, Robert A., 1986. "Performance measurement with the arbitrage pricing theory : A new framework for analysis," Journal of Financial Economics, Elsevier, vol. 15(3), pages 373-394, March.
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