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Forecasting Korean inflation

Author

Listed:
  • In Choi

    () (Department of Economics, Sogang University, Seoul)

  • Seong Jin Hwang

    ()

Abstract

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.

Suggested Citation

  • In Choi & Seong Jin Hwang, 2012. "Forecasting Korean inflation," Working Papers 1202, Research Institute for Market Economy, Sogang University.
  • Handle: RePEc:sgo:wpaper:1202
    as

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    References listed on IDEAS

    as
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    7. 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.
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    9. Dotsey, Michael & Fujita, Shigeru & Stark, Tom, 2011. "Do Phillips curves conditionally help to forecast inflation?," Working Papers 11-40, Federal Reserve Bank of Philadelphia.
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    12. 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.
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    Cited by:

    1. Ruiz Ortega, Esther & Poncela, Pilar & Corona, Francisco, 2017. "Estimating non-stationary common factors : Implications for risk sharing," DES - Working Papers. Statistics and Econometrics. WS 24585, Universidad Carlos III de Madrid. Departamento de Estadística.

    More about this item

    Keywords

    inflation forecasting; Phillips curve; term spread; factor model; principal-component estimation; generalized principal-component estimation;

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