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The output gap and inflation in U.S. data: an empirical note

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  • Anindya Biswas

    (Spring Hill College)

Abstract

This paper analyzes the relationship between the output gap and inflation. This study uses a newly proposed flexible data-driven measure of the output gap and finds that such a distance weight-based measure of the ex-ante output gap (WAgap), has a significant and better in-sample relation with inflation in U.S from January, 1948 to August, 2013 compared to a prevalent ex-ante trend-based measure of the output gap. However, this study confirms the literature's conclusion that finding the out-of-sample/real-time predictability for inflation is most challenging, and the WAgap model provides only modest improvement over the benchmark historical mean model.

Suggested Citation

  • Anindya Biswas, 2015. "The output gap and inflation in U.S. data: an empirical note," Economics Bulletin, AccessEcon, vol. 35(2), pages 841-845.
    • Handle: RePEc:ebl:ecbull:eb-14-00475
    as

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    References listed on IDEAS

    as
    1. Orphanides, Athanasios & van Norden, Simon, 2005. "The Reliability of Inflation Forecasts Based on Output Gap Estimates in Real Time," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 583-601, June.
    2. Anderson, Evan W. & Ghysels, Eric & Juergens, Jennifer L., 2009. "The impact of risk and uncertainty on expected returns," Journal of Financial Economics, Elsevier, vol. 94(2), pages 233-263, November.
    3. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    4. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    5. Ilan Cooper, 2009. "Time-Varying Risk Premiums and the Output Gap," Review of Financial Studies, Society for Financial Studies, vol. 22(7), pages 2601-2633, July.
    6. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    7. Ghysels, Eric & Wright, Jonathan H., 2009. "Forecasting Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 504-516.
    8. Jay Choi, Jongmoo & Hauser, Shmuel & Kopecky, Kenneth J., 1999. "Does the stock market predict real activity? Time series evidence from the G-7 countries," Journal of Banking & Finance, Elsevier, vol. 23(12), pages 1771-1792, December.
    9. Biswas, Anindya, 2014. "The output gap and expected security returns," Review of Financial Economics, Elsevier, vol. 23(3), pages 131-140.
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    More about this item

    Keywords

    Inflation predictability; Output gap; Real-time data; Beta-weighting scheme;
    All these keywords.

    JEL classification:

    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit

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