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Financial Variables as Predictors of Real Output Growth

Author

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  • Anthony Tay

    () (School of Economics, Singapore Management University)

Abstract

We investigate two methods for using daily stock returns to forecast, and update forecasts of, quarterly real output growth. Both methods aggregate daily returns in some manner to form a single stock market variable. We consider (i) augmenting the quarterly AR(1) model for real output growth with daily returns using a nonparametric Mixed Data Sampling (MIDAS) setting, and (ii) augmenting the quarterly AR(1) model with the most recent r -day returns as an additional predictor. We discover that adding low frequency stock returns (up to annual returns, depending on forecast horizon) to a quarterly AR(1) model improves forecasts of output growth

Suggested Citation

  • Anthony Tay, 2007. "Financial Variables as Predictors of Real Output Growth," Working Papers 14-2007, Singapore Management University, School of Economics.
  • Handle: RePEc:siu:wpaper:14-2007
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    File URL: https://mercury.smu.edu.sg/rsrchpubupload/10603/Tay(2007).pdf
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    Cited by:

    1. repec:eee:ecosta:v:5:y:2018:i:c:p:45-66 is not listed on IDEAS
    2. J. Isaac Miller, 2014. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 12(3), pages 584-614.
    3. 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.
    4. LUPU, Radu & CALIN, Adrian Cantemir, 2014. "A Mixed Frequency Analysis Of Connections Between Macroeconomic Variables And Stock Markets In Central And Eastern Europe," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 18(2), pages 69-79.

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