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Financial Variables as Predictors of Real Output Growth

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Author Info

  • 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

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File URL: https://mercury.smu.edu.sg/rsrchpubupload/10603/Tay(2007).pdf
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Bibliographic Info

Paper provided by Singapore Management University, School of Economics in its series Working Papers with number 14-2007.

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Length: 29 pages
Date of creation: Mar 2007
Date of revision:
Publication status: Published in SMU Economics and Statistics Working Paper Series
Handle: RePEc:siu:wpaper:14-2007

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Related research

Keywords: Forecasting; Mixed Frequencies; Functional linear regression;

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Cited by:
  1. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2010. "Should macroeconomic forecasters use daily financial data and how?," University of Cyprus Working Papers in Economics 09-2010, University of Cyprus Department of Economics.
  2. J. Isaac Miller, 2012. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Working Papers 1211, Department of Economics, University of Missouri.

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