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
Forecasting, Mixed Frequencies, Functional linear regression.
SMU Economics and Statistics Working Paper Series, Paper No. 14-2007
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Tay, Anthony S..
Financial variables as predictors of real output growth. (2007). 1-29. Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/1058
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