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 find that our mixed frequency models perform well in forecasting real output growth.
Forecasting, Mixed Data Sampling, Functional linear regression, Test for Superior Predictive Ability
Mixing Frequencies: Stock Returns as a Predictor of Real Output Growth. (2006). Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/949
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