Financial Variables as Predictors of Real Output Growth

Anthony S Tay (anthonytay@smu.edu.sg)

No 14-2007, Working Papers from Singapore Management University, School of Economics

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

Keywords: Forecasting; Mixed Frequencies; Functional linear regression (search for similar items in EconPapers)
Pages: 29 pages
Date: 2007-03
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Citations: View citations in EconPapers (8)

Published in SMU Economics and Statistics Working Paper Series

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