Financial Variables as Predictors of Real Output Growth

Anthony S. Tay
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Anthony S. Tay: SMU

Development Economics Working Papers from East Asian Bureau of Economic Research

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)
JEL-codes: C83 G17 (search for similar items in EconPapers)
Date: 2007-01
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Citations: View citations in EconPapers (6)

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