Financial Variables as Predictors of Real Output Growth
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
|Date of creation:||Mar 2007|
|Date of revision:|
|Publication status:||Published in SMU Economics and Statistics Working Paper Series|
|Contact details of provider:|| Postal: 90 Stamford Road, Singapore 178903|
Phone: 65-6828 0832
Fax: 65-6828 0833
Web page: http://www.economics.smu.edu.sg/
More information through EDIRC
|Order Information:|| Email: |
When requesting a correction, please mention this item's handle: RePEc:siu:wpaper:14-2007. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (QL THor)
If references are entirely missing, you can add them using this form.