The Use of Multiple Instruments for Measurement of Earnings Forecast Errors, Firm Size Effect and the Quality of Analysts' Forecast Errors

This paper develops an instrumental variables framework to form a better proxy for earnings forecast errors. The key aspect of the approach is to extract information from alternative proxies for the same underlying variable, namely a portion of realized earnings signals unexpected by the market. We use signs of various proxies for earnings forecast errors obtained from different time‐series forecasting models as multiple instruments. The results show that the instrumental variables approach is effective for reducing measurement errors inherent in various proxies for earnings forecast errors. It produces not only a smaller magnitude but also a narrower dispersion of earnings forecast errors. The paper provides evidence that the instrumental variables approach performs better for small‐firm samples than for large‐firm samples. Finally, we observe that analysts’ forecast errors seasoned with the signs of various time‐series forecast errors (as well as the signs of their own forecast errors) outperform those without seasoning. This indicates that analysts’ forecast errors can still be improved by employing the instrumental variables technique.