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on Forecasting |
| By: | Todd E. Clark; Michael W. McCracken |
| Abstract: | This paper develops bootstrap methods for testing whether, in a finite sample, competing out-of-sample forecasts from nested models are equally accurate. Most prior work on forecast tests for nested models has focused on a null hypothesis of equal accuracy in population basically, whether coefficients on the extra variables in the larger, nesting model are zero. We instead use an asymptotic approximation that treats the coefficients as non-zero but small, such that, in a finite sample, forecasts from the small model are expected to be as accurate as forecasts from the large model. Under that approximation, we derive the limiting distributions of pairwise tests of equal mean square error, and develop bootstrap methods for estimating critical values. Monte Carlo experiments show that our proposed procedures have good size and power properties for the null of equal finite-sample forecast accuracy. We illustrate the use of the procedures with applications to forecasting stock returns and inflation. |
| Date: | 2009 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedkrw:rwp09-11&r=for |
| By: | Todd E. Clark; Michael W. McCracken |
| Abstract: | This paper presents analytical, Monte Carlo, and empirical evidence linking in-sample tests of predictive content and out-of-sample forecast accuracy. Our approach focuses on the negative effect that finite-sample estimation error has on forecast accuracy despite the presence of significant population-level predictive content. Specifically, we derive simple-to-use in-sample tests that test not only whether a particular variable has predictive content but also whether this content is estimated precisely enough to improve forecast accuracy. Our tests are asymptotically non-central chi-square or non-central normal. We provide a convenient bootstrap method for computing the relevant critical values. In the Monte Carlo and empirical analysis, we compare the effectiveness of our testing procedure with more common testing procedures. |
| Date: | 2009 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedkrw:rwp09-10&r=for |
| By: | Gloede, Oliver; Menkhoff, Lukas |
| Abstract: | This paper examines financial professionals' overconfidence in their forecasting performance. We are the first to compare individual financial professionals' self-ratings with their true forecasting performance. Data spans several years at monthly frequency. The forecasters in our sample do not provide feasible self-ratings compared to their true performance but show overconfidence on average. In analyzing this, we find an easing relation to experience. Job characteristics are also related to less overconfidence, such as being a fund manager and using fundamental analysis. The same effect is found for the attitude to herd, whereas recent forecasting success comes along with more overconfidence. |
| Keywords: | overconfidence, self-rating, forecasting, foreign exchange, better-than-average, experience, performance |
| JEL: | G1 D84 F31 |
| Date: | 2009–08 |
| URL: | http://d.repec.org/n?u=RePEc:han:dpaper:dp-428&r=for |