@techreport{NBERw12026,
 title = "The Dog That Did Not Bark: A Defense of Return Predictability",
 author = "John H. Cochrane",
 institution = "National Bureau of Economic Research",
 type = "Working Paper",
 series = "Working Paper Series",
 number = "12026",
 year = "2006",
 month = "February",
 URL = "http://www.nber.org/papers/w12026",
 abstract = {To question the statistical significance of return predictability, we cannot specify a null that simply turns off that predictability, leaving dividend growth predictability at its essentially zero sample value. If neither returns nor dividend growth are predictable, then the dividend-price ratio is a constant. If the null turns off return predictability, it must turn on the predictability of dividend growth, and then confront the evidence against such predictability in the data. I find that the absence of dividend growth predictability gives much stronger statistical evidence against the null, with roughly 1-2% probability values, than does the presence of return predictability, which only gives about 20% probability values. I argue that tests based on long-run return and dividend growth regressions provide the cleanest and most interpretable evidence on return predictability, again delivering about 1-2% probability values against the hypothesis that returns are unpredictable. I show that Goyal and Welch's (2005) finding of poor out-of-sample R2 does not reject return forecastability. Out-of-sample R2 is poor even if all dividend yield variation comes from time-varying expected returns.},
}