The Review of Financial Studies/v 10n 3 1997 advances in asset pricing theory,along with mounting empirical evi- dence of predictability,have persuaded the majority of researchers to abandon the constant expected returns paradigm.Nevertheless,cer- tain aspects of the empirical research on predicting returns remain controversial.A number of studies,for example,report that stock and bond returns appear to exhibit a striking degree of predictability over long horizons.This evidence is not necessarily inconsistent with the view that markets are efficient,but it does seem to contradict much of the conventional wisdom in this regard. Almost all of the research on predicting long-horizon returns falls under the general heading of regression analysis.The basic strategy adopted in most studies is to regress overlapping returns for various holding periods on a set of predetermined instrumental variables.In the majority of cases,the authors of such studies treat the sample R2 from the regression specification as a measure of the economic sig- nificance of the predictable component of returns.Fama and French (1988a),for example,argue that dividend yields explain a large frac- tion of the total variation in long-horizon stock returns.To support this claim they show that the sample R-increases from around 3%for monthly returns to well over 25%for four-year returns.Campbell and Shiller (1988)cite similar evidence in their study of the link between dividend yields,earnings:price ratios,and stock returns.They report large values of the sample R2 for both 3-year and 10-year returns and conclude,like Fama and French (1988a),that long-horizon stock returns are indeed highly predictable. The apparent pattern of strong predictability at long horizons ex- tends to other classes of assets as well.In a follow-up to their initial study,Fama and French (1989)demonstrate that two interest rate variables-a term spread and a default-risk spread-seem to explain a substantial fraction of the long-term variation in bond returns.Again they observe a dramatic increase in the sample R2 as the return hori- zon grows from one month to four years.The sample R2 is usually less than 10%for monthly and quarterly bond returns,but often exceeds 30%for returns measured over longer horizons.Fama and French attribute this increase in explanatory power at long horizons to low- frequency oscillations in expected returns.They further contend that these low-frequency oscillations reflect the rational response of in- vestors to slowly changing business conditions. This tendency to treat the sample R as a measure of the economic significance of predictability is not surprising.After all,the approach does have a substantial degree of intuitive appeal.The important point to remember,however,is that the least-squares theory used to justify this practice rests largely on the assumption that the error term for the regression model can be treated as an independently and identically 580