Implementing Statistical Criteria to Select Return Forecasting Models monthly excess stock return,lagged once and twice;a monthly bond excess return,lagged once and twice;*the yield-to-maturity on a representative Treasury bond;the stock market's price level;the yield-to-maturity on a 3- month Treasury bill (also used to compute excess stock and bond returns); the stock market's dividend yield;and the stock market's price-earnings ratio.None of these predictors use information that would not have been available at the moment that future excess stock returns were predicted. Appendix C provides a list of the data sources. Table I displays descriptive statistics of the monthly excess stock returns and the predictors.The lengths of the samples(T+n)run from a minimum of 122 observations (Norway)to a maximum of 471 observations(United States).Returns are expressed in percent per month;yields in percent per year.Table I also lists the beginning of the estimation sample for each country (t =1).Our choice was based on data availability,and hence was purely incidental. To get an idea of the amount of predictability that obtains in this dataset using in-sample statistical analysis,Table 2 lists the results from a regression of monthly excess stock returns (now not expressed in percentage terms) onto (i)an intercept,(ii)a January dummy,(iii)the stock dividend yield, (iv)the short-term interest rate,and (v)the long-term bond yield.Solnik (1993)also reports these estimates,for a similar dataset,but a shorter time period.The results of regressions like the ones reported in Table 2 match those of Solnik when run over his subperiod only (1/71-8/90).There is one exception though:our bond yield has more predictive power than his. A closer look at Table 2 reveals the patterns that Solnik discussed at length:predictability is not uniform across stock markets [R2s are between 2%(Germany)and 9.8%(U.K.)];predictors vary.The well-known nega- tive correlation between excess stock returns and contemporaneous interest rates is significant(at the 1%level)only for Belgium,France,and the United States. An unfortunate choice of subperiod,however,may give one the wrong impression of the amount of predictability.We tried one possible subsample and generated impressive evidence of predictability.Table 2 reports the results from estimation of the same regression over the subperiod 1/71- 8/80(10 years shorter than Solnik's sampling period).They are listed under Period II.The R2s run from a low of 4.4%(Canada)to a high of 36.4% (Belgium)or higher!Overall,the significance of the coefficients in the regression is overwhelming. 4This bond return is actually computed from the yield on a representative Treasury bond and an estimate of the duration.Hence it cannot strictly be considered to be a bond (excess)return.Nevertheless,its computation does not require future information.Therefore it is a proper predictor. 5 Some high R2s may be the result of lack of observations.The highest R2,40%(Spain).is based on a sampling period that runs from 1/78 to 8/80 only.Belgium's high R2.however,is based on a full sample. 411