This paper tests the rationality of individual price forecasts in a panel of professional forecasters. Here, unlike in most previous studies, rationality is not rejected. The results here differ because (1) using individual forecasts avoids aggregation bias, (2) comparison of forecasts to initial data avoids bias due to data revision, (3) the professional forecasters have economic incentives to state their expectations accurately, (4) a new covariance matrix estimator consistent when forecast errors are correlated across individuals is used.

We find support for a negative relation between conditional expected monthly return and conditional variance of monthly return, using a GARCH-M model modified by allowing (1) seasonal patterns in volatility, (2) positive and negative innovations to returns having different impacts on conditional volatility, and (3) nominal interest rates to predict conditional variance. Using the modified GARCH-M model, we also show that monthly conditional volatility may not be as persistent as was thought. Positive unanticipated returns appear to result in a downward revision of the conditional volatility whereas negative unanticipated returns result in an upward revision of conditional volatility.

We find support for a negative relation between conditional expected monthly return and conditional variance of monthly return, using a GARCH‐M model modified by allowing (1) seasonal patterns in volatility, (2) positive and negative innovations to returns having different impacts on conditional volatility, and (3) nominal interest rates to predict conditional variance. Using the modified GARCH‐M model, we also show that monthly conditional volatility may not be as persistent as was thought. Positive unanticipated returns appear to result in a downward revision of the conditional volatility whereas negative unanticipated returns result in an upward revision of conditional volatility.

The authors find support for a negative relation between conditional expected monthly return and conditional variance of monthly return using a GARCH-M model modified by allowing (1) seasonal patterns in volatility, (2) positive and negative innovations to returns having different impacts on conditional volatility, and (3) nominal interest rates to predict conditional variance. Using the modified GARCH-M model, they also show that monthly conditional volatility may not be as persistent as was thought. Positive unanticipated returns appear to result in a downward revision of the conditional volatility, whereas negative unanticipated returns result in an upward revision of conditional volatility.

Carl Bonham and Richard Cohen (1995) are quite correct in noting the errors in our paper (Keane and Runkle, 1990), which were caused by our ignorance of cointegration. We stand chagrined. However, Bonham and Cohen are overstating their case when they claim that Keane and Runkle's results do not support the empirical validity of the rational-expectations hypothesis (p. 289). Bonham and Cohen focus on our tests of price-forecast rationality conditioned on past oil prices and Ml, which they claim are the core of our paper and provide our most stringent tests of rationality. Those particular tests account for only two paragraphs of our 20-page paper-obviously, these tests do not provide the core results of our paper. Rather, the main result of our paper is that individual price forecasts are unbiased and rational, conditioned on the forecaster's own past errors. No previous researchers had ever found even this limited support for the rational-expectations hypothesis. These core results are unaffected by the cointegration issues noted by Bonham and Cohen. Given that caveat, however, note how few of our results are actually overturned by Bonham and Cohen. Although our test statistics for determining whether forecasters properly condition on Ml growth are incorrect, Bonham and Cohen reach the same conclusion that we do: price forecasts conditioned on Ml growth are rational. Bonham and Cohen do reach different conclusions about forecast rationality than we do when they condition on oil price changes. But they themselves show that forecasters were only irrational in conditioning on oil prices after 1973 (their table 2, rows 5 and 6). To call such forecasting failure irrationality may or may not be correct. We think that Bonham and Cohen's results merely confirm the widespread view that forecasters did not completely understand the effects that oil price shocks would have on the economy because such large oil price shocks had never been seen before. Although Bonham and Cohen overturn only one of our original tests, they do provide additional evidence against forecast rationality with their tests that condition on interest-rate spreads and the unemployment rate. We have no doubt that a search over a large number of conditioning variables will uncover some instances in which forecast rationality is rejected. But conducting such a search will also incorrectly bias tests toward rejecting rationality. Since our original paper, we have also examined the rationality of earnings forecasts made by individual stock analysts-a group that has even more incentive than economic forecasters to make accurate predictions. Although all previous studies in that literature had found individual earnings forecasts to be irrational, we found (Keane and Runkle, 1994) that analysts' forecasts are rational. This additional research provides further support for the paper criticized by Bonham and Cohen.

This paper tests the rationality of individual price forecasts in a panel of professional forecasters. Here, unlike in most previous studies, rationality is not rejected. The results here differ because (1) using individual forecasts avoids aggregation bias, (2) comparison of forecasts to initial data avoids bias due to data revision, (3) the professional forecasters have economic incentives to state their expectations accurately, and (4) a new covariance matrix estimator consistent when forecast errors are correlated across individuals is used.

This paper develops generalized method‐of‐moments tests for the rationality of earnings per share forecasts made by individual stock analysts. We fail to reject the hypothesis of rationality as long as we take into account two complications: (1) the correlation in a given period of analysts' forecast errors in predicting earnings for firms in the same industry and (2) discretionary asset write‐downs, which affect earnings but are intentionally ignored by analysts when they make earnings forecasts. Our results challenge earlier work by De Bondt and Thaler and by Abarbanell and Bernard that found irrationality in analysts' forecasts.