For conditional heteroskedasticity models, the authors study the identification condition that is required for consistency of a non-Gaussian quasi-maximum-likelihood estimator. They show that, if the conditional mean is zero or if a symmetry condition is satisfied, then the identification condition is satisfied. Without symmetry, an additional parameter, for the location of the innovation density, must be added for identification. For the conditional variance parameters of a GARCH process, there is no efficiency loss from adding the parameter under symmetry, when the parameter is not needed.

We develop a method for measuring the foresight agents have. We first dichotomize an agent’s information at current date t into knowledge up to date t 1 f and expectations after t 1 f. We then form a residual-based test statistic that allows us to compare prediction errors for econometric models based on different values of f. We illustrate the method, examining investment around tax reforms to measure the foresight firms have about tax policy. In this illustration, current investment appears to reflect currently available information but little foresight other than foresight of enacted policy changes.

We develop a method for measuring the foresight agents have. We first dichotomize an agent's information at current date t into knowledge up to date t 1 f and expectations after t 1 f. We then form a residual-based test statistic that allows us to compare prediction errors for econometric models based on different values of f. We illustrate the method, examining investment around tax reforms to measure the foresight firms have about tax policy. In this illustration, current investment appears to reflect currently available information but little foresight other than foresight of enacted policy changes.