Full-versus limited-information estimation of a rational-expectations model. Some numerical comparisons (Q1082770)

This paper compares numerically the asymptotic distributions of parameter estimates and test statistics associated with two estimation techniques: (a) a limited-information one, which uses instrumental variables to estimate a single equation [\textit{L. P. Hansen} and \textit{K. J. Singleton}, Econometrika 50, 1269-1286 (1982; Zbl 0497.62098)], and (b) a full- information one, which uses a procedure asymptotically equivalent to maximum likelihood to simultaneously estimate multiple equations [\textit{L. P. Hansen} and \textit{T. Sargent}, Formulating and estimating dynamic linear rational expectations models. J. Econ. Dynamics Control 2, 7-46 (1980)]. The paper compares the two with respect to both (1) asymptotic efficiency under the null hypothesis of no misspecification, and (2) asymptotic bias and power in the presence of certain local alternatives. It is found that (1) full-information standard errors are only moderately smaller than limited-information standard errors, and (2) when the model is misspecified, full-information tests tend to be more powerful, and its parameter estimates tend to be more biased. This suggests that at least in the model considered here, the gains from the use of the less robust and computationally more complex full-information technique are not particularly large.