演講者簡介 : 管中閔所長為Ph.D. in Economics，University of California，San Diego (1989)。
現為本所研究員兼所長。
其主要研究領域為Econometric Theory、Time Series Analysis及Applied Finance。
演講摘要 : We extend the KVB approach of Kiefer, Vogelsang, and Bunzel (2000, Econometrica) to constructing robust M tests without estimating asymptotic covariance matrix. We demonstrate that, when model parameters have to be estimated, the normalizing matrix computed using the full-sample estimator is able to eliminate the nuisance parameters when there is no estimation effect but not otherwise. To circumvent the problem of estimation effect, we propose using recursive estimators to compute the normalizing matrix and show that the resulting M test is asymptotically pivotal. This M test is thus also robust to the presence of estimation effect. As examples, we consider robust tests for serial correlations and robust information matrix tests. The former tests extend that of Lobato (2001, JASA) and are applicable to model residuals. For testing higherorder moments, we find that the latter tests are also robust to mis-specified lower-order moments.
