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The Gaussian pseudo-maximum likelihood (PML) estimators advocated by Bollerslev and Wooldridge ( 1992 ) among many others remain root- T consistent for the conditional variance
The Gaussian pseudo-maximum likelihood (PML) estimators advocated by Bollerslev and Wooldridge ( 1992 ) among many others remain root- T consistent for the conditional variance
Would this be enough to guarantee that the stan- dard parametric bootstrap method is valid for both the testing and the interval estimation for those parameters, avoiding
Our procedure results in tests statistics with asymptotic distribution depending only on the number of common trends under the null hypothesis of rank r, and on the interval of
Let me raise three questions. The first two are related. 1) How powerful are the tests in detecting the possibility that the forecaster is using a single internally consistent,
One way to study the finite sample performance of the new forecast rationality tests is to use the bootstrap reality check of White ( 2000 ) or the refinement proposed by Hansen (
The reality check results provided in Table 7 show that, tak- ing the search for a best model into account, most of the tests compared against our proposed bootstrap critical
It is important to establish whether the performance of the tests is related to some features of the data, such as different sampling frequencies, different levels of
