122,246 research outputs found
Jonathan Mackinnon
No full textCampbell J. L. Jonathan Mackinnon. In: Etudes Celtiques, vol. 6, fascicule 1, 1952. pp. 216-217
Jonathan Mackinnon
No full textCampbell J. L. Jonathan Mackinnon. In: Etudes Celtiques, vol. 6, fascicule 1, 1952. pp. 216-217
Bootstrap Testing in Nonlinear Models
No full textWhen a model is nonlinear, bootstrap testing can be expensive because of the need to perform at least one nonlinear estimation for every bootstrap sample. We show that it may be possible to reduce computational costs by performing only a fixed, small number of artificial regressions, or Newton steps, for each bootstrap sample. The number of iterations needed is smaller for likelihood ratio tests than for other types of classical tests. The suggested procedures are applied to tests of slope coefficients in the tobit model, where asymptotic procedures often work surprisingly poorly. In contrast, bootstrap tests work remarkably well, and very few iterations are needed to compute them.bootstrapping, hypothesis testing, tobit model, one-step estimation
Improving the reliability of bootstrap tests with the fast double bootstrap
No full textTwo procedures are proposed for estimating the rejection probabilities of bootstrap tests in Monte Carlo experiments without actually computing a bootstrap test for each replication. These procedures are only about twice as expensive (per replication) as estimating rejection probabilities for asymptotic tests. Then a new procedure is proposed for computing bootstrap P values that will often be more accurate than ordinary ones. This “fast double bootstrap” is closely related to the double bootstrap, but it is far less computationally demanding. Simulation results for three different cases suggest that the fast double bootstrap can be very useful in practice.Bootstrap
Improving the Reliability of Bootstrap Tests with the Fast Double Bootstrap
No full textWe first propose two procedures for estimating the rejection probabilities of bootstrap tests in Monte Carlo experiments without actually computing a bootstrap test for each replication. These procedures are only about twice as expensive (per replication) as estimating rejection probabilities for asymptotic tests. We then propose a new procedure for computing bootstrap P values that will often be more accurate than ordinary ones. This "fast double bootstrap" is closely related to the double bootstrap, but it is far less computationally demanding. Simulation results for three different cases suggest that this procedure can be very useful in practice.bootstrap test, double bootstrap, Monte Carlo experiment, rejection frequency, fast double bootstrap, FDB
Model Specification Tests Against Non-Nested Alternatives
No full textNon-nested hypothesis tests provide a way to test the specification of an econometric model against the evidence provided by one or more non-nested alternatives. This paper surveys the recent literature on non-nested hypothesis testing in the context of regression and related models. Much of the purely statistical literature which has evolved from the fundamental work of Cox is discussed briefly or not at all. Instead, emphasis is placed on those techniques which are easy to employ in practice and are likely to be useful to applied workers.Cox test, nonnested hypotheses, J test, specification tests, nonnested hypothesis test
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