104,236 research outputs found
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
Donald M. MacKinnon, Themes in Theology : The Threefold Cord. Edinburgh, T. and T. Clark, 1987
No full textVahanian Gabriel. Donald M. MacKinnon, Themes in Theology : The Threefold Cord. Edinburgh, T. and T. Clark, 1987. In: Revue d'histoire et de philosophie religieuses, 71e année n°4, Octobre-décembre 1991. p. 481
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
Bootstrap Inference in a Linear Equation Estimated by Instrumental Variables
No full textWe study several tests for the coefficient of the single right-hand-side endogenous variable in a linear equation estimated by instrumental variables. We show that all the test statistics--Student's t, Anderson-Rubin, Kleibergen's K, and likelihood ratio (LR)--can be written as functions of six random quantities. This leads to a number of interesting results about the properties of the tests under weak-instrument asymptotics. We then propose several new procedures for bootstrapping the three non-exact test statistics and a conditional version of the LR test. These use more efficient estimates of the parameters of the reduced-form equation than existing procedures. When the best of these new procedures is used, K and conditional LR have excellent performance under the null, and LR also performs very well. However, power considerations suggest that the conditional LR test, bootstrapped using this new procedure when the sample size is not large, is probably the method of choice.bootstrap test, weak instruments, anderson-rubin test, conditional LR test, wald test, instrumental variables
Òran do'n t-Soitheach Catriona
No full textCaptain John MacKinnon Iain Mhìcheil 'Ic Iain Mhóir.; fragment; Song for the Boat Catherin
Bootstrap Hypothesis Testing
No full textThis paper surveys bootstrap and Monte Carlo methods for testing hypotheses in econometrics. Several different ways of computing bootstrap P values are discussed, including the double bootstrap and the fast double bootstrap. It is emphasized that there are many different procedures for generating bootstrap samples for regression models and other types of model. As an illustration, a simulation experiment examines the performance of several methods of bootstrapping the supF test for structural change with an unknown break point.bootstrap test, supF test, wild bootstrap, pairs bootstrap, moving block bootstrap, residual bootstrap, bootstrap P value
Artificial Regressions
No full textAssociated with every popular nonlinear estimation method is at least one "artificial" linear regression. We define an artificial regression in terms of three conditions that it must satisfy. Then we show how artificial regressions can be useful for numerical optimization, testing hypotheses, and computing parameter estimates. Several existing artificial regressions are discussed and are shown to satisfy the defining conditions, and a new artificial regression for regression models with heteroskedasticity of unknown form is introduced.artificial regression, LM test, specification test, Gauss-Newton regression, one-step estimation, OPG regression, double-length regression, binary response model
Bootstrap inference in a linear equation estimated by instrumental variables
No full textWe study several tests for the coefficient of the single right-hand-side endogenous variable in a linear equation estimated by instrumental variables. We show that writing all the test statistics—Student's t, Anderson-Rubin, the LM statistic of Kleibergen and Moreira (K), and likelihood ratio (LR)—as functions of six random quantities leads to a number of interesting results about the properties of the tests under weakinstrument asymptotics. We then propose several new procedures for bootstrapping the three non-exact test statistics and also a new conditional bootstrap version of the LR test. These use more efficient estimates of the parameters of the reduced-form equation than existing procedures. When the best of these new procedures is used, both the K and conditional bootstrap LR tests have excellent performance under the null. However, power considerations suggest that the latter is probably the method of choice.bootstrap, weak instruments, IV estimation
Testing the Specification of Econometric Models in Regression and Non-Regression Directions
No full textThe asymptotic power of a statistical test depends on the model being tested, the (implicit) alternative against which the test is constructed, and the process which actually generated the data. The exact way in which it does so is examined for several classes of models and tests. First, we analyze the power of tests of nonlinear regression models in regression directions. Next, we consider the power of heteroskedasticity-robust variants of these tests. Finally, we examine the power of very general tests in the context of a very general class of models.power, specification test, heteroskedasticity-robust test
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