Journal Article10.2139/SSRN.2326121
The Iterated Bootstrap
Russell Davidson,Mirza Trokic +1 more
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TL;DR: In a series of simulation experiments, it is shown that the fast triple bootstrap improves on both the standard and fast double bootstraps, in the sense that it suers from less size distortion under the null with no accompanying loss of power.
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Abstract: The standard forms of bootstrap iteration are very computationally demanding. As a result, there have been several attempts to alleviate the computational burden by use of approximations. In this paper, we extend the fast double bootstrap of Davidson and MacKinnon (2007) to higher orders of iteration, and provide algorithms for their implementation. The new methods make computational demands that increase only linearly with the level of iteration, unlike standard procedures, whose demands increase exponentially. In a series of simulation experiments, we show that the fast triple bootstrap improves on both the standard and fast double bootstraps, in the sense that it suers from less size distortion under the null with no accompanying loss of power.
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Citations
Reference distributions and inequality measurement
TL;DR: In this paper, the authors investigate a general problem of comparing pairs of distributions which includes approaches to inequality measurement, the evaluation of "unfair" income inequality, evaluation of inequality relative to norm incomes, and goodness of fit.
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Reference distributions and inequality measurement
TL;DR: In this paper, the authors investigate a general problem of comparing pairs of distributions which includes approaches to inequality measurement, the evaluation of "unfair" income inequality, evaluation of inequality relative to norm incomes, and goodness of fit, and show how to represent the generic problem simply using a class of divergence measures derived from a parsimonious set of axioms and alternative types of "reference distributions."
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Bootstrap-based testing inference in beta regressions
TL;DR: In this article, the authors considered the problem of performing testing inference in small samples in the class of beta regression models and proposed two alternative resampling-based tests, one of which uses the bootstrap test statistic replicates to numerically estimate a Bartlett correction factor that can be applied to the likelihood ratio test statistic.
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References
Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation
TL;DR: In this article, a new class of stochastic processes called autoregressive conditional heteroscedastic (ARCH) processes are introduced, which are mean zero, serially uncorrelated processes with nonconstant variances conditional on the past, but constant unconditional variances.
23.7K
Generalized autoregressive conditional heteroskedasticity
Tim Bollerslev,Tim Bollerslev +1 more
TL;DR: In this paper, a natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in 1982 to allow for past conditional variances in the current conditional variance equation is proposed.
23.2K
Bootstrap Methods: Another Look at the Jackknife
TL;DR: In this article, the authors discuss the problem of estimating the sampling distribution of a pre-specified random variable R(X, F) on the basis of the observed data x.
Testing against general autoregressive and moving average error models when the regressors include lagged dependent variables
TL;DR: In this article, the Lagrange multiplier approach is adopted and it is shown that the test against the nth order autoregressive and moving average error models is exactly the same as the test in the case of the serial correlation model.
1.5K
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