The Bootstrap Small Sample Properties
نویسنده
چکیده
This report reviews several bootstrap methods with special emphasis on small sample properties. Only those bootstrap methods are covered which promise wide applicability. The small sample properties can be investigated analytically only in parametric bootstrap applications. Thus there is a strong emphasis on the latter although the bootstrap methods can be applied nonparametrically as well. The disappointing confidence coverage behavior of several, computationally less extensive, parametric bootstrap methods should raise equal or even more concerns about the corresponding nonparametric bootstrap versions. The computationally more expensive double bootstrap methods hold great hope in the parametric case and may provide enough assurance for the nonparametric case.
منابع مشابه
A Joint Variance Ratio Test based on the Wild Bootstrap
The variance ratio (VR) test has been widely used as a means of testing for the martingale hypothesis in financial time series. The conventional VR tests are asymptotic tests, which can exhibit deficient small sample properties. In this paper, a joint VR test based on the wild bootstrap is proposed. It is a small sample test which does not rely on asymptotic approximations. An extensive Monte C...
متن کاملBootstrap and fast double bootstrap tests of cointegration rank with financial time series
The likelihood ratio test of cointegration rank is the most widely used test for cointegration. Many studies have shown by simulation that the small sample distribution is not well approximated by the limiting distribution. We suggest using the bootstrap to generate small sample critical values instead of correcting the test statistics. The idea of bootstrapping the trace test of cointegration ...
متن کاملSmall Sample Bootstrap Confidence Intervals for Long-Memory Parameter
The log periodogram regression is widely used in empirical applications because of its simplicity, since only a least squares regression is required to estimate the memory parameter, d, its good asymptotic properties and its robustness to misspecification of the short term behavior of the series. However, the asymptotic distribution is a poor approximation of the (unknown) finite sample distrib...
متن کاملBootstrap Prediction Intervals for Autoregressive Models Based on Asymptotically Mean-Unbiased Parameter Estimators
The use of asymptotically mean-unbiased estimation is considered as a means of biascorrection, when bootstrap prediction interval is constructed for autoregressive (AR) models with unknown lag order. Its computational efficiency enables application of the endogenous lag order bootstrap algorithm to prediction intervals. Extensive Monte Carlo experiments are conducted using a number of stationar...
متن کاملLocal Bootstrap Approach for the Estimation of the Memory Parameter
The log periodogram regression is widely used in empirical applications because of its simplicity to estimate the memory parameter, d, its good asymptotic properties and its robustness to misspecification of the short term behavior of the series. However, the asymptotic distribution is a poor approximation of the (unknown) finite sample distribution if the sample size is small. Here the finite ...
متن کامل