The introduction of the bootstrap methods by Efron (1979) enables many empirical researches, which would otherwise be difficult if not totally impossible. Nowadays, bootstrapping has become an important aspect in research. This paper reviews various ways of bootstrapping data for cross-sectional and time series samples. Various ways of bootstrapping confidence intervals for estimators, an important application of bootstrap methods, are also discussed in this paper. Several other applications of bootstrap methods are briefly mentioned preceding to the concluding remarks this pape
The bootstrap is a powerful non-parametric statistical technique for making probability-based infere...
International audienceThe bootstrap is a technique for performing statistical inference. The underly...
While often simple to implement in practice, application of the bootstrap in econometric modeling o...
The introduction of the bootstrap methods by Efron (1979) enables many empirical researches, which w...
The bootstrap is a statistical technique used more and more widely in econometrics. While it is capa...
This paper provides a brief survey of the bootstrap and its use in econometrics. As an introduction,...
Although it is common to refer to “the bootstrap, ” there are actually a great many different bootst...
The bootstrap is a method for estimating the distribution of an estimator or test statistic by resam...
Aim of this thesis is to introduce the reader to the basic bootstrap techniques used in econometrics...
The bootstrap is a method for estimating the distribution of an estimator or test statistic by resam...
The Bootstrap is the most widely used resampling statistical method. This method becomes very popula...
The bootstrap, extensively studied during the last decade, has become a powerful tool in different a...
1. Researchers often want to place a confidence interval around estimated parameter values calculate...
Bootstrap methods involve estimating a model many times using simulated data. Then quantities comput...
Sedert die ontstaan van die skoenlusmetodologie, het dit beide 'n kragtige versameling oplossings ge...
The bootstrap is a powerful non-parametric statistical technique for making probability-based infere...
International audienceThe bootstrap is a technique for performing statistical inference. The underly...
While often simple to implement in practice, application of the bootstrap in econometric modeling o...
The introduction of the bootstrap methods by Efron (1979) enables many empirical researches, which w...
The bootstrap is a statistical technique used more and more widely in econometrics. While it is capa...
This paper provides a brief survey of the bootstrap and its use in econometrics. As an introduction,...
Although it is common to refer to “the bootstrap, ” there are actually a great many different bootst...
The bootstrap is a method for estimating the distribution of an estimator or test statistic by resam...
Aim of this thesis is to introduce the reader to the basic bootstrap techniques used in econometrics...
The bootstrap is a method for estimating the distribution of an estimator or test statistic by resam...
The Bootstrap is the most widely used resampling statistical method. This method becomes very popula...
The bootstrap, extensively studied during the last decade, has become a powerful tool in different a...
1. Researchers often want to place a confidence interval around estimated parameter values calculate...
Bootstrap methods involve estimating a model many times using simulated data. Then quantities comput...
Sedert die ontstaan van die skoenlusmetodologie, het dit beide 'n kragtige versameling oplossings ge...
The bootstrap is a powerful non-parametric statistical technique for making probability-based infere...
International audienceThe bootstrap is a technique for performing statistical inference. The underly...
While often simple to implement in practice, application of the bootstrap in econometric modeling o...