This paper weakens the size and moment conditions needed for typical block bootstrap methods (i.e. the moving blocks, circular blocks, and stationary bootstraps) to be valid for the sample mean of Near-Epoch-Dependent functions of mixing processes; they are consistent under the weakest conditions that ensure the original process obeys a Central Limit Theorem (those of de Jong, 1997, Econometric Theory). In doing so, this paper extends de Jong\u27s method of proof, a blocking argument, to hold with random and unequal block lengths. This paper also proves that bootstrapped partial sums satisfy a Functional CLT under the same conditions
Block bootstrap has been introduced in the literature for resampling dependent data, i.e. stationary...
This article introduces a resampling procedure called the stationary bootstrap as a means of calcula...
AbstractIt is shown that the blockwise bootstrap of the empirical process for a stationary β-mixing ...
In this thesis we establish that the blockwise bootstrap works for a large class of statistics. The ...
In this thesis we establish that the blockwise bootstrap works for a large class of statistics. The ...
In this thesis we establish that the blockwise bootstrap works for a large class of statistics. The ...
AbstractWe propose a circular block resampling procedure to modify Künsch's moving block bootstrap. ...
AbstractIt is shown that the blockwise bootstrap of the empirical process for a stationary β-mixing ...
The efficient bootstrap methodology is developed for overidentified moment conditions models with we...
The efficient bootstrap methodology is developed for overidentified moment conditions models with we...
Consistency and optimality of block bootstrap schemes for distribution and variance estimation of sm...
AbstractWe propose a circular block resampling procedure to modify Künsch's moving block bootstrap. ...
AbstractWe apply the blockwise bootstrap for stationary observations, proposed by Künsch (1989), to ...
The efficient bootstrap methodology is developed for overidentified moment conditions models with we...
Grahn, (1995) introduced the Conditional Least Squares estimators for the class (I) of bilinear mod...
Block bootstrap has been introduced in the literature for resampling dependent data, i.e. stationary...
This article introduces a resampling procedure called the stationary bootstrap as a means of calcula...
AbstractIt is shown that the blockwise bootstrap of the empirical process for a stationary β-mixing ...
In this thesis we establish that the blockwise bootstrap works for a large class of statistics. The ...
In this thesis we establish that the blockwise bootstrap works for a large class of statistics. The ...
In this thesis we establish that the blockwise bootstrap works for a large class of statistics. The ...
AbstractWe propose a circular block resampling procedure to modify Künsch's moving block bootstrap. ...
AbstractIt is shown that the blockwise bootstrap of the empirical process for a stationary β-mixing ...
The efficient bootstrap methodology is developed for overidentified moment conditions models with we...
The efficient bootstrap methodology is developed for overidentified moment conditions models with we...
Consistency and optimality of block bootstrap schemes for distribution and variance estimation of sm...
AbstractWe propose a circular block resampling procedure to modify Künsch's moving block bootstrap. ...
AbstractWe apply the blockwise bootstrap for stationary observations, proposed by Künsch (1989), to ...
The efficient bootstrap methodology is developed for overidentified moment conditions models with we...
Grahn, (1995) introduced the Conditional Least Squares estimators for the class (I) of bilinear mod...
Block bootstrap has been introduced in the literature for resampling dependent data, i.e. stationary...
This article introduces a resampling procedure called the stationary bootstrap as a means of calcula...
AbstractIt is shown that the blockwise bootstrap of the empirical process for a stationary β-mixing ...