The finite-sample performance of bootstrap procedures is studied by simulation in a linear regression model containing 2 endogenous regressors. Besides several residual-based bootstrap procedures, we also consider the GMM bootstrap. The test statistics include Wald type t-statistics based on k-estimators and the robust subset (quasi) LR statistic. In the simulations, the restricted fully efficient (RFE) bootstrap based on Fuller estimates and the LIML t-statistic performs best of the Wald type statistics. Unfortunately, the bootstrap only marginally reduces the conservativeness of the subset QLR statistic. Finally, the GMM bootstrap does not seem to improve upon the asymptotic approximation. An empirical example illustrates the use of these...
The paper investigates how the particular choice of residuals used in a bootstrap-based testing proc...
We focus on the linear instrumental variable model with two endogenous regressors under conditional ...
We focus on the linear instrumental variable model with two endogenous regressors under conditional ...
We propose a wild bootstrap procedure for linear regression models estimated by instrumental variabl...
We propose a wild bootstrap procedure for linear regression models estimated by instrumental variabl...
This paper studies robustness of bootstrap inference methods for instrumental variable (IV) regressi...
The bootstrap is a computationally intensive data analysis technique. It is particularly useful for ...
This paper studies robustness of bootstrap inference methods for instrumental variable (IV) regressi...
We propose a wild bootstrap procedure for linear regression models estimated by instrumental variabl...
This paper studies robustness of bootstrap inference methods for instrumental variable (IV) regressi...
We propose a wild bootstrap procedure for linear regression models estimated by instrumental variabl...
AbstractBootstrap is a resampling procedure drawn from an original sample data with replacement allo...
We propose a wild bootstrap procedure for linear regression models estimated by instrumental variabl...
We propose a wild bootstrap procedure for linear regression models estimated by instrumental variabl...
We propose a wild bootstrap procedure for linear regression models estimated by instrumental variabl...
The paper investigates how the particular choice of residuals used in a bootstrap-based testing proc...
We focus on the linear instrumental variable model with two endogenous regressors under conditional ...
We focus on the linear instrumental variable model with two endogenous regressors under conditional ...
We propose a wild bootstrap procedure for linear regression models estimated by instrumental variabl...
We propose a wild bootstrap procedure for linear regression models estimated by instrumental variabl...
This paper studies robustness of bootstrap inference methods for instrumental variable (IV) regressi...
The bootstrap is a computationally intensive data analysis technique. It is particularly useful for ...
This paper studies robustness of bootstrap inference methods for instrumental variable (IV) regressi...
We propose a wild bootstrap procedure for linear regression models estimated by instrumental variabl...
This paper studies robustness of bootstrap inference methods for instrumental variable (IV) regressi...
We propose a wild bootstrap procedure for linear regression models estimated by instrumental variabl...
AbstractBootstrap is a resampling procedure drawn from an original sample data with replacement allo...
We propose a wild bootstrap procedure for linear regression models estimated by instrumental variabl...
We propose a wild bootstrap procedure for linear regression models estimated by instrumental variabl...
We propose a wild bootstrap procedure for linear regression models estimated by instrumental variabl...
The paper investigates how the particular choice of residuals used in a bootstrap-based testing proc...
We focus on the linear instrumental variable model with two endogenous regressors under conditional ...
We focus on the linear instrumental variable model with two endogenous regressors under conditional ...
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