Showing 1 - 10 of 185
Inference using large datasets is not nearly as straightforward as conventional econometric theory suggests when the disturbances are clustered, even with very small intra-cluster correlations. The information contained in such a dataset grows much more slowly with the sample size than it would...
Persistent link: https://www.econbiz.de/10011528432
The paper proposes a new algorithm for finding the confidence set of a collection of forecasts or prediction models. Existing numerical implementations for finding the confidence set use an elimination approach where one starts with the full collection of models and successively eliminates the...
Persistent link: https://www.econbiz.de/10011342917
In this paper, we propose a simple dependent wild bootstrap procedure for us to establish valid inferences for a wide class of panel data models including those with interactive fixed effects. The proposed method allows for the error components having weak correlation over both dimensions, and...
Persistent link: https://www.econbiz.de/10013290159
Although the concept of the sampling distribution is at the core of much of what we do in econometrics, it is a concept that is often difficult for students to grasp. The thought process behind bootstrapping provides a way for students to conceptualize the sampling distribution in a way that is...
Persistent link: https://www.econbiz.de/10014155875
The bootstrap is a convenient tool for calculating standard errors of the parameter estimates of complicated econometric models. Unfortunately, the fact that these models are complicated often makes the bootstrap extremely slow or even practically infeasible. This paper proposes an alternative...
Persistent link: https://www.econbiz.de/10014137255
The bootstrap is a popular and useful tool for estimating the asymptotic variance of complicated estimators. Ironically, the fact that the estimators are complicated can make the standard bootstrap computationally burdensome because it requires repeated re-calculation of the estimator. In...
Persistent link: https://www.econbiz.de/10012948675
The bootstrap is a convenient tool for calculating standard errors of the parameters of complicated econometric models. Unfortunately, the fact that these models are complicated often makes the bootstrap extremely slow or even practically infeasible. This paper proposes an alternative to the...
Persistent link: https://www.econbiz.de/10012948676
This paper introduces a new bootstrap approach to the construction of confidence regions for Average Treatment Effect (ATE) identified sets. Minimum Hausdorff distance bootstrap confidence regions are developed and shown to be valid under suitable regularity. A novel measure of the discrepancy...
Persistent link: https://www.econbiz.de/10014347518
Bootstrapping methods have so far been rarely used to evaluate spatial data sets. Based on an extensive Monte Carlo study we find that also for spatial, cross-sectional data, the wild bootstrap test proposed by Davidson and Flachaire (2008) based on restricted residuals clearly outperforms...
Persistent link: https://www.econbiz.de/10010239512
The bootstrap is a convenient tool for calculating standard errors of the parameters of complicated econometric models. Unfortunately, the fact that these models are complicated often makes the bootstrap extremely slow or even practically infeasible. This paper proposes an alternative to the...
Persistent link: https://www.econbiz.de/10010490878