Bootstrapping non-parametric models is a fairly complicated exercise which is associated with implicit assumptions or requirements that are not always obvious to the non-expert user. Bootstrap DEA is a significant development of the past decade; however, some of its assumptions and properties are...

We propose double bootstrap methods to test the mean-variance efficiency hypothesis when multiple portfolio groupings of the test assets are considered jointly rather than individually. A direct test of the joint null hypothesis may not be possible with standard methods when the total number of...

While permutation tests and bootstraps have very wide-ranging application, both share a common potential drawback: as data-intensive resampling methods, both can be runtime prohibitive when applied to large or even medium-sized data samples drawn from large datasets. The data explosion over the...

Financial event studies using daily stock returns are frequently employed in the analysis of mergers to estimate the sign and magnitude of stock movements to particular merger announcements. A common method of conducting the event study is least squares regression with dummy variables. Daily...

We propose an improved model selection test for dynamic models based on the method of Rivers and Vuong (2002) using a new asymptotic approximation to the sampling distribution of a new test statistic. The model selection test is applicable to dynamic models with very general selection criteria...

Extending the multiplier central limit theorem and resampling bootstrap to statistics and empirical processes of pseudo-observations, it is shown how to build asymptotically independent copies of statistics and empirical processes to perform statistical tests. Application to parametric and...