We develop inference procedures robust to general forms of weak dependence. The procedures use test statistics constructed by resampling data in a manner that does not depend on the unknown correlation structure of the data. We prove that the statistics are asymptotically normal under the weak...

This paper presents the first analysis of the life course outcomes through late midlife (around age 55) for the participants of the iconic Perry Preschool Project, an experimental high-quality preschool program for disadvantaged African-American children in the 1960s. We discuss the design of...

This article introduces and investigates the properties of a new bootstrap method for time-series data, the kernel block bootstrap. The bootstrap method, although akin to, offers an improvement over the tapered block bootstrap of Paparoditis and Politis (2001), admitting kernels with unbounded...

The bootstrap is a method for estimating the distribution of an estimator or test statistic by resampling one's data or a model estimated from the data. Under conditions that hold in a wide variety of econometric applications, the bootstrap provides approximations to distributions of statistics,...

This paper considers the specification and performance of jackknife estimators of the autoregressive coefficient in a model with a near-unit root. The limit distributions of sub-sample estimators that are used in the construction of the jackknife estimator are derived, and the joint moment...

The bootstrap is a method for estimating the distribution of an estimator or test statistic by resampling one's data or a model estimated from the data. Under conditions that hold in a wide variety of econometric applications, the bootstrap provides approximations to distributions of statistics,...

This article introduces and investigates the properties of a new bootstrap method for time-series data, the kernel block bootstrap. The bootstrap method, although akin to, offers an improvement over the tapered block bootstrap of Paparoditis and Politis (2001), admitting kernels with unbounded...

There has been a recent interest in reporting p-values adjusted for resampling-based stepdown multiple testing procedures proposed in Romano and Wolf (2005a,b). The original papers only describe how to carry out multiple testing at a fixed significance level. Computing adjusted p-values instead...

There has been a recent interest in reporting p-values adjusted for resampling-based stepdown multiple testing procedures proposed in Romano and Wolf (2005a,b). The original papers only describe how to carry out multiple testing at a fixed significance level. Computing adjusted p-values instead...

In stratified sampling there are designs with one unit selected per stratum or when one is willing to make estimations on unplanned domains with one unit in some strata. In these cases the variance is generally estimated by the collapsed strata method, which requires identification of the strata...

We introduce a new Stata program xtspj as a bias correction tool for nonlinear models with fixed effects. The correction removes the first-order bias term using the split-panel jackknife estimation technique introduced by Dhaene and Jochmans (2015). Both the jackknife of the parameter and the...

In stratified sampling there are designs with one unit selected per stratum or when one is willing to make estimations on unplanned domains with one unit in some strata. In these cases the variance is generally estimated by the collapsed strata method, which requires identification of the strata...

The jackknife is a resampling method that uses subsets of the original database by leaving out one observation at a time from the sample. The paper develops fast algorithms for jackknifing inequality indices with only a few passes through the data. The number of passes is independent of the...

The when-to-trade decision is a critical yet neglected component of modern asset management. Typical rebalancing rules are based on suboptimal heuristics. Rebalancing is necessarily a statistical similarity test between current and proposed optimal portfolios. Available tests ignore many real...

The jackknife is a resampling method that uses subsets of the original database by leaving out one observation at a time from the sample. The paper develops fast algorithms for jackknifing inequality indices with only a few passes through the data. The number of passes is independent of the...

The jackknife is a resampling method that uses subsets of the original database by leaving out one observation at a time from the sample. The paper develops fast algorithms for jackknifing inequality indices with only a few passes through the data. The number of passes is independent of the...

The jackknife is a resampling method that uses subsets of the original database by leaving out one observation at a time from the sample. The paper develops fast algorithms for jackknifing inequality indices with only a few passes through the data. The number of passes is independent of the...

Resampled Efficiency provides the solution to using uncertain information in portfolio optimization. The proper purpose of investment advice is to improve a client's portfolio in terms of maximizing return for an appropriate level of risk. Asset management techniques for optimizing the...

The jackknife is a resampling method that uses subsets of the original database by leaving out one observation at a time from the sample. The paper outlines a procedure to obtain jackknife estimates for several inequality indices with only a few passes through the data. The number of passes is...

The jackknife is a resampling method that uses subsets of the original database by leaving out one observation at a time from the sample. The paper outlines a procedure to obtain jackknife estimates for several inequality indices with only a few passes through the data. The number of passes is...

The jackknife is a resampling method that uses subsets of the original database by leaving out one observation at a time from the sample. The paper outlines a procedure to obtain jackknife estimates for several inequality indices with only a few passes through the data. The number of passes is...

This paper presents two computational techniques and shows that these techniques can improve tests for market efficiency based on profit of trading rules. The two techniques focus on interval estimates for expected profit per trade, in contrast to the standard approach that emphasizes point...

The jackknife is a resampling method that uses subsets of the original database by leaving out one observation at a time from the sample. The paper outlines a procedure to obtain jackknife estimates for several inequality indices with only a few passes through the data. The number of passes is...