Harvey Liu Multiple Testing

Harvey, Campbell R. and Yan Liu, 2014b, Multiple testing in economics, Working Paper, Duke. University. Harvey, Campbell R. and Yan Liu, 2014c, Evaluating

For more details on the selection of the threshold t-statistic, see Harvey and Liu (2014a to multiple hypothesis testing. Assuming independence, we model

Oct 20, 2015 Third, the extended statistical framework in. Harvey and Liu [2015] needs only test statistics. return hurdles for single testing and multiple

Harvey and Liu [2014b] explicitly take the correlation among tests into account and provide multiple-testing-adjusted. Sharpe ratios using a variety of methods

May 3, 2015 The Setting. The good news: ▫Harvey and Liu (2014) suggest a multiple testing correction which provides a haircut for the. Sharpe Ratios

What is an appropriate cut-off for statistical significance? In Harvey and Liu (HL, 2015 we present three approaches to multiple testing. We answer the

systematically accounts for multiple tests and yields the appropriate haircut for any Harvey and Liu's adjustment to Sharpe ratios is non-linear. At some

For more details on the selection of the threshold statistic, see Harvey and Liu (2014a to multiple hypothesis testing. Assuming independence, we model

these multiple-testing problems. TWO VIEWS OF MULTIPLE TESTING. There are two is minimal. HAIRCUTTING SHARPE RATIOS. Harvey and Liu [2014a] provide a method

Harvey, Liu, and Zhu. (2016) present a stylized Bayesian framework for multiple hypothesis testing in which multiple-testing adjustment is achieved indirectly

Jun 29, 2025 regarding multiple hypothesis testing. We begin by reviewing the Cauchy combination test of Liu and Xie (2020 highlighting its key advantages

For more details on the selection of the threshold t-statistic, see Harvey and Liu (2014a to multiple hypothesis testing. Assuming independence, we model

Following Harvey, Liu, and Zhu (2016 the first approach orders outcomes by the date they were first reported: in other words, when we apply multiple testing

Oct 18, 2019 to rigorously perform multiple hypothesis testing in addressing the multiple testing problem: Harvey and Liu (2018) and Barras et al

These tests are close in spirit to the size and power tests Harvey and Liu (2020) better at explaining alpha after controlling for multiple testing?

For more details on the selection of the threshold t-statistic, see Harvey and Liu (2014a to multiple hypothesis testing. Assuming independence, we model

The meta-study of market anomalies by Harvey, Liu, and Zhu (2016) leads off possibly the need to re-calibrate the multiple testing framework along the lines

May 3, 2018 For this reason, Harvey, Liu, and Zhu (2016) concluded that “most claimed research findings in financial economics are likely false Yet

Dec 6, 2023 multiple testing (Romano and Wolf (2005 Harvey et al. (2020 Harvey and Liu (2021a is very focused on this notion of power, i.e the

Multiple testing (Harvey, Liu, Zhu 2016; Harvey 2017 2. Multi-model factor alphas (FF5, FF6, q-factor 3. Spanning vs. 209 published anomalies (Chen