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Robust resampling methods for time series
Camponovo, Lorenzo; Scaillet, Olivier; Trojani, Fabio - 2010 - This version: May 9, 2010
We study the robustness of block resampling procedures for time series. We first derive a set of formulas to … applications. This renders inference based on standard resampling methods useless already in simple estimation and testing settings …. To solve this problem, we introduce a robust fast resampling scheme that is applicable to a wide class of time series …
Persistent link: https://www.econbiz.de/10003971115
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Multivariate tests of mean-variance efficiency and spanning with a large number of assets and time-varying covariances
Gungor, Sermin; Luger, Richard - 2013
resulting generalized non-parametric bounds tests with Monte Carlo resampling techniques. In sharp contrast to the usual tests …
Persistent link: https://www.econbiz.de/10009746573
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Identifying genuine effects in observational research by means of meta-regressions
Bruns, Stephan B. - 2013
Meta-regression models are increasingly utilized to integrate empirical results across studies while controlling for the potential threats of data-mining and publication bias. We propose extended meta-regression models and evaluate their performance in identifying genuine empirical effects by...
Persistent link: https://www.econbiz.de/10010199109
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Frequentist Methods for Statistical Inference
Kaye, David H. - 2020
resampling methods. It identifies common misinterpretations of computed quantities and discusses some of the comparative …
Persistent link: https://www.econbiz.de/10014100750
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Dependence-Robust Inference Using Resampled Statistics
Leung, Michael P. - 2020
constructed by resampling data in a manner that does not depend on the unknown correlation structure of the data. We prove that …
Persistent link: https://www.econbiz.de/10014034120
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Using large samples in econometrics
MacKinnon, James G. - 2021
As I document using evidence from a journal data repository that I manage, the datasets used in empirical work are getting larger. When we use very large datasets, it can be dangerous to rely on standard methods for statistical inference. In addition, we need to worry about computational issues....
Persistent link: https://www.econbiz.de/10012815681
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A simple two-step method for testing moment inequalities with an application to inference in partially identified models
Romano, Joseph P.; Shaikh, Azeem M.; Wolf, Michael - 2012
This paper considers the problem of testing a finite number of moment inequalities. We propose a two-step approach. In the first step, a confidence region for the moments is constructed. In the second step, this set is used to provide information about which moments are "negative." A...
Persistent link: https://www.econbiz.de/10009743164
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Bootstrapping Rank Statistics
Steland, Ansgar - 1998
is derived for the bootstrap based on resampling from the original data. Further, the residual bootstrap is studied. The …
Persistent link: https://www.econbiz.de/10014219443
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A specification test for nonparametric instrumental variable regression
Gagliardini, Patrick (contributor);  … - 2007
We consider testing for correct specification of a nonparametric instrumental variable regression. In this ill-posed inverse problem setting, the test statistic is based on the empirical minimum distance criterion corresponding to the conditional moment restriction evaluated with a Tikhonov...
Persistent link: https://www.econbiz.de/10003550675
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Bootstrapping mean squared errors of robust small-area estimators : application to the method-of-payments data
Jiongo, Valéry D.; Nguimkeu, Pierre - 2018
This paper proposes a new bootstrap procedure for mean squared errors of robust smallarea estimators. We formally prove the asymptotic validity of the proposed bootstrap method and examine its finite sample performance through Monte Carlo simulations. The results show that our procedure performs...
Persistent link: https://www.econbiz.de/10011864612