Statistics & Probability Letters

2016

DOI: 10.1016/j.spl.2015.09.025

|View full text |Cite

|

Sign up to set email alerts

|

A note on Bartlett correction factor for tests on cointegrating relations

Alessandra Canepa

¹

Abstract: In this paper it is proposed to use a non-parametric bootstrap based Bartlett correction factor for the LR test for linear restrictions on the cointegrating vectors to reduce the finite sample size distortion problem of the test statistic.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

0

Mentioning

0

Contrasting

0

Year Published

2017

2017

2021

2021

Publication Types

Select...

Article2

Book1

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 12 publications

Supporting

0

Mentioning

0

Contrasting

0

Order By: Relevance

Testing in VECMs with Small Samples

¹

,

²

,

³

2017

Multivariate Modelling of Non-Stationary Economic Time Series

View full text Add to dashboard Cite

No abstract

Testing in VECMs with Small Samples

¹

,

²

,

³

2017

Multivariate Modelling of Non-Stationary Economic Time Series

View full text Add to dashboard Cite

No abstract

Small Sample Adjustment for Hypotheses Testing on Cointegrating Vectors

¹

2021

Journal of Time Series Econometrics

View full text Add to dashboard Cite

Johansen’s (2000. “A Bartlett Correction Factor for Tests of on the Cointegrating Relations.” Econometric Theory 16: 740–78) Bartlett correction factor for the LR test of linear restrictions on cointegrated vectors is derived under the i.i.d. Gaussian assumption for the innovation terms. However, the distribution of most data relating to financial variables is fat-tailed and often skewed; there is therefore a need to examine small sample inference procedures that require weaker assumptions for the innovation term. This paper suggests that using the non-parametric bootstrap to approximate a Bartlett-type correction provides a statistic that does not require specification of the innovation distribution and can be used by applied econometricians to perform a small sample inference procedure that is less computationally demanding than it’s analytical counterpart. The procedure involves calculating a number of bootstrap values of the LR test statistic and estimating the expected value of the test statistic by the average value of the bootstrapped LR statistic. Simulation results suggest that the inference procedure has good finite sample property and is less dependent on the parameter space of the data generating process.

Consumption Tilting, Habit Persistence and Precautionary Savings: Are They a New Hope for Current Account Approach?

²

2010

View full text Add to dashboard Cite

No abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Product

Browser Extension Assistant by scite Citation Statement Search Reference Check Visualizations Dashboards Explore Journals Explore Organizations Explore Funders Embedding Badge Embedding Citation Search Pricing

Resources

Blog Help & FAQ Accessibility Statement API Terms For Universities & Governments For Researchers For Publishers For Corporate, Pharma & Enterprise Author Marketing Become an Affiliate Get an organization trial or quote scite Data & Services

About

News & Press Careers Read our Paper Coverage

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Copyright © 2025 scite LLC. All rights reserved.

Made with 💙 for researchers

Part of the Research Solutions Family.