A Gini estimator for regression with autocorrelated errors
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Ndéné Ka
and
Stéphane Mussard
Abstract
The widely used Prais–Winsten technique for estimating parameters of linear regression model with serial correlation is sensitive to outliers. In this paper, an alternative method based on Gini mean difference (GMD) is proposed. A Monte Carlo simulation is used to show that the Gini estimator is more robust than the general least squares one when the data are contaminated by outliers.
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Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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Supplementary Material
The online version of this article offers supplementary material (https://doi.org/10.1515/snde-2020-0134).
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Articles in the same Issue
- Frontmatter
- Research Articles
- Estimation and forecasting of long memory stochastic volatility models
- Uncertainty and realized jumps in the pound-dollar exchange rate: evidence from over one century of data
- Bidirectional volatility transmission between stocks and bond in East Asia – The quantile estimates based on wavelets
- A threshold model for the spread
- A Gini estimator for regression with autocorrelated errors
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Articles in the same Issue
- Frontmatter
- Research Articles
- Estimation and forecasting of long memory stochastic volatility models
- Uncertainty and realized jumps in the pound-dollar exchange rate: evidence from over one century of data
- Bidirectional volatility transmission between stocks and bond in East Asia – The quantile estimates based on wavelets
- A threshold model for the spread
- A Gini estimator for regression with autocorrelated errors
- State price density estimation with an application to the recovery theorem
- Testing for random coefficient autoregressive and stochastic unit root models
