Detecting at‐Most‐m Changes in Linear Regression Models-Reference-Cited by-同舟云学术

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Detecting at‐Most‐m Changes in Linear Regression Models

Published:2016-12-26 Issue:4 Volume:38 Page:552-590
ISSN:0143-9782
Container-title:Journal of Time Series Analysis
language:en
Short-container-title:J. Time Ser. Anal.

Author:

Horváth Lajos¹,Pouliot William²,Wang Shixuan²

Affiliation:

1. Department of Mathematics University of Utah Salt Lake City 84112‐0090 UT USA

2. Department of Economics University of Birmingham Birmingham B15 2TT UK

Funder

National Science Foundation

Publisher

Wiley

Subject

Applied Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/jtsa.12228

Reference58 articles.

1. On a Test Statistic for Linear Trend

2. Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation

3. Tests for Parameter Instability and Structural Change With Unknown Change Point

4. An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator

5. SEQUENTIAL TESTING FOR THE STABILITY OF HIGH-FREQUENCY PORTFOLIO BETAS

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1. Regression Models;Springer Series in Statistics;2023-12-11

2. Testing for changes in linear models using weighted residuals;Journal of Multivariate Analysis;2023-11

3. Modelling Australian electricity prices using indicator saturation;Energy Economics;2023-04

4. Change-points and functional features of intraday volatility in China stock market;Annals of Operations Research;2022-10-11

5. Multiple Change-Points Estimation in Linear Regression Models via an Adaptive LASSO Expectile Loss Function;Journal of Statistical Theory and Practice;2022-05-24

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