Empirical likelihood inference in autoregressive models with time-varying variances
This paper develops the empirical likelihood ( $ \mathrm {EL} $ ) inference procedure for parameters in autoregressive models with the error variances scaled by an unknown nonparametric time-varying function. Compared with existing methods based on non-parametric and semi-parametric estimation, the...
Main Authors: | Yu Han, Chunming Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
Taylor & Francis Group
2022-05-01
|
Series: | Statistical Theory and Related Fields |
Subjects: | |
Online Access: | http://dx.doi.org/10.1080/24754269.2021.1913977 |
Similar Items
-
Changes in the Unconditional Variance and Autoregressive Conditional Heteroscedasticity
by: Amado Peiró
Published: (2016-09-01) -
Empirical likelihood inference for threshold autoregressive conditional heteroscedasticity model
by: Cuixin Peng, et al.
Published: (2021-03-01) -
Restricted Empirical Likelihood Estimation for Time Series Autoregressive Models
by: Mahdieh Bayati, et al.
Published: (2021-02-01) -
Empirical likelihood based heteroscedasticity diagnostics for varying coefficient partially nonlinear models
by: Cuiping Wang, et al.
Published: (2024-12-01) -
A Blockwise Empirical Likelihood Test for Gaussianity in Stationary Autoregressive Processes
by: Chioneso S. Marange, et al.
Published: (2023-02-01)