Empirical likelihood inference for threshold autoregressive conditional heteroscedasticity model - Cuixin Peng (Journal of Inequalities and Applications, 2021)
Abstract This paper considers the parameter estimation problem of a first-order threshold autoregressive conditional heteroscedasticity model by using the empirical likelihood method. We obtain the empirical likelihood ratio statistic based on the estimating equation of the least squares estimation and construct the confidence region for the model parameters. Simulation studies indicate that the empirical likelihood method outperforms the normal approximation-based method in terms of coverage probability.