Multivariate contemporaneous-threshold autoregressive models

Threshold Quantile Autoregressive Models

We study in this article threshold quantile autoregressive processes. In particular we propose estimation and inference of the parameters in nonlinear quantile processes when the threshold parameter defining nonlinearities is known for each quantile, and also when the parameter vector is estimated consistently. We derive the asymptotic properties of the nonlinear threshold quantile autoregressi...

Chapter 40 : Multivariate autoregressive models

Estimation in Threshold Autoregressive Models with Nonstationarity

This paper proposes a class of new nonlinear threshold autoregressive models with both stationary and nonstationary regimes. Existing literature basically focuses on testing for a unit–root structure in a threshold autoregressive model. Under the null hypothesis, the model reduces to a simple random walk. Parameter estimation then becomes standard under the null hypothesis. How to estimate para...

Bootstrap Prediction Intervals for Threshold Autoregressive Models

This paper proposes the use of prediction intervals based on bootstrap for threshold autoregressive models. We consider four bootstrap methods to account for the variability of estimated threshold values, correct the bias of autoregressive coefficients and allow for heterogenous errors. Simulation shows that bootstrap prediction intervals generally perform better than classical prediction inter...

Threshold Variable Determination and Threshold Variable Driven Switching Autoregressive Models

In this paper we propose a new class of nonlinear time series models, the threshold variable driven switching autoregressive models. It is a hierarchical model that combines two important nonlinear time series models, the threshold autoregressive (AR) models and the random switching AR models. The underlying time series process switches between two (or more) different linear models. The switchi...