On non-stationary threshold autoregressive models
نویسندگان
چکیده
منابع مشابه
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...
متن کاملGeneralization error bounds for stationary autoregressive models
We derive generalization error bounds for stationary univariate autoregressive (AR) models. We show that imposing stationarity is enough to control the Gaussian complexity without further regularization. This lets us use structural risk minimization for model selection. We demonstrate our methods by predicting interest rate movements.
متن کاملEstimation in Threshold Autoregressive Models with a Stationary and a Unit Root Regime
This paper treats estimation in a class of new nonlinear threshold autoregressive models with both a stationary and a unit root regime. Existing literature on nonstationary threshold models have basically focused on models where the nonstationarity can be removed by differencing and/or where the threshold variable is stationary. This is not the case for the process we consider, and nonstandard ...
متن کاملEstimation 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...
متن کاملBootstrap 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...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Bernoulli
سال: 2011
ISSN: 1350-7265
DOI: 10.3150/10-bej306