Bootstrap Prediction Intervals for Threshold Autoregressive Models
نویسنده
چکیده
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 intervals.
منابع مشابه
Functional-Coefficient Autoregressive Model and its Application for Prediction of the Iranian Heavy Crude Oil Price
Time series and their methods of analysis are important subjects in statistics. Most of time series have a linear behavior and can be modelled by linear ARIMA models. However, some of realized time series have a nonlinear behavior and for modelling them one needs nonlinear models. For this, many good parametric nonlinear models such as bilinear model, exponential autoregressive model, threshold...
متن کاملSemiparametric Bootstrap Prediction Intervals in time Series
One of the main goals of studying the time series is estimation of prediction interval based on an observed sample path of the process. In recent years, different semiparametric bootstrap methods have been proposed to find the prediction intervals without any assumption of error distribution. In semiparametric bootstrap methods, a linear process is approximated by an autoregressive process. The...
متن کاملBootstrap Prediction Intervals for Autoregressive Models Based on Asymptotically Mean-Unbiased Parameter Estimators
The use of asymptotically mean-unbiased estimation is considered as a means of biascorrection, when bootstrap prediction interval is constructed for autoregressive (AR) models with unknown lag order. Its computational efficiency enables application of the endogenous lag order bootstrap algorithm to prediction intervals. Extensive Monte Carlo experiments are conducted using a number of stationar...
متن کاملBeyond point forecasting: evaluation of alternative prediction intervals for tourist arrivals
This paper evaluates the performance of prediction intervals generated from alternative time series models, in the context of tourism forecasting. The forecasting methods considered include the autoregressive (AR) model, the AR model using the bias-corrected bootstrap, seasonal ARIMA models, innovations state-space models for exponential smoothing, and Harvey’s structural time series models. We...
متن کاملOn the Consistency of Sieve Bootstrap Prediction Intervals for Stationary Time Series
In the article, we consider construction of prediction intervals for stationary time series using Bühlmann’s [8], [9] sieve bootstrap approach. Basic theoretical properties concerning consistency are proved. We extend the results obtained earlier by Stine [21], Masarotto and Grigoletto [13] for an autoregressive time series of finite order to the rich class of linear and invertible stationary m...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2008