Efficient prediction for linear and nonlinear autoregressive models
نویسندگان
چکیده
منابع مشابه
Efficient Prediction for Linear and Nonlinear Autoregressive Models
Conditional expectations given past observations in stationary time series are usually estimated directly by kernel estimators, or by plugging in kernel estimators for transition densities. We show that, for linear and nonlinear autoregressive models driven by independent innovations, appropriate smoothed and weighted von Mises statistics of residuals estimate conditional expectations at better...
متن کاملWhich Methodology is Better for Combining Linear and Nonlinear Models for Time Series Forecasting?
Both theoretical and empirical findings have suggested that combining different models can be an effective way to improve the predictive performance of each individual model. It is especially occurred when the models in the ensemble are quite different. Hybrid techniques that decompose a time series into its linear and nonlinear components are one of the most important kinds of the hybrid model...
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 کاملPrediction with Mixture Autoregressive Models
Mixture autoregressive (MAR) models have the attractive property that the shape of the conditional distribution of a forecast depends on the recent history of the process. In particular, it may have a varying number of modes over time. We show that the distributions of the multi-step predictors in MAR models are also mixtures and specify them analytically. In the important case when the origina...
متن کاملNonlinear Autoregressive Models and Long Memory
This note shows that regime switching nonlinear autoregressive models widely used in the time series literature can exhibit arbitrary degrees of long memory via appropriate definition of the model regimes.
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: The Annals of Statistics
سال: 2006
ISSN: 0090-5364
DOI: 10.1214/009053606000000812