Forecasting for Stationary and Non-Stationary Time Series
Compute autocovariances of an AR(p) process
Mean Squared Prediction Errors, for a single
Compute for a tvAR(p) process
Forecasting of Stationary and Non-Stationary Time Series
Mean squared or absolute -step ahead prediction errors
Plot a MSPE or MAPE object
-step Prediction coefficients
Simulation of an tvARMA(p,q) time series.
Workhorse function for tvARMA time series generation
Methods to compute linear h-step ahead prediction coefficients based on localised and iterated Yule-Walker estimates and empirical mean squared and absolute prediction errors for the resulting predictors. Also, functions to compute autocovariances for AR(p) processes, to simulate tvARMA(p,q) time series, and to verify an assumption from Kley et al. (2019), Electronic of Statistics, forthcoming. Preprint <arXiv:1611.04460>.
Useful links