Improved Prediction Intervals for ARIMA Processes and Structural Time Series
Compute a theoretical autocovariance function of ARMA process
Prediction Intervals for ARIMA Processes with Exogenous Variables Usin...
Compute the average coverage of the prediction intervals computed by n...
Compute the average coverage of the prediction intervals computed by `...
Compute the partial derivatives of theoretical autocovariance function...
Large Sample Approximation of Information Matrix for ARMA process
Compute different types of importance weights based on Jeffreys's prio...
Prediction Intervals for Structural Time Series with Exogenous Variabl...
Improved Prediction Intervals for ARIMA Processes and Structural Time ...
Prediction intervals for ARIMA and structural time series models using importance sampling approach with uninformative priors for model parameters, leading to more accurate coverage probabilities in frequentist sense. Instead of sampling the future observations and hidden states of the state space representation of the model, only model parameters are sampled, and the method is based solving the equations corresponding to the conditional coverage probability of the prediction intervals. This makes method relatively fast compared to for example MCMC methods, and standard errors of prediction limits can also be computed straightforwardly.