R: Improved Prediction Intervals for ARIMA Processes and...
tsPI
R Documentation
Improved Prediction Intervals for ARIMA Processes and Structural Time Series
Description
Package tsPI computes prediction intervals for ARIMA and structural
time series models by 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.
References
Helske, J. and Nyblom, J. (2013). Improved frequentist prediction
intervals for autoregressive models by simulation.
In Siem Jan Koopman and Neil Shephard, editors,
Unobserved Components and Time Series Econometrics. Oxford University Press. In press.
Helske, J. and Nyblom, J. (2014). Improved frequentist prediction intervals for
ARMA models by simulation.
In Johan Knif and Bernd Pape, editors,
Contributions to Mathematics, Statistics, Econometrics, and Finance:
essays in honour of professor Seppo Pynn<c3><83><c2><b6>nen,
number 296 in Acta Wasaensia, pages 71<c3><a2><c2><80><c2><93>86. University of Vaasa.
Helske, J. (2015). Prediction and interpolation of time series by state space models. University of Jyv<c3><83><c2><a4>skyl<c3><83><c2><a4>. PhD thesis, Report 152.