dacv_arma

Function <code>dacv_arma</code> computes the partial derivatives of theoretical autocovariance function of ARMA process

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.

Jouni Helske

tsPI

Improved Prediction Intervals for ARIMA Processes and Structural
Time Series

dacv_arma function

<dl><dt>phi</dt>
<dd>vector containing the AR parameters</dd>
<dt>theta</dt>
<dd>vector containing the MA parameters</dd>
<dt>n</dt>
<dd>length of the time series</dd></dl>

Arguments

Compute the partial derivatives of theoretical autocovariance function of ARMA process — dacv_arma

<dl>

<dt>phi</dt>
<dd>vector containing the AR parameters</dd>


<dt>theta</dt>
<dd>vector containing the MA parameters</dd>


<dt>n</dt>
<dd>length of the time series</dd>

</dl>

dacv_arma: Compute the partial derivatives of theoretical autocovariance function of ARMA process

Description

Usage

Value

Arguments

See Also