jeffreys

approx_joint_jeffreys

approx_marginal_jeffreys

exact_joint_jeffreys

exact_marginal_jeffreys

These functions compute different types of importance weights based on Jeffreys's priors used in <code>arima_pi</code>.

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

jeffreys function

<dl><dt>psi</dt>
<dd>vector containing the ar and ma parameters (in that order).</dd>
<dt>xreg</dt>
<dd>matrix or data frame containing the exogenous variables
(not including the intercept which is always included for non-differenced series)</dd>
<dt>p</dt>
<dd>number of ar parameters</dd>
<dt>q</dt>
<dd>number of ma parameters</dd>
<dt>n</dt>
<dd>length of the time series</dd></dl>

Arguments

Compute different types of importance weights based on Jeffreys's prior — jeffreys

<dl>

<dt>psi</dt>
<dd>vector containing the ar and ma parameters (in that order).</dd>


<dt>xreg</dt>
<dd>matrix or data frame containing the exogenous variables
(not including the intercept which is always included for non-differenced series)</dd>


<dt>p</dt>
<dd>number of ar parameters</dd>


<dt>q</dt>
<dd>number of ma parameters</dd>


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

</dl>

jeffreys: Compute different types of importance weights based on Jeffreys's prior

Description

Usage

Arguments

See Also