priors: Compute different types of importance weights based on...

In helske/tsPI: Improved Prediction Intervals for ARIMA Processes and Structural Time Series

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

Description

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

Usage

approx_joint_jeffreys(psi, xreg = NULL, p, q, n)

approx_marginal_jeffreys(psi, p, q)

exact_joint_jeffreys(psi, xreg = NULL, p, q, n)

exact_marginal_jeffreys(psi, p, q, n)

Arguments

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

See Also

arima_pi.

helske/tsPI documentation built on Sept. 9, 2023, 8:15 a.m.