bayesforecast (version 1.0.1)

set_prior: Set a prior distribution to a model parameter.

Description

setting a prior distribution to an specify model parameter.

Usage

set_prior(model, par = "ar", dist = normal(), lag = 0)

Arguments

model

a time series model class specified in varstan.

par

a string value with the desired parameter which a prior is defined could be: "mu0", "sigma0", "ar", "ma", "arch", "garch", "mgarch", "dfv", "df", "LKJ" or "breg".

dist

the distribution of the prior parameter. The only accepted is a prior_dist object.

lag

an optional integer value, indicates the desired lag of the parameter which the prior is defined if lag = 0, then the prior distribution will be applied for all lags.

Value

a time series model class specified in varstan with the changed prior.

Details

varstan provides its own functions to manipulate the parameter prior, this functions return a prior_dist class, the dist argument only accepts this objects.

lag parameter is an optional value to change the prior distribution of one parameter in particular, this argument is only valid for: "ar","ma", "arch", "garch", "mgarch", or "breg" par arguments. lag has to be a integer lower than the total amount of lagged parameters of the model. For example, to ONLY change the prior of the second "arch" parameter in a garch(3,1) model, a lag = 2 values must be specified.

For varma and Bekk models the covariance matrix Sigma is factorized as follows:

              Sigma = D' Omega D

Where Omega is the correlation matrix that accepts an LKJ prior distribution D is a diagonal matrix with the inverse std deviations

For changing the degree freedom in a LKJ distribution for omega use par = "LKJ" and dist = LKJ(df), where df are the desired degree freedom.

For changing the the priors in the diagonal D use par = "sigma0" and select one of the available prior distributions.

For ar, ma garch, arch parameters in varma and Bekk models only normal distributions priors with different mu and sd are accepted. Even if get_prior accepts its change, Stan will change it to a normal(0,1) prior.

Examples

# NOT RUN {
library(astsa)
dat = Sarima(birth,order = c(1,1,2))
dat = set_prior(model = dat,par = "ar",dist = normal(0,2))
dat

dat = set_prior(model = dat,par = "mu0",dist = student(mu=0,sd = 2.5,df = 7))
dat

dat = set_prior(model = dat,par = "ma",dist= beta(shape1 = 2,shape2 = 2),lag = 2)
dat

# }