bssm (version 1.1.7-1)

ar1_lg: Univariate Gaussian model with AR(1) latent process

Description

Constructs a simple Gaussian model where the state dynamics follow an AR(1) process.

Usage

ar1_lg(y, rho, sigma, mu, sd_y, beta, xreg = NULL)

Arguments

y

Vector or a ts object of observations.

rho

Prior for autoregressive coefficient. Should be an object of class bssm_prior.

sigma

Prior for the standard deviation of noise of the AR-process. Should be an object of class bssm_prior

mu

A fixed value or a prior for the stationary mean of the latent AR(1) process. Should be an object of class bssm_prior or scalar value defining a fixed mean such as 0.

sd_y

A prior for the standard deviation of observation equation.

beta

Prior for the regression coefficients. Should be an object of class bssm_prior or bssm_prior_list (in case of multiple coefficients) or missing in case of no covariates.

xreg

Matrix containing covariates with number of rows matching the length of y.

Value

Object of class ar1_lg.

Examples

# NOT RUN {
set.seed(1)
mu <- 2
rho <- 0.7
sd_y <- 0.1
sigma <- 0.5
beta <- -1
x <- rnorm(30)
z <- y <- numeric(30)
z[1] <- rnorm(1, mu, sigma / sqrt(1 - rho^2))
y[1] <- rnorm(1, beta * x[1] + z[1], sd_y)
for(i in 2:30) {
  z[i] <- rnorm(1, mu * (1 - rho) + rho * z[i - 1], sigma)
  y[i] <- rnorm(1, beta * x[i] + z[i], sd_y)
}
model <- ar1_lg(y, rho = uniform(0.5, -1, 1), 
  sigma = halfnormal(1, 10), mu = normal(0, 0, 1), 
  sd_y = halfnormal(1, 10), 
  xreg = x,  beta = normal(0, 0, 1))
out <- run_mcmc(model, iter = 2e4)
summary(out, return_se = TRUE)

# }