Basic Structural (Time Series) Model
Constructs a basic structural model with local level or local trend component and seasonal component.
bsm_lg( y, sd_y, sd_level, sd_slope, sd_seasonal, beta, xreg = NULL, period = frequency(y), a1, P1, D, C )
y |
Vector or a |
sd_y |
A fixed value or prior for the standard error of observation equation. See priors for details. |
sd_level |
A fixed value or a prior for the standard error of the noise in level equation. See priors for details. |
sd_slope |
A fixed value or a prior for the standard error of the noise in slope equation. See priors for details. If missing, the slope term is omitted from the model. |
sd_seasonal |
A fixed value or a prior for the standard error of the noise in seasonal equation. See priors for details. If missing, the seasonal component is omitted from the model. |
beta |
Prior for the regression coefficients. |
xreg |
Matrix containing covariates. |
period |
Length of the seasonal component i.e. the number of |
a1 |
Prior means for the initial states (level, slope, seasonals). Defaults to vector of zeros. |
P1 |
Prior covariance for the initial states (level, slope, seasonals). Default is diagonal matrix with 1000 on the diagonal. |
D, C |
Intercept terms for observation and state equations, given as a length n vector and m times n matrix respectively. |
Object of class bsm_lg.
prior <- uniform(0.1 * sd(log10(UKgas)), 0, 1) model <- bsm_lg(log10(UKgas), sd_y = prior, sd_level = prior, sd_slope = prior, sd_seasonal = prior) mcmc_out <- run_mcmc(model, iter = 5000) summary(expand_sample(mcmc_out, "theta"))$stat mcmc_out$theta[which.max(mcmc_out$posterior), ] sqrt((fit <- StructTS(log10(UKgas), type = "BSM"))$coef)[c(4, 1:3)]
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