bsm_lg | R Documentation |

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,
a1 = NULL,
P1 = NULL,
D = NULL,
C = NULL
)
```

`y` |
A vector or a |

`sd_y` |
Standard deviation of the noise of observation equation.
Should be an object of class |

`sd_level` |
Standard deviation of the noise of level equation.
Should be an object of class |

`sd_slope` |
Standard deviation of the noise of slope equation.
Should be an object of class |

`sd_seasonal` |
Standard deviation of the noise of seasonal equation.
Should be an object of class |

`beta` |
A prior for the regression coefficients.
Should be an object of class |

`xreg` |
A matrix containing covariates with number of rows matching the
length of |

`period` |
Length of the seasonal pattern.
Must be a positive value greater than 2 and less than the length of the
input time series. Default is |

`a1` |
Prior means for the initial states (level, slope, seasonals). Defaults to vector of zeros. |

`P1` |
Prior covariance matrix for the initial states (level, slope, seasonals).Default is diagonal matrix with 100 on the diagonal. |

`D` |
Intercept terms for observation equation, given as a length n numeric vector or a scalar in case of time-invariant intercept. |

`C` |
Intercept terms for state equation, given as a m times n matrix or m times 1 matrix in case of time-invariant intercept. |

An object of class `bsm_lg`

.

```
set.seed(1)
n <- 50
x <- rnorm(n)
level <- numeric(n)
level[1] <- rnorm(1)
for (i in 2:n) level[i] <- rnorm(1, -0.2 + level[i-1], sd = 0.1)
y <- rnorm(n, 2.1 + x + level)
model <- bsm_lg(y, sd_y = halfnormal(1, 5), sd_level = 0.1, a1 = level[1],
P1 = matrix(0, 1, 1), xreg = x, beta = normal(1, 0, 1),
D = 2.1, C = matrix(-0.2, 1, 1))
ts.plot(cbind(fast_smoother(model), level), col = 1:2)
prior <- uniform(0.1 * sd(log10(UKgas)), 0, 1)
# period here is redundant as frequency(UKgas) = 4
model_UKgas <- bsm_lg(log10(UKgas), sd_y = prior, sd_level = prior,
sd_slope = prior, sd_seasonal = prior, period = 4)
# Note small number of iterations for CRAN checks
mcmc_out <- run_mcmc(model_UKgas, iter = 5000)
summary(mcmc_out, return_se = TRUE)
# Use the summary method from coda:
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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