State space model — ssm

Computes state space model with one equation. Starting with a simple lm model, build the all state space model and run it.

Use rjd3sts packages.

Usage

ssm_lm(
  x,
  trend = FALSE,
  var_intercept = 0,
  var_slope = 0,
  var_variables = 0,
  fixed_var_intercept = TRUE,
  fixed_var_trend = TRUE,
  fixed_var_variables = TRUE,
  ...,
  remove_last_dummies = FALSE,
  intercept = TRUE
)

Arguments

x: a lm or dynlm model
trend: boolean indicating if the model should have a trend.
var_intercept, var_slope: variance of the intercept (used if ìntercept = TRUE) and the slope (used if trend = TRUE).
var_variables: variance of the other variables: can be either a single value (same variance for all the variables) or a vector specifying each variance.
fixed_var_intercept, fixed_var_trend, fixed_var_variables: logical indicating if the variance are fixed or estimated.
...: other arguments used in rjd3sts::estimate().
remove_last_dummies: boolean indicating if current dummies (i.e.: only 0 and 1 at the last date) should be removed.
intercept: boolean indicating if the model should have an intercept.

Value

Returns a list containing:

smoothed_states: \(E[a_t|y_0,\dots,y_n]\)
smoothed_stdev: \(\sqrt{V[a_t|y_0,\dots,y_n]}\)
filtering_states: \(E[a_t|y_0,\dots,y_{t-1}]\)
filtering_stdev: \(\sqrt{V[a_t|y_0,\dots,y_{t-1}]}\)
parameters: some estimation parameters
data: data used in the original model

Examples

data_gdp <- window(gdp, start = 1980, end = c(2019, 4))
reg_lin <- lm(
  formula = growth_gdp ~ bc_fr_m1 + diff_bc_fr_m1,
  data = data_gdp
)
ssm <- ssm_lm(reg_lin, fixed_var_intercept = FALSE, fixed_var_variables = FALSE)
ssm
#> Mean of time-varying estimated coefficients (smoothing): 
#>   (Intercept)      bc_fr_m1 diff_bc_fr_m1         noise 
#>       -1.5964        0.0205        0.0425        0.0000 
summary(ssm)
#> Summary of time-varying estimated coefficients (smoothing): 
#>         (Intercept) bc_fr_m1 diff_bc_fr_m1      noise
#> Min.         -1.596  0.01877       0.01990 -1.426e+00
#> 1st Qu.      -1.596  0.01920       0.02492 -1.953e-01
#> Median       -1.596  0.02094       0.04371  1.087e-02
#> Mean         -1.596  0.02046       0.04252 -1.605e-17
#> 3rd Qu.      -1.596  0.02147       0.05741  2.240e-01
#> Max.         -1.596  0.02174       0.07843  7.934e-01