Get Dynamic Factor Model Results

Provides access to estimation results, including pre-processing (e.g., standardization input for dynamic workflows), parameter and factor estimates, residuals, and likelihood.

Usage

get_results(dfm_estimates)

Arguments

dfm_estimates

An object of class "JD3_DFMESTIMATES", typically generated using the estimate_ml(), estimate_em(), or estimate_pca() function.

Value

An object of class "JD3_DFMRESULTS" is returned. The following are returned invisibly as a list:

• preprocessing [[1]] the original and transformed data, along with the sample mean and standard deviation used for standardization;

• parameters [[2]] the estimated parameters, the variance of the measurement errors, and the variance-covariance matrix of the VAR errors;

• factors [[3]] the estimated factors and their standard deviations;

• residuals [[4]] the residuals and the standardized residuals for diagnostic checks;

• likelihood [[5]] the estimated log-likelihood and a Boolean indicating whether the estimation has converged.

See also

get_forecasts() to obtain forecast results.

For more information, see the vignette:

utils::browseVignettes(), e.g. browseVignettes(package = "rjd3nowcasting")

Examples

if (FALSE) { # rjd3toolkit::get_java_version() >= rjd3toolkit::minimal_java_version
set.seed(100)
data <- ts(matrix(rnorm(500), 100, 5),
           frequency = 12,
           start = c(2010, 1))
data[100, 1] <- data[99:100, 2] <- data[(1:100)[-seq(3, 100, 3)], 5] <- NA

dfm <- create_model(
    nfactors = 2,
    nlags = 2,
    factors_type = c("M", "M", "YoY", "M", "Q"),
    factors_loading = matrix(data = TRUE, 5, 2),
    var_init = "Unconditional"
)

est_em <- estimate_em(dfm, data)

rslt_em <- get_results(est_em)
}