Estimate a Dynamic Factor Model Using Principal Components Analysis

Estimate the model parameters using Principal Component Analysis (PCA). While this approach is fast, it is generally not recommended to rely on it exclusively, particularly when the dataset includes quarterly series or variables related to year-on-year growth rates.

Usage

estimate_pca(
  dfm,
  data,
  standardized = FALSE,
  input_standardization = NULL,
  re_estimate = TRUE
)

Arguments

dfm

An object of class "JD3_DFMMODEL", typically generated using the create_model() function.

data

A "mts" object containing the (transformed) input data.

standardized

Boolean. Indicates whether the input data are already standardized. The default is FALSE, in which case standardization is applied as a preprocessing step.

input_standardization

A matrix specifying the mean and standard deviation of the series to be used during standardization. The default is NULL, meaning that these quantities are computed from the data. This argument can be set using the output of get_results()$preprocessing$sample_mean_stdev from a previous model estimation. If provided manually, it must be a two-column matrix with the means in the first column and the standard deviations in the second column. The ordering of the rows must match the series in data. This argument must be provided if re_estimate = FALSE, and is ignored if standardized = TRUE.

re_estimate

Boolean. Indicates whether the model parameters should be re-estimated. The default is TRUE. It can be set to FALSE to keep the model frozen for some period of time, although prolonged use of a frozen model is not recommended.

Value

An object of class "JD3_DFMESTIMATES" is returned. The following are returned invisibly as a list:

• dfm [[1]] an object of class "JD3_DFMMODEL" containing the estimated model parameters;

• data [[2]] the value of the data argument;

• is_standardized [[3]] the value of the standardized argument;

• input_standardization [[4]] the value of the input_standardization argument;

• additional elements not relevant for Principal Components Analysis estimates.

See also

create_model() to define a new model,

estimate_em() for estimation using the Expectations-Maximization algorithm,

estimate_ml() for estimation using maximum likelihood,

get_results() to access estimation results,

get_forecasts() to obtain forecasts.

For more information, see the vignette:

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

Examples

if (FALSE) { # rjd3toolkit::get_java_version() >= rjd3toolkit::minimal_java_version
# input data
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

# define a new model
dfm <- create_model(
    nfactors = 2,
    nlags = 2,
    factors_type = c("M", "M", "M", "M", "M"),
    factors_loading = matrix(data = TRUE, 5, 2),
    var_init = "Unconditional"
)

# estimate using PCA
est_pca_a <- estimate_pca(dfm, data)

# results and forecasts
rslts_a <- get_results(est_pca_a)
fcsts_a <- get_forecasts(est_pca_a)

# no re-estimation of a previous model while integrating updated data in the output object
data_new <- data
data_new[99, 2] <- 1
est_pca_b <- estimate_pca(est_pca_a$dfm,
                          data_new,
                          input_standardization = rslts_a$preprocessing$sample_mean_stdev,
                          re_estimate = FALSE)
}