predict.SDFMFit: Generic plotting function for SDFMFit S3 objects

In TwoStepSDFM: Estimate a Sparse Mixed Frequency Gaussian Factor Model Using a Two-Step Procedure

Generic plotting function for SDFMFit S3 objects

Description

Predict all missing observations due to ragged edges in the data set plus horizon steps ahead.

Usage

## S3 method for class 'SDFMFit'
predict(object, horizon = 0, ...)

Arguments

object	SDFMFit object.
horizon	Number of forecasting steps into the future. Must be smaller than or equal to x$factor_fcast_horizon.
...	Additional parameters for the prediction function.

Value

A named list of plot objects:

data

Object containing the original data. The object inherits its class from object$data: If data is provided as zoo, data will be a zoo object. If data is provided as matrix, data will be a (no_of_factors\timesno_of_obs) matrix.

data_missing_pred

Object containing only the predictions of all missing observations plus the forecasts. Inherits its class from object$data as above.

data_imputed

Object containing the observed data, predictions of all missing observations plus the forecasts. Inherits its class from object$data as above.

Author(s)

Domenic Franjic

Examples

data(factor_model)
no_of_vars <- dim(factor_model$data)[2]
no_of_factors <- dim(factor_model$factors)[2]
sdfm_fit <- twoStepSDFM(data = factor_model$data, delay = factor_model$delay,
                        selected = rep(floor(0.5 * no_of_vars), no_of_factors),
                        no_of_factors = no_of_factors, fcast_horizon = 5)
dfm_fit <- twoStepDenseDFM(data = factor_model$data, delay = factor_model$delay, 
                           no_of_factors = no_of_factors, fcast_horizon = 5)
predict(sdfm_fit, horizon = 5)
predict(dfm_fit, horizon = 5)

TwoStepSDFM documentation built on May 19, 2026, 9:07 a.m.