DFM News analysis
Arguments
- dfm_estimates
an object of class 'JD3_DfmEstimates'. Typically generated by the functions estimate_pca(), estimate_em() or estimate_ml().
- new_data
an mts object containing the updated dataset.
- target_series
the name of the series of interest. By default, the first series is considered.
- n_fcst
the number of forecasting periods to consider. Default is 3.
References
Banbura and Modugno (2010) - Maximum likelihood estimation of factor models on data sets with arbitrary pattern of missing data
Examples
set.seed(100)
data_t1<-ts(matrix(rnorm(500), 100, 5), frequency = 12, start = c(2010,1))
data_t1[100,1]<-data_t1[99:100,2]<-data_t1[(1:100)[-seq(3,100,3)],5]<-NA
data_t2<-ts(rbind(data_t1, rep(NA,5)), frequency = 12, start = c(2010,1))
data_t2[100,1]<-data_t2[99,2]<-data_t2[101,3]<-data_t2[101,4]<-1
dfm_model <- create_model(nfactors=2,
nlags=2,
factors_type = c("M", "M", "YoY", "M", "Q"),
factors_loading = matrix(TRUE, 5, 2),
var_init = "Unconditional")
est_em<-estimate_em(dfm_model, data_t1)
# or to use any previous frozen model:
# est_em_frozen<-estimate_em(dfm_model, data_t1, re_estimate = FALSE)
news<-get_news(est_em, data_t2, target_series = "Series 2", n_fcst = 2)