Run dynamic factor models (DFM) in R. Adapted from Bok et al. 2017, MATLAB code. The package provides the ability to estimate a DFM model using the expectation–maximization method, obtain predictions from estimated models, and obtain the impact of new data releases on model predictions. On CRAN.
install.packages("nowcastDFM")`
If this does not work, you can install directly from Github with:
install.packages("devtools")
devtools::install_github("dhopp1/nowcastDFM")
dfm: estimate a dynamic factor model using the EM method. ?dfm for more info.predict_dfm: obtain predictions from a previously estimated model. ?predict_dfm for more info.gen_news: obtain impacts of new data releases and revisions on the forecast of a target variable. ?gen_news for more info.Given data is a dataframe (not a tibble) with a date column and 4 columns for various seasonally adjusted growth rates of economic series with missing values of NA:
library(nowcastDFM) # estimate a DFM with one block for all variables output_dfm <- dfm(data) # estimate a DFM with two different blocks blocks <- data.frame(block_1 = c(1,1,1,0), block_2 = c(0,0,1,1)) # defining two blocks output_dfm <- dfm(data, blocks = blocks) # get predictions from estimated DFM for the following 3 months # new data is dataframe with same columns as data the model was trained on, but newer data predictions <- predict_dfm(new_data, output_dfm, months_ahead = 3) # get impact of new data on predictions for a particular variable and time period # old_data and new_data are dataframes with same columns as the data the model was trained on, but with older and newer data news <- gen_news(old_data, new_data, output_dfm, target_variable = "target_name", target_period = "2020-01-01")
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