/nowcastDFM

Dynamic factor models (DFM) in R. Easy estimation and new data contributions to changes in prediction.

Primary LanguageR

nowcastDFM

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.

Installation

install.packages("nowcastDFM")`

If this does not work, you can install directly from Github with:

install.packages("devtools")
devtools::install_github("dhopp1/nowcastDFM")

Functionality

  • 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.

Example

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")