rjd3nowcasting

Overview

Nowcasting is often defined as the prediction of the present, the very near future and the very recent past.

rjd3nowcasting provides helps to operationalize the process of nowcasting. It can be used to specify and estimate Dynamic Factor Models. Recent version of the package also includes news analysis. The way the package was conceived is inspired by the GUI add-in developed for JDemetra+ V2.

Installation

Running rjd3 packages requires Java 17 or higher. How to set up such a configuration in R is explained here

Latest release

To get the current stable version (from the latest release):

• From GitHub:

# install.packages("remotes")
remotes::install_github("rjdverse/rjd3toolkit@*release")
remotes::install_github("rjdverse/rjd3nowcasting@*release", build_vignettes = TRUE)

• From r-universe:

install.packages("rjd3nowcasting", repos = c("https://rjdverse.r-universe.dev", "https://cloud.r-project.org"))

Development version

You can install the development version of rjd3nowcasting from GitHub with:

# install.packages("remotes")
remotes::install_github("rjdverse/rjd3nowcasting")

Usage

library("rjd3nowcasting")

Once the package is loaded, there are four steps to follow:

1. Import data
2. Create or update the model
3. Estimate the model
4. Get results

Detailed information concerning each step can be found in the vignette.

1. Input

set.seed(100)
data0 <- stats::ts(
    data = matrix(rnorm(500), 100, 5), 
    frequency = 12, 
    start = c(2010, 1)
)
data0[100, 1] <- data0[99:100, 2] <- data0[(1:100)[-seq(3, 100, 3)], 5] <- NA

data1 <- stats::ts(
    data = rbind(data0, c(NA, NA, 1, 1, NA)), 
    frequency = 12, 
    start = c(2010, 1)
)
data1[100,1] <- data1[99,2] <- 1

2. Create or update the model

### Create model from scratch
dfm0 <- create_model(nfactors=2,
                     nlags=2,
                     factors_type = c("M", "M", "YoY", "M", "Q"),
                     factors_loading = matrix(data = TRUE, 5, 2),
                     var_init = "Unconditional")

### Update model
est0 <- estimate_em(dfm0, data0) # cfr. next step

dfm1 <- est0$dfm # R object (list) to potentially save from one time to another  
# or, equivalently,
dfm1 <- create_model(nfactors=2,
                     nlags=2,
                     factors_type = c("M", "M", "YoY", "M", "Q"),
                     factors_loading = matrix(data = TRUE, 5, 2),
                     var_init = "Unconditional",
                     var_coefficients = est0$dfm$var_coefficients,
                     var_errors_variance = est0$dfm$var_errors_variance,
                     measurement_coefficients = est0$dfm$measurement_coefficients,
                     measurement_errors_variance = est0$dfm$measurement_errors_variance)

3. Estimate the model

est1 <- estimate_ml(dfm1, data1)
# or est1<-estimate_em(dfm1, data1)
# or est1<-estimate_pca(dfm1, data1)

4. Get results

rslt1 <- get_results(est1)
print(rslt1)
fcst1 <- get_forecasts(est1, n_fcst = 2)
print(fcst1)
plot(fcst1)

news1 <- get_news(est0, data1, target_series = "Series 1", n_fcst = 2)
print(news1)
plot(news1)
# ...

Package Maintenance and contributing

Any contribution is welcome and should be done through pull requests and/or issues. pull requests should include updated tests and updated documentation. If functionality is changed, docstrings should be added or updated.

Licensing

The code of this project is licensed under the European Union Public Licence (EUPL).