The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

convergenceDFM

convergenceDFM is an R package for convergence analysis in macro-financial panels, combining Dynamic Factor Models (DFM) with mean-reverting Ornstein-Uhlenbeck (OU) processes.

Main Features

• Dynamic Factor Models (DFM): Static and approximate estimation for large panels with VAR/VECM stability checks, Portmanteau tests, and out-of-sample \(R^2\).

• Cointegration analysis: Implementation of Johansen’s test.

• Ornstein-Uhlenbeck processes: Convergence and half-life estimation based on OU processes.

• Robust inference: HC/HAC sandwich-type estimators via the ‘sandwich’ package.

• Factor preselection: Methods based on Partial Least Squares (PLS).

• Visualization: Publication-ready graphics.

• Robustness tests: Complete suite of diagnostics and validation.

Installation

From CRAN

install.packages("convergenceDFM")

Development version (GitHub)

# install.packages("devtools")
devtools::install_github("your-username/convergenceDFM")

Optional dependency: cmdstanr

For advanced Bayesian features (optional), install cmdstanr:

install.packages("cmdstanr", 
                 repos = c("https://stan-dev.r-universe.dev", 
                          getOption("repos")))

Note: cmdstanr is not on CRAN and must be installed from the Stan repository. The main functionalities of the package do not require cmdstanr.

Basic Usage

library(convergenceDFM)

# Basic example (adjust according to your main functions)
# data <- prepare_panel_data(your_data)
# dfm_result <- estimate_dfm(data)
# ou_result <- estimate_ou_process(dfm_result)
# plot_convergence(ou_result)

For more examples, see the vignettes:

browseVignettes("convergenceDFM")

Package Structure

convergenceDFM/
├── R/                  # Source code
├── data/               # Package datasets
├── inst/               # Additional package files
├── man/                # Documentation (auto-generated by roxygen2)
├── tests/              # Tests with testthat (edition 3)
├── vignettes/          # Vignettes and tutorials
├── DESCRIPTION         # Package metadata
├── LICENSE             # License file
├── NAMESPACE           # Package namespace (auto-generated)
├── NEWS.md             # Changelog
└── README.md           # This file

Development

• Documentation: Generated with roxygen2.

• Tests: Complete suite with testthat.

References

The package implements methods from:

• Forni, M., Hallin, M., Lippi, M., & Reichlin, L. (2000). “The Generalized Dynamic-Factor Model: Identification and Estimation.” Review of Economics and Statistics, 82(4), 540-554.

• Stock, J. H., & Watson, M. W. (2002). “Forecasting Using Principal Components From a Large Number of Predictors.” Journal of the American Statistical Association, 97(460), 1167-1179.

• Johansen, S. (1988). “Statistical analysis of cointegration vectors.” Journal of Economic Dynamics and Control, 12(2-3), 231-254.

• Uhlenbeck, G. E., & Ornstein, L. S. (1930). “On the Theory of the Brownian Motion.” Physical Review, 36(5), 823.

• Vasicek, O. (1977). “An equilibrium characterization of the term structure.” Journal of Financial Economics, 5(2), 177-188.

• Zeileis, A. (2004). “Econometric Computing with HC and HAC Covariance Matrix Estimators.” Journal of Statistical Software, 11(10), 1-17.

• Mevik, B.-H., & Wehrens, R. (2007). “The pls Package: Principal Component and Partial Least Squares Regression in R.” Journal of Statistical Software, 18(2), 1-23.

License

This package is free and open source software, licensed under GPL-3.

Author

José Mauricio Gómez Julián
Email: isadorenabi@pm.me

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.