convergenceDFM is an R package for convergence analysis in macro-financial panels, combining Dynamic Factor Models (DFM) with mean-reverting Ornstein-Uhlenbeck (OU) processes.
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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.
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Ornstein-Uhlenbeck processes: Convergence and half-life estimation based on OU processes.
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Robust inference: HC/HAC sandwich-type estimators via the 'sandwich' package.
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Factor preselection: Methods based on Partial Least Squares (PLS).
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Visualization: Publication-ready graphics.
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Robustness tests: Complete suite of diagnostics and validation.
install.packages("convergenceDFM")# install.packages("devtools")
devtools::install_github("your-username/convergenceDFM")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.
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")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
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Documentation: Generated with
roxygen2. -
Tests: Complete suite with
testthat.
The package implements methods from:
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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.
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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.
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Johansen, S. (1988). "Statistical analysis of cointegration vectors." Journal of Economic Dynamics and Control, 12(2-3), 231-254.
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Uhlenbeck, G. E., & Ornstein, L. S. (1930). "On the Theory of the Brownian Motion." Physical Review, 36(5), 823.
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Vasicek, O. (1977). "An equilibrium characterization of the term structure." Journal of Financial Economics, 5(2), 177-188.
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Zeileis, A. (2004). "Econometric Computing with HC and HAC Covariance Matrix Estimators." Journal of Statistical Software, 11(10), 1-17.
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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.
This package is free and open source software, licensed under GPL-3.
José Mauricio Gómez Julián
Email: isadorenabi@pm.me