TwoStepSDFM

A C++-based R implementation of the two-step estimation procedure for a (linear Gaussian) Sparse Dynamic Factor Model (SDFM) as outlined in Franjic and Schweikert (2024).

Introduction

The TwoStepSDFM package provides a fast implementation of the Kalman Filter and Smoother (hereinafter KFS, see Koopman and Durbin, 2000) to estimate factors in a mixed-frequency SDFM framework, explicitly accounting for cross-sectional correlation in the measurement error. The KFS is initialized using results from Sparse Principal Components Analysis (SPCA) by Zou and Hastie (2006) in a preliminary step. This approach generalizes the two-step estimator for approximate dynamic factor models by Giannone, Reichlin, and Small (2008) and Doz, Giannone, and Reichlin (2011). For more details see Franjic and Schweikert (2024).

Main Features

• Fast Model Simulation: The simFM() function provides a flexible framework to simulate mixed-frequency data with ragged edges from an approximate DFM.
• Estimation of the Number of Factors: The noOfFactors() function uses the Onatski (2009) procedure to estimate the number of factors efficiently while providing good finite sample performance.
• Fast Model Estimation: The twoStepSDFM() function provides a fast, memory-efficient, and convenient implementation of the two-step estimator outlined in Franjic and Schweikert (2024).
• Fast Hyper-Parameter Cross-Validation: The crossVal() function provides a fast and parallel cross-validation wrapper to retrieve the optimal hyper-parameters using time-series cross-validation (Hyndman and Athanasopoulos 2018) with random hyper-parameter search (Bergstra and Bengio 2012).
• Fast Model Prediction: The nowcast() function is a highly convenient prediction function for backcasts, nowcasts, and forecasts of multiple targets. It automatically takes care of all issues arising with mixed-frequency data and ragged edges.
• Compatibility: All functions take advantage of C++ for enhanced speed and memory-efficiency.

Side Features

• Fast dense DFM estimation and prediction: The nowcast() function is also able to produce predictions of a dense DFM according to Giannone, Reichlin, and Small (2008). The function twoStepDenseDFM() additionally exposes an estimation procedure for the dense two-step estimator.
• Fast SPCA: sparsePCA() exposes the internal C++-backed SPCA routine in R. This provides access to a fast and memory-efficient SPCA estimation routine as implemented by Zou and Hastie (2020) in pure R.
• Fast Kalman Filter and Smoother: The kalmanFilterSmoother() function exposes the internal C++-backed KFS routine.

Prerequisites

• Rcpp: A package for integrating C++ code into R (Eddelbuettel and François, 2011). Rcpp CRAN repository
• RcppEigen: A package for integrating the Eigen linear algebra library into R (Bates and Eddelbuettel, 2013). RcppEigen CRAN repository
• GCC compiler (version 5.0 or later) GCC Website.

Installation

Compile from scratch

Rcpp and RcppEigen can be downloaded from CRAN or directly installed from within R by calling install.packages("...").

To install the package itself, a short R script is provided (see PackageBuilder.R). The package currently only compiles with the g++/gcc compiler.

Usage

For a quick step-by-step user guide of the main features, see the package vignette.

License

License: GPL v3

(C) 2024-2026 Domenic Franjic

This project is licensed under the GNU General Public License v3.0. See the LICENSE file for details.

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.

To Contribute:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Commit your changes with descriptive messages.
4. Push to your fork and submit a pull request.

Support

If you have any questions or need assistance, please open an issue on the GitHub repository or contact us via email.

Contact

• Name: Domenic Franjic
• Institution: University of Hohenheim
• Department: Econometrics and Statistics, Core Facility Hohenheim
• E-Mail: franjic@uni-hohenheim.de

References

Papers

• Bergstra, James, and Yoshua Bengio (2012). Random Search for Hyper-Parameter Optimization. Journal of Machine Learning Research, 13(2).
• Doz, Catherine, Domenico Giannone, and Lucrezia Reichlin (2011). A two-step estimator for large approximate dynamic factor models based on Kalman filtering. Journal of Econometrics, 164(1), 188–205.
• Franjic, Domenic, and Karsten Schweikert (2024). Nowcasting Macroeconomic Variables with a Sparse Mixed Frequency Dynamic Factor Model. SSRN 4733872.
• Giannone, Domenico, Lucrezia Reichlin, and David Small (2008). Nowcasting: The Real-Time Informational Content of Macroeconomic Data. Journal of Monetary Economics, 55(4), 665–76.
• Koopman, Siem Jan, and James Durbin (2000). Fast Filtering and Smoothing for Multivariate State Space Models. Journal of Time Series Analysis, 21(3), 281–96.
• Marcellino, Massimiliano, and Christian Schumacher (2010). Factor MIDAS for nowcasting and forecasting with ragged-edge data: A model comparison for German GDP. Oxford Bulletin of Economics and Statistics, 72(4), 518–50.
• Mariano, Roberto S., and Yasutomo Murasawa (2003). A New Coincident Index of Business Cycles Based on Monthly and Quarterly Series. Journal of Applied Econometrics, 18(4), 427–43.
• Onatski, Alexei (2009). Testing Hypotheses about the Number of Factors in Large Factor Models. Econometrica, 77(5), 1447–79.
• Zou, Hui, Trevor Hastie, and Robert Tibshirani (2006). Sparse Principal Component Analysis. Journal of Computational and Graphical Statistics, 15(2), 265–86.

Books

• Hyndman, Rob J., and George Athanasopoulos (2018). Forecasting: Principles and Practice (3rd ed.). OTexts Melbourne.

Software / Packages

• Zou, Hui, and Trevor Hastie (2020). Elasticnet: Elastic-Net for Sparse Estimation and Sparse PCA (R package)