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Efficient Dynamic Factor Models for R? · Issue #555 · ropensci/software-review · GitHub

Submitting Author Name: Sebastian Krantz
Submitting Author Github Handle: @SebKrantz
Other Package Authors Github handles: @rbagd
Repository: https://github.com/SebKrantz/dfms
Submission type: Pre-submission
Language: en

• Paste the full DESCRIPTION file inside a code block below:

Package: dfms
Version: 0.1.1
Title: Dynamic Factor Models
Authors@R: c(person("Sebastian", "Krantz", role = c("aut", "cre"), email = "sebastian.krantz@graduateinstitute.ch"),
             person("Rytis", "Bagdziunas", role = "aut"))
Description: Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm 
  or Two-Step (2S) estimation, on datasets with missing data. The implementation follows advances in the econometric 
  literature: estimation can be done either by running the Kalman Filter and Smoother once with initial values 
  from PCA - following Doz, Giannone and Reichlin (2011) (2S) - or via iterated Kalman Filtering and Smoothing until EM 
  convergence - following Doz, Giannone and Reichlin (2012) - or using the adapted EM algorithm of Banbura and Modugno 
  (2014), allowing estimation with arbitrary patterns of missing data. The implementation makes heavy use of the 
  Armadillo C++ library and the collapse package, providing for particularly speedy estimation. A comprehensive set of 
  methods supports interpretation/visualization of the model and forecasting. Information criteria to choose the number 
  of factors are also provided - following Bai and Ng (2002).
  --- Key References: ---
  Doz, C., Giannone, D., & Reichlin, L. (2011). A two-step estimator for large approximate dynamic 
       factor models based on Kalman filtering. Journal of Econometrics, 164(1), 188-205.
  Doz, C., Giannone, D., & Reichlin, L. (2012). A quasi-maximum likelihood approach for large, approximate 
       dynamic factor models. Review of economics and statistics, 94(4), 1014-1024.
  Banbura, M., & Modugno, M. (2014). Maximum likelihood estimation of factor models on datasets with arbitrary 
       pattern of missing data. Journal of Applied Econometrics, 29(1), 133-160.
URL: https://sebkrantz.github.io/dfms/
BugReports: https://github.com/SebKrantz/dfms/issues
Depends: R (>= 3.0.0)
Imports: Rcpp (>= 1.0.1), collapse (>= 1.8.0)
LinkingTo: Rcpp, RcppArmadillo
Suggests: xts, vars, magrittr, testthat (>= 3.0.0), covr
License: GPL-3
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE, roclets = c ("namespace", "rd", "srr::srr_stats_roclet"))
RoxygenNote: 7.1.2
Config/testthat/edition: 3

• Please indicate which category or categories from our package fit policies or statistical package categories this package falls under. (Please check an appropriate box below):

  Data Lifecycle Packages

  • data retrieval
  • data extraction
  • data munging
  • data deposition
  • data validation and testing
  • workflow automation
  • version control
  • citation management and bibliometrics
  • scientific software wrappers
  • field and lab reproducibility tools
  • database software bindings
  • geospatial data
  • text analysis

  Statistical Packages

  • Bayesian and Monte Carlo Routines
  • Dimensionality Reduction, Clustering, and Unsupervised Learning
  • Machine Learning
  • Regression and Supervised Learning
  • Exploratory Data Analysis (EDA) and Summary Statistics
  • Spatial Analyses
  • Time Series Analyses

• Explain how and why the package falls under these categories (briefly, 1-2 sentences). Please note any areas you are unsure of:

Dynamic factor models are a time series modelling and dimensionality reduction technique.

• If submitting a statistical package, have you already incorporated documentation of standards into your code via the srr package?

I'm working on this.

• Who is the target audience and what are scientific applications of this package?

Anybody working with time series. The package is useful for dimensionality reduction and forecasting with a large amount of time series.

• Are there other R packages that accomplish the same thing? If so, how does yours differ or meet our criteria for best-in-category?

See README.md. In short: dfms is the much faster, provides multiple estimation methods, and has a comprehensive set of methods for exploring the model and forecasting. It is less specialized than economic nowcasting packages.

• (If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research?

No Applicable.

• Any other questions or issues we should be aware of?:

First, I would like to ask if you think you'll be able to review this package in a statistical sense. Then, I will likely not be able to comply with all of your standards, as I intend to export some C++ level helper function (mainly efficient Kalman Filtering and Smoothing functions) without any checks on the inputs. My hope here is in part to provide infrastructure that more specialized software (such as nowcasting packages) can take advantage of. The iterative filtering and smoothing performed in the estimation of dynamic factor models via expectation maximization (EM) algorithms does not square well with R-level checks in those functions (which would be executed many times per second).

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