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https://github.com/SebKrantz/dfms

Dynamic Factor Models for R
https://github.com/SebKrantz/dfms

dynamic-factor-models rstats time-series

Last synced: about 1 month ago
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Dynamic Factor Models for R

Host: GitHub
URL: https://github.com/SebKrantz/dfms
Owner: SebKrantz
License: gpl-3.0
Created: 2021-08-25T09:04:15.000Z (about 3 years ago)
Default Branch: main
Last Pushed: 2024-06-09T16:27:08.000Z (3 months ago)
Last Synced: 2024-06-10T13:50:52.494Z (3 months ago)
Topics: dynamic-factor-models, rstats, time-series
Language: R
Homepage: https://sebkrantz.github.io/dfms
Size: 16.4 MB
Stars: 27
Watchers: 3
Forks: 8
Open Issues: 0
Metadata Files:
- Readme: README.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Codemeta: codemeta.json

Awesome Lists containing this project

jimsghstars - SebKrantz/dfms - Dynamic Factor Models for R (R)

README

        # **dfms**: Dynamic Factor Models for R

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*dfms* provides efficient estimation of Dynamic Factor Models via the EM Algorithm. Estimation can be done in 3 different ways following:

- 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. 

The default is `em.method = "auto"`, which chooses `"DGR"` following Doz, Giannone & Reichlin (2012) if there are no missing values in the data, and `"BM"` following Banbura & Modugno (2014) with missing data. Using `em.method = "none"` generates Two-Step estimates following Doz, Giannone & Reichlin (2011). This is extremely efficient on bigger datasets. PCA and Two-Step estimates are also reported in EM-based methods. 

All 3 estimation methods support missing data, with various preprocessing options, but `em.method = "DGR"` does not account for them in the EM iterations, and should only be used if a few values are missing at random. For all other cases `em.method = "BM"` or `em.method = "none"` is the way to go. 

### Comparison with Other R Packages

*dfms* is intended to provide a simple, numerically robust, and computationally efficient baseline implementation of (linear Gaussian) Dynamic Factor Models for R, allowing straightforward application to various contexts such as time series dimensionality reduction and multivariate forecasting. The implementation is based on efficient C++ code, making *dfms* orders of magnitude faster than packages such as [*MARSS*]() that can be used to fit dynamic factor models, or packages like [*nowcasting*]() and [*nowcastDFM*](), which fit dynamic factor models specific to mixed-frequency nowcasting applications. The latter two packages additionally support blocking of variables into different groups for which factors are to be estimated, and EM adjustments for variables at different frequencies. 

The package is currently not intended to fit more general forms of the state space model such as provided by [*MARSS*]().

### Installation 

```r

# CRAN

install.packages("dfms")

# Development Version

install.packages('dfms', repos = c('https://sebkrantz.r-universe.dev', 'https://cloud.r-project.org'))

```

### Usage Example 

```r

library(dfms)

# Fit DFM with 6 factors and 3 lags in the transition equation

mod = DFM(diff(BM14_M), r = 6, p = 3) 

# 'dfm' methods

summary(mod)

plot(mod)

as.data.frame(mod)

# Forecasting 20 periods ahead

fc = predict(mod, h = 20)

# 'dfm_forecast' methods

print(fc)

plot(fc)

as.data.frame(fc)

```