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sparseDFM: An R Package to Estimate Dynamic Factor Models with Sparse Loadings

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  • Luke Mosley
  • Tak-Shing Chan
  • Alex Gibberd

Abstract

sparseDFM is an R package for the implementation of popular estimation methods for dynamic factor models (DFMs) including the novel Sparse DFM approach of Mosley et al. (2023). The Sparse DFM ameliorates interpretability issues of factor structure in classic DFMs by constraining the loading matrices to have few non-zero entries (i.e. are sparse). Mosley et al. (2023) construct an efficient expectation maximisation (EM) algorithm to enable estimation of model parameters using a regularised quasi-maximum likelihood. We provide detail on the estimation strategy in this paper and show how we implement this in a computationally efficient way. We then provide two real-data case studies to act as tutorials on how one may use the sparseDFM package. The first case study focuses on summarising the structure of a small subset of quarterly CPI (consumer price inflation) index data for the UK, while the second applies the package onto a large-scale set of monthly time series for the purpose of nowcasting nine of the main trade commodities the UK exports worldwide.

Suggested Citation

  • Luke Mosley & Tak-Shing Chan & Alex Gibberd, 2023. "sparseDFM: An R Package to Estimate Dynamic Factor Models with Sparse Loadings," Papers 2303.14125, arXiv.org.
    • Handle: RePEc:arx:papers:2303.14125
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    References listed on IDEAS

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    1. Xiao, Qiang & Cao, Honghong & He, Yongda & Oxley, Les, 2025. "State-dependent pricing of monetary policy nonlinearities and inflation at risk for China," Research in International Business and Finance, Elsevier, vol. 75(C).

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