Luke Mosley, Tak-Shing Chan, Alex Gibberd
arXiv 23 Mar 2023 · Statistics — Computation · 4 citations (OpenAlex)
arXiv:2303.14125 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bańbura, M. and M. Modugno (2014) Maximum likelihood estimation of factor models on datasets with arbitrary pattern of missing data | 0.933 | 16 | 4 | 81% |
| 2 | Mosley, L., T.-S. Chan, and A. Gibberd (2023) The sparse dynamic factor model: A regularised quasi-maximum likelihood approach self | 0.874 | 13 | 2 | 100% |
| 3 | Giannone, D., L. Reichlin, and D. Small (2008) Nowcasting: The real-time informational content of macroeconomic data | 0.874 | 8 | 2 | 100% |
| 4 | Stock, J. H. and M. W. Watson (2002) Forecasting using principal components from a large number of predictors | 0.874 | 6 | 2 | 100% |
| 5 | Koopman, S. J. and J. Durbin (2000) Fast filtering and smoothing for multivariate state space models | 0.814 | 13 | 3 | 54% |
| 6 | Shumway, R. H. and D. S. Stoffer (1982) An approach to time series smoothing and forecasting using the em algorithm | 0.794 | 12 | 3 | 50% |
| 7 | Banbura, M., D. Giannone, and L. Reichlin (2010) Nowcasting | 0.737 | 3 | 2 | 100% |
| 8 | Doz, C., D. Giannone, and L. Reichlin (2011) A two-step estimator for large approximate dynamic factor models based on kalman filtering | 0.737 | 3 | 2 | 100% |
| 9 | Bai, J. and S. Ng (2002) Determining the number of factors in approximate factor models | 0.693 | 7 | 1 | 100% |
| 10 | Doz, C., D. Giannone, and L. Reichlin (2012) A quasi-maximum likelihood approach for large, approximate dynamic factor models | 0.585 | 3 | 1 | 100% |
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