Matteo Barigozzi, Filippo Pellegrino
arXiv 29 Jan 2023 · Econometrics · 2 citations (OpenAlex)
arXiv:2301.12499 · PDF · DOI · OpenAlex · Extracted main text
This paper generalises dynamic factor models for multidimensional dependent data. In doing so, it develops an interpretable technique to study complex information sources ranging from repeated surveys with a varying number of respondents to panels of satellite images. We specialise our results to model microeconomic data on US households jointly with macroeconomic aggregates. This results in a powerful tool able to generate localised predictions, counterfactuals and impulse response functions for individual households, accounting for traditional time-series complexities depicted in the state-space literature. The model is also compatible with the growing focus of policymakers for real-time economic analysis as it is able to process observations online, while handling missing values and asynchronous data releases.
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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 | F. Pellegrino (2023) Factor-augmented tree ensembles | 0.874 | 12 | 2 | 100% |
| 2 | M. Barigozzi and M. Luciani (2020) Quasi maximum likelihood estimation and inference of large approximate dynamic factor models via the em algorithm | 0.843 | 3 | 3 | 100% |
| 3 | D. Giannone, L. Reichlin, and D. Small (2008) Nowcasting: The real-time informational content of macroeconomic data | 0.843 | 3 | 3 | 100% |
| 4 | F. Pellegrino (2002) Selecting time-series hyperparameters with the artificial jackknife | 0.811 | 4 | 2 | 100% |
| 5 | M. Bańbura and M. Modugno (2014) Maximum likelihood estimation of factor models on datasets with arbitrary pattern of missing data | 0.737 | 3 | 2 | 100% |
| 6 | J. Bai and P. Wang (2015) Identification and bayesian estimation of dynamic factor models | 0.644 | 2 | 2 | 100% |
| 7 | T. Hasenzagl, F. Pellegrino, L. Reichlin, and G. Ricco (2022) A model of the fed's view on inflation | 0.644 | 2 | 2 | 100% |
| 8 | T. Hasenzagl, F. Pellegrino, L. Reichlin, and G. Ricco (2022) Monitoring the economy in real time: Trends and gaps in real activity and prices | 0.644 | 2 | 2 | 100% |
| 9 | X.-L. Meng and D. B. Rubin (1993) Maximum likelihood estimation via the ecm algorithm: A general framework | 0.644 | 2 | 2 | 100% |
| 10 | R. H. Shumway and D. S. Stoffer (1982) An approach to time series smoothing and forecasting using the em algorithm | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 28 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Quasi Maximum Likelihood Estimation of High-Dimensional Factor Models: A Critical Review | 0.405 | 1 | 1 |