Luke Mosley, Idris Eckley, Alex Gibberd
arXiv 12 Aug 2021 · Econometrics · publishedJournal of the Royal Statistical Society Series A (Statistics in Society) (2022) · 11 citations (OpenAlex)
arXiv:2108.05783 · PDF · DOI · OpenAlex · Extracted main text
Temporal disaggregation is a method commonly used in official statistics to enable high-frequency estimates of key economic indicators, such as GDP. Traditionally, such methods have relied on only a couple of high-frequency indicator series to produce estimates. However, the prevalence of large, and increasing, volumes of administrative and alternative data-sources motivates the need for such methods to be adapted for high-dimensional settings. In this article, we propose a novel sparse temporal-disaggregation procedure and contrast this with the classical Chow-Lin method. We demonstrate the performance of our proposed method through simulation study, highlighting various advantages realised. We also explore its application to disaggregation of UK gross domestic product data, demonstrating the method's ability to operate when the number of potential indicators is greater than the number of low-frequency observations.
appendix boundary found by appendix_titled_section at “Appendix” · 94% of the source is main text. Read the extracted text to check this.
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 | Chow, G. C. and Lin, A.-l (1971) Best linear unbiased interpolation, distribution, and extrapolation of time series by related series | 1.000 | 13 | 5 | 100% |
| 2 | Bühlmann, P. and Van De Geer, S (2011) Statistics for high-dimensional data: methods, theory and applications | 1.000 | 6 | 3 | 100% |
| 3 | Tibshirani, R (1996) Regression shrinkage and selection via the lasso | 0.928 | 4 | 3 | 100% |
| 4 | Ciammola, A., Di Palma, F., and Marini, M (2005) Temporal disaggregation techniques of time series by related series: A comparison by a monte carlo experiment | 0.843 | 3 | 3 | 100% |
| 5 | Efron, B., Hastie, T., Johnstone, I., Tibshirani, R., et al (2004) Least angle regression | 0.811 | 4 | 2 | 100% |
| 6 | Sax, C. and Steiner, P (2013) Temporal disaggregation of time series | 0.737 | 3 | 2 | 100% |
| 7 | Zou, H (2006) The adaptive lasso and its oracle properties | 0.693 | 5 | 1 | 100% |
| 8 | Chen, B (2007) An empirical comparison of methods for temporal disaggregation at the national accounts | 0.644 | 2 | 2 | 100% |
| 9 | Eurostat (2018) European Statistical System (ESS) guidelines on temporal disaggregation, benchmarking and reconciliation | 0.644 | 2 | 2 | 100% |
| 10 | Proietti, T (2006) Temporal disaggregation by state space methods: Dynamic regression methods revisited | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 58 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Nowcasting R&D Expenditures: A Machine Learning Approach | 0.874 | 15 | 2 |
| 2 | Reconstructing Subnational Labor Indicators in Colombia: An Integrated Machine and Deep Learning Approach | 0.405 | 1 | 1 |