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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2411.02029 |
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| _version_ | 1866909377021083648 |
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| author | Mantziou, Anastasia Hotte, Kerstin Cucuringu, Mihai Reinert, Gesine |
| author_facet | Mantziou, Anastasia Hotte, Kerstin Cucuringu, Mihai Reinert, Gesine |
| contents | Real-time economic information is essential for policy-making but difficult to obtain. We introduce a granular nowcasting method for macro- and industry-level GDP using a network approach and data on real-time monthly inter-industry payments in the UK. To this purpose we devise a model which we call an extended generalised network autoregressive (GNAR-ex) model, tailored for networks with time-varying edge weights and nodal time series, that exploits the notion of neighbouring nodes and neighbouring edges. The performance of the model is illustrated on a range of synthetic data experiments. We implement the GNAR-ex model on the payments network including time series information of GDP and payment amounts. To obtain robustness against statistical revisions, we optimise the model over 9 quarterly releases of GDP data from the UK Office for National Statistics. Our GNAR-ex model can outperform baseline autoregressive benchmark models, leading to a reduced forecasting error. This work helps to obtain timely GDP estimates at the aggregate and industry level derived from alternative data sources compared to existing, mostly survey-based, methods. Thus, this paper contributes both, a novel model for networks with nodal time series and time-varying edge weights, and the first network-based approach for GDP nowcasting based on payments data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_02029 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GDP nowcasting with large-scale inter-industry payment data in real time -- A network approach Mantziou, Anastasia Hotte, Kerstin Cucuringu, Mihai Reinert, Gesine Applications Real-time economic information is essential for policy-making but difficult to obtain. We introduce a granular nowcasting method for macro- and industry-level GDP using a network approach and data on real-time monthly inter-industry payments in the UK. To this purpose we devise a model which we call an extended generalised network autoregressive (GNAR-ex) model, tailored for networks with time-varying edge weights and nodal time series, that exploits the notion of neighbouring nodes and neighbouring edges. The performance of the model is illustrated on a range of synthetic data experiments. We implement the GNAR-ex model on the payments network including time series information of GDP and payment amounts. To obtain robustness against statistical revisions, we optimise the model over 9 quarterly releases of GDP data from the UK Office for National Statistics. Our GNAR-ex model can outperform baseline autoregressive benchmark models, leading to a reduced forecasting error. This work helps to obtain timely GDP estimates at the aggregate and industry level derived from alternative data sources compared to existing, mostly survey-based, methods. Thus, this paper contributes both, a novel model for networks with nodal time series and time-varying edge weights, and the first network-based approach for GDP nowcasting based on payments data. |
| title | GDP nowcasting with large-scale inter-industry payment data in real time -- A network approach |
| topic | Applications |
| url | https://arxiv.org/abs/2411.02029 |