Data Assimilation for Parameter Estimation in Economic Modelling

Nadler, Philip; Arcucci, Rossella; Guo, Yike

doi:10.1109/sitis.2019.00106

Cited by 11 publications

(8 citation statements)

References 13 publications

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“…We minimise the cost function in (7) so as to incorporate observations, y and exogenous input X, to infer latent parameter β. Further details can be found in previous work by Nadler et al [3]. In (7), following notations are used:…”

Section: Our Proposed Tvp-var-vi: Variational Inferencementioning

confidence: 99%

“…This granular dataset, in combination with our model, provides a data-driven approach to inform policymakers and can provide valuable information when creating new regulations to protect investors in this yet largely unregulated market. Previous works have successfully modelled cryptocurrencies as a dynamical system using Time-Varying Parameter -Vector Autoregression (TVP-VAR) [2,3]. They note that a time-varying regression coefficient could ideally model this ecosystem.…”

Section: Introductionmentioning

confidence: 99%

“…They note that a time-varying regression coefficient could ideally model this ecosystem. As noted by Nadler et al [3] and Haldane [4], the economic model needs to scale to deal with big data appropriately. We build on the previous research and improve the Bayesian inference in a way that is scalable for big data.…”

Section: Introductionmentioning

confidence: 99%

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A Scalable Inference Method For Large Dynamic Economic Systems

Khandelwal¹,

Nadler²,

Arcucci³

et al. 2021

Preprint

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The nature of available economic data has changed fundamentally in the last decade due to the economy's digitisation. With the prevalence of often black box datadriven machine learning methods, there is a necessity to develop interpretable machine learning methods that can conduct econometric inference, helping policymakers leverage the new nature of economic data. We therefore present a novel Variational Bayesian Inference approach to incorporate a time-varying parameter auto-regressive model which is scalable for big data. Our model is applied to a large blockchain dataset containing prices, transactions of individual actors, analyzing transactional flows and price movements on a very granular level. The model is extendable to any dataset which can be modelled as a dynamical system. We further improve the simple state-space modelling by introducing non-linearities in the forward model with the help of machine learning architectures.

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Section: Our Proposed Tvp-var-vi: Variational Inferencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Scalable Inference Method For Large Dynamic Economic Systems

Khandelwal¹,

Nadler²,

Arcucci³

et al. 2021

Preprint

Self Cite

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“…In this paper, by investigating how the accuracy of network data could impact vaccination effectiveness, we propose a real-time network updating approach based on sequential data assimilation (DA) techniques. Originally developed in the field of meteorological and environmental science, DA has been applied to a wide variety of industrial domains, including geophysical modelling [10], hydrology [14] and economics [49]. Recently, sequential DA algorithms have also been used for real-time parameter identification in the SIR model for COVID spread simulation [61,50,22].…”

Section: Introductionmentioning

confidence: 99%

Real-time Updating of Dynamic Social Networks for COVID-19 Vaccination Strategies

Cheng

Arcucci

Pain

et al. 2021

Preprint

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Vaccination strategy is crucial in fighting against the COVID-19 pandemic. Since the supply is limited, contact network-based interventions can be most powerful to set an optimal strategy by identifying high-risk individuals or communities. However, due to the high dimension, only partial and noisy network information can be available in practice, especially for dynamical systems where the contact networks are highly time-variant. Furthermore, numerous mutations of SARS-CoV-2 impact considerably the current infectious probability, requiring real-time network updating algorithms. In this study, we propose a sequential network updating approach based on data assimilation techniques to combine different sources of temporal information. We then prioritise the individuals with high-degree or high-centrality, obtained from the assimilated networks, for vaccination. The assimilation-based approach is compared with the standard method (based on partially observed networks) and a random selection strategy in terms of vaccination effectiveness in a SIR model. The numerical comparison is first carried out using real-world face-to-face dynamical networks collected in a high school, following by sequential multi-layer networks, generated relying on the Barabasi-Albert model emulating the department of Computing at Imperial College London in the UK as an example.

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“…DA algorithms are often used in dynamical systems for continuously updating state estimation/prediction. They have recently made their way to other fields such as biomedical applications [5] or quantitative economics [6]. These methods rely on a weighted combination of different sources of noisy information, including prior numerical estimation (also known as background states) and real-time observations, to improve field reconstruction or parameters calibration.…”

Section: Introductionmentioning

confidence: 99%

Observation data compression for variational assimilation of dynamical systems

Cheng

Lucor

Argaud³

2021

Preprint

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Accurate estimation of error covariances (both background and observation) is crucial for efficient observation compression approaches in data assimilation of large-scale dynamical problems. We propose a new combination of a covariance tuning algorithm with existing PCA-type data compression approaches, either observation-or information-based, with the aim of reducing the computational cost of real-time updating at each assimilation step. Relying on a local assumption of flow-independent error covariances, dynamical assimilation residuals are used to adjust the covariance in each assimilation window. The estimated covariances then contribute to better specify the principal components of either the observation dynamics or the state-observation sensitivity. The proposed approaches are first validated on a shallow water twin experiment with correlated and non-homogeneous observation error. Proper selection of flow-independent assimilation windows, together with sampling density for background error estimation, and sensitivity of the approaches to the observations error covariance knowledge, are also discussed and illustrated with various numerical tests and results. The method is then applied to a more challenging industrial hydrological model with real-world data and non-linear transformation operator provided by an operational precipitation-flow simulation software.

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Data Assimilation for Parameter Estimation in Economic Modelling

Cited by 11 publications

References 13 publications

A Scalable Inference Method For Large Dynamic Economic Systems

A Scalable Inference Method For Large Dynamic Economic Systems

Real-time Updating of Dynamic Social Networks for COVID-19 Vaccination Strategies

Observation data compression for variational assimilation of dynamical systems

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