Imperial College London
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DrRossellaArcucci

Faculty of Engineering, Department of Earth Science & Engineering

Senior Lecturer in Data Science and Machine Learning
 
 
 
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Contact

 

r.arcucci Website

 
 
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Location

 

Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Nadler:2019:10.1109/SITIS.2019.00106,
author = {Nadler, P and Arcucci, R and Guo, YK},
doi = {10.1109/SITIS.2019.00106},
pages = {649--656},
title = {Data assimilation for parameter estimation in economic modelling},
url = {http://dx.doi.org/10.1109/SITIS.2019.00106},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - We propose a data assimilation approach for latent parameter estimation in economic models. We describe a dynamic model of an economic system with latent state variables describing the relationship of economic entities over time as well as a stochastic volatility component. We show and discuss the model's relationship with data assimilation and how it is derived. We apply it to conduct a multivariate analysis of the cryptocurrency ecosystem. Combining these approaches opens a new dimension of analysis to economic modelling. Economics, Multivariate Analysis, Dynamical System, Bitcoin, Data Assimilation.
AU  - Nadler,P
AU  - Arcucci,R
AU  - Guo,YK
DO  - 10.1109/SITIS.2019.00106
EP  - 656
PY  - 2019///
SP  - 649
TI  - Data assimilation for parameter estimation in economic modelling
UR  - http://dx.doi.org/10.1109/SITIS.2019.00106
ER  -
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