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Structural Models for Text and other Unstructured Data

Project description

New probabilistic models could help manage unstructured data

The amount of data generated each day is overwhelming. Most of it is unstructured, meaning that it cannot easily be stored in databases. It is therefore more difficult to analyse and is not searchable. Common examples of unstructured data include text files, transaction data, images and web browsing histories. The EU-funded UnStruct project plans to build novel probabilistic models that link unstructured data directly to relevant economic parameters. Studies will focus on three main themes: how information about economic conditions is dispersed amongst agents, and how they aggregate it through interactions; the evolution of an economy hit by multiple shocks; and transaction payments between firms.

Objective

Most usable data is unstructured. Examples include text, transaction data, images, and web browsing histories. Although rich and plentiful, most economists do not use unstructured data. The few that do generally quantify it with off-the-shelf algorithms that are unrelated to the economic environment in which it is generated, which makes connecting it to economic models difficult. I instead propose to build novel probabilistic models of unstructured data that link it directly to relevant economic parameters. This powerful approach will use the information in unstructured data to test and estimate economic models in a way that is not currently possible with existing methods.

I will focus on three distinct themes. The first studies how information about economic conditions is dispersed among agents, and how they aggregate it through interactions. This process it at the heart of the policymaking process, and the use of text data provides a unique opportunity to structurally model this information in innovative ways.

The second theme jointly models unstructured data and the evolution of an economy hit by multiple, unobserved shocks. This will provide a novel forecasting tool, which is of key interest to policymakers. But it will also use unstructured data to estimate equilibrium models of the macroeconomy, and hence recover economic fundamentals.

The final theme will use transaction payments between firms, and extend probabilistic models of network formation to create new definitions of markets that go well beyond anything in the current literature. This will contribute to measuring market power and the transmission of economic shocks, both questions of fundamental importance.

Beyond these specific themes, my research will also pave the way for the use of probabilistic machine learning that combines novel data with clear economic models. The frameworks I introduce will provide a template for others to follow in the future.

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

ERC-COG - Consolidator Grant

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) ERC-2019-COG

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Host institution

UNIVERSITY COLLEGE LONDON
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 889 090,74
Address
GOWER STREET
WC1E 6BT LONDON
United Kingdom

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Region
London Inner London — West Camden and City of London
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 889 090,74

Beneficiaries (3)

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