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Can less be more?: Semiparametric and partial identification in panel data discrete response models with an application to consumer demand

Project description

Innovative panel data models of consumer demand

Traditional panel data demand models that provide information on individual behaviour frequently use distributional and functional form assumptions for identification and estimation. When not specified, these models shift towards interpretation and inference problems, which might negatively impact policymaking. Semi-parametric models make fewer assumptions and can be applied to different settings and data sets but can lose identification power and only partially identify the parameters of interest. The EU-funded PartialIO project will develop innovative econometric panel data models of consumer demand investigating the determinants affecting consumer decisions. The project will study the role of inertia, habits and lock-in in choices and the dynamics in decisions.

Objective

In this project novel econometric panel data models of consumer demand will be developed. In particular, the determinants affecting individuals’ decisions will be examined when individuals are observed making decisions over time using new and less restrictive models. The new models will examine the role of inertia, habits and lock-in in choices and the importance of dynamics in decisions, when consumers choose from a set of alternatives offered in different quality levels. The methodologies in this project will allow the examination of substitution patterns not examined before.

Traditional panel data demand models often use distributional and functional form assumptions for identification and estimation. If misspecified, these models can lead to interpretation and inference problems, jeopardizing the success and effectiveness in the policy decision-making processes of firms and governments. Semiparametric models that make fewer assumptions have increased credibility, however, can lose identification power hence partially identifying the parameters of interest. Such models can be applied to many different settings and datasets and do not rely on unfounded assumptions, making it clear what can be tested and what conclusions can be drawn from the analysis. This directly relates to one of the seven priority challenges identified by the EU as part of the H2020, related to health, demographic change and wellbeing, since accurate representation of individuals’ decision process has a direct effect on the welfare policies designed.

This project will lead to publication outcomes in econometric theory and applied microeconometrics in terms of novel methodologies. These methodologies will form the basis for a broad spectrum of theoretical and applied research in econometrics and microeconomics; discrete response data is found in many applications including marketing, industrial organization, health and labour economics, as well as help economic decision and policy making.

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MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)

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Call for proposal

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(opens in new window) H2020-MSCA-IF-2020

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Coordinator

UNIVERSITY OF CYPRUS
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.

€ 145 941,12
Address
AVENUE PANEPISTIMIOU 2109 AGLANTZI
1678 Nicosia
Cyprus

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Region
Κύπρος Κύπρος Κύπρος
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.

€ 145 941,12
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