PartialIO · Can less be more?: Semiparametric and partial identification in panel data discrete response models with an application to consumer demand
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2021-09-01 → 2025-07-07
- EU contribution
- €145,941
- Participants
- 1
- Scheme
- MSCA-IF
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Results in brief
Can less be more?: Semiparametric and partial identification in panel data discrete response models with an application to consumer demand
In many settings, individuals are observed making repeated discrete choices, for example, whether to work or not in a specific year, or which brand of cereals to buy on each shopping trip. Furthermore, it is often noted that these choices are intertemporally correlated. Traditional panel data discrete response models often use distributional and functional form assumptions for identification and estimation. If misspecified, these models can lead to interpretation and inference problems. Developing models that are flexible is thus very important since they can be applied to many different settings and datasets, making clear what can be tested and what conclusions can be drawn from the analysis. Getting a better understanding of how individuals make decisions is particularly important for society since this affects the success and effectiveness of the economic and policy decisions of firms and governments. PartialIO’s overall objectives were the development and examination of new and less restrictive dynamic panel data discrete response models. These models would be applicable to consumer demand, capturing for example the role of inertia, habits and lock-in in choices. The project developed a new model where individuals base their decision on whether they switch from the option they chose last period, without imposing assumptions on the unobserved individual heterogeneity. The identification power of this model was examined, and although the model did not provide a single solution for the parameters of interest, identification bounds were derived under different scenarios.
Data: CORDIS, © European Union
Project 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.
Original text from CORDIS.
Participants
- UNIVERSITY OF CYPRUS · NicosiaCoordinatorCyprus
Links
Data: CORDIS, © European Union