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

Programme(s): H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
Topic(s): MSCA-IF-2020 - Individual Fellowships
Call for proposal: H2020-MSCA-IF-2020
Funding Scheme: MSCA-IF-EF-ST - Standard EF

Grant agreement ID: 101028470

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.