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The Consequences of Mismeasuring Economic Activity

Measuring economic activity is a fundamental challenge for empirical work in economics. Most empirical projects raise concerns about whether the data do in fact measure what they purport to measure. Mismeasurement may lead to seve... see more

30/09/2025

LMU MUENCHEN

1M€

Project Budget: 1M€

Project leader

LUDWIGMAXIMILIANSUNIVERSITAET MUENCHEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

PARTICIPATION DEPralty Sin fecha límite de participación.

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Financing granted El organismo H2020 notifico la concesión del proyecto The day 2019-09-26

ERC-2019-STG: ERC Starting Grant Scope:Objectives

I+D

Cerrada does 6 years

0% 100% 100%

characteristics of the participant

Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

Private Information

No hay información privada compartida para este proyecto. Habla con el coordinador.

3 Participantes

LMU MUENCHEN

768.13K€ | Leader

SAPLEX

149.96K€ | participant

UNIVERSITY COLLEGE LONDON

568.89K€ | participant

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Project duration: 72 months Date Start: 2019-09-26
End date: 2025-09-30

Project leader

LUDWIGMAXIMILIANSUNIVERSITAET MUENCHEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Project Budget 1M€

participation deadline Sin fecha límite de participación.

Project description Measuring economic activity is a fundamental challenge for empirical work in economics. Most empirical projects raise concerns about whether the data do in fact measure what they purport to measure. Mismeasurement may lead to severe model misspecification, biased estimates, and misled conclusions and policy decisions. Unfortunately, formally accounting for the possibility of mismeasurement in the econometric model is complicated and possible only under strong assumptions that limit the credibility of resulting conclusions. Therefore, the most common approaches to measurement issues are to ignore them, to informally argue why they may not be of first-order importance, to abandon the project, or to search for better data. The objective of the research described in this proposal is to develop new methodologies for formally assessing the potential impact of measurement error (ME) on all aspects of an empirical project: on model-building, on estimation and inference, and on decision-making. For instance, the new inference procedures allow the researcher to test whether ME is a statistically significant feature that should be modeled, whether ME distorts objects of interest (e.g. a production or utility function), whether ME distorts conclusions from hypothesis tests, and whether ME affects subsequent decision-making. I show that answering such questions is possible under much weaker assumptions than identification and estimation of a ME model and thus leads to more credible and robust conclusions. In addition, the implementation of the new procedures can be based on standard nonparametric estimation techniques that are part of many applied researchers’ toolkits. In consequence, the research has the potential to fundamentally transform the way empirical researchers approach measurement issues, to significantly impact empirical practice, and ultimately to avoid misled conclusions and policy decisions.

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