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MEImpact

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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... 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. ver más
30/09/2025
LMU MUENCHEN
1M€
Duración del proyecto: 72 meses Fecha Inicio: 2019-09-26
Fecha Fin: 2025-09-30

Línea de financiación: concedida

El organismo H2020 notifico la concesión del proyecto el día 2019-09-26
Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:
ERC-2019-STG: ERC Starting Grant
Cerrada hace 6 años
Presupuesto El presupuesto total del proyecto asciende a 1M€
Líder del proyecto
LUDWIGMAXIMILIANSUNIVERSITAET MUENCHEN No se ha especificado una descripción o un objeto social para esta compañía.
Perfil tecnológico TRL 4-5