Add to ORKGEmpirical papers in economics often describe heuristically how their estimates depend on in-tuitive features of the data. We propose two quantitative measures of this relationship that can be computed at negligible cost even for complex models. We show that our measures can be informative about robustness to model misspecification, and can complement the discussions of identification that have become common in applied work. We illustrate our measures with applications to industrial organization, macroeconomics, public economics, and finance. ∗Conversations with Kevin M. Murphy inspired and greatly improved this work. We are grateful also to Isaia
We propose a framework for estimation and inference when the model may be misspecified. We rely on a...
Identification in econometric models maps prior assumptions and the data to information about a para...
The problem of identification is defined in terms of the possibility of characterizing parameters of...
Empirical papers in economics often describe heuristically how their estimators map specific data fe...
We propose a local measure of the relationship between parameter estimates and the moments of the da...
Calibration is a much used but problematic method for achieving quantitative predictions from modern...
Statistical models are simplification of reality; we rarely expect the model to be exactly true. Ne...
Statistical models are simplification of reality; we rarely expect the model to be exactly true. Ne...
This article evaluates the reliability of sensitivity tests (Leamer 1978). Using Monte Carlo methods...
Identification in econometric models maps prior assumptions and the data to information about a para...
A statistical sensitivity analysis may be defined and performed in terms of the response of a vector...
Existing guidelines for impact assessment recommend that mathematical modelling of real or man-made ...
1I would like to thank ESRC (grant number RES-000-22-0646) and the British Academy for the nancial s...
We propose a framework for estimation and inference when the model may be misspecified. We rely on a...
We propose a framework for estimation and inference when the model may be misspecified. We rely on a...
We propose a framework for estimation and inference when the model may be misspecified. We rely on a...
Identification in econometric models maps prior assumptions and the data to information about a para...
The problem of identification is defined in terms of the possibility of characterizing parameters of...
Empirical papers in economics often describe heuristically how their estimators map specific data fe...
We propose a local measure of the relationship between parameter estimates and the moments of the da...
Calibration is a much used but problematic method for achieving quantitative predictions from modern...
Statistical models are simplification of reality; we rarely expect the model to be exactly true. Ne...
Statistical models are simplification of reality; we rarely expect the model to be exactly true. Ne...
This article evaluates the reliability of sensitivity tests (Leamer 1978). Using Monte Carlo methods...
Identification in econometric models maps prior assumptions and the data to information about a para...
A statistical sensitivity analysis may be defined and performed in terms of the response of a vector...
Existing guidelines for impact assessment recommend that mathematical modelling of real or man-made ...
1I would like to thank ESRC (grant number RES-000-22-0646) and the British Academy for the nancial s...
We propose a framework for estimation and inference when the model may be misspecified. We rely on a...
We propose a framework for estimation and inference when the model may be misspecified. We rely on a...
We propose a framework for estimation and inference when the model may be misspecified. We rely on a...
Identification in econometric models maps prior assumptions and the data to information about a para...
The problem of identification is defined in terms of the possibility of characterizing parameters of...