Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/305432 
Erscheinungsjahr: 
2024
Schriftenreihe/Nr.: 
Discussion Papers No. 1015
Verlag: 
Statistics Norway, Research Department, Oslo
Zusammenfassung: 
This paper introduces a panel GMM framework for identifying and estimating demand elasticities via heteroscedasticity. While existing panel estimators address the simultaneity problem, the state-ofthe-art Feenstra/Soderbery (F/S) estimator suffers from inconsistency, inefficiency, and lacks a valid framework for inference. We develop a constrained GMM (C-GMM) estimator that is consistent and derive a uniform formula of its asymptotic standard error that is valid even at the boundary of the parameter space. A Monte Carlo study demonstrates the consistency of the C-GMM estimator and shows that it substantially reduces bias and root mean squared error compared to the F/S estimator. Unlike the F/S estimator, the C-GMM estimator maintains high coverage of confidence intervals across a wide range of sample sizes and parameter values, enabling more reliable inference.
Schlagwörter: 
Demand Elasticity
Panel Data
Heteroscedasticity
GMM
Constrained Estimation
Bagging
JEL: 
C13
C33
C36
Dokumentart: 
Working Paper

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