Identifying demand elasticity via heteroscedasticity: A panel GMM approach to estimation and inference - Thomas von Brasch; Arvid Raknerud; Trond C. Vigtel (Statistics Norway, Research Department, 2024)
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.
Keywords: C13; C33; C36; 330; Demand Elasticity; Panel Data; Heteroscedasticity; GMM; Constrained Estimation; Bagging