Semiparametric Estimation of Consumer Demand Systems with Micro Data

Maximum likelihood and two-step estimators of censored demand systems yield biased and inconsistent parameter estimates when the assumed joint distribution of disturbances is incorrect. This paper proposes a semiparametric estimator that retains the computational advantage of the two-step approach but is immune to distributional misspecification. The key difference between the proposed estimator and the two-step estimator is that the parameters of the binary censoring equations are estimated using a distribution-free single-index model. We implement the proposed estimator using household-level data obtained from the Hainan province in China. specification test lends support to our approach. Copyright 2010, Oxford University Press.