Smoothed instrumental variables quantile regression, with estimation of quantile Euler equations∗

This paper develops theory for feasible estimation and testing of finite-dimensional parameters identified by general conditional quantile restrictions. This includes instrumental variables nonlinear quantile regression as a special case, under much weaker assumptions than previously seen in the literature. More specifically, we consider a set of unconditional moments implied by the conditional quantile restrictions and provide conditions for local identification. Since estimators based on the sample moments are generally impossible to compute numerically in practice, we study a feasible estimator based on smoothed sample moments. We establish consistency and asymptotic normality under general conditions that allow for weakly dependent data and nonlinear structural models, and we explore options for testing general nonlinear hypotheses. Simulations with iid and time series data illustrate the finite-sample properties of the estimators and tests. Our in-depth empirical application concerns the consumption Euler equation derived from quantile utility maximization. Advantages of the quantile Euler equation include robustness to fat tails, decoupling of risk attitude from the elasticity of intertemporal substitution, and log-linearization without any approximation error. For the four countries we examine, the quantile estimates of discount factor and elasticity of intertemporal substitution are economically reasonable for a range of quantiles just above the median, even when two-stage least squares estimates are not reasonable.