Semiparametric Bayesian Inference for a Conditional Moment Equality Model

Conditional moment equality models are regularly encountered in empirical economics, yet they are difficult to estimate. These models map a conditional distribution of data to a structural parameter via the restriction that a conditional mean equals zero. Using this observation, I introduce a Bayesian inference framework in which an unknown conditional distribution is replaced with a nonparametric posterior, and structural parameter inference is then performed using an implied posterior. The method has the same flexibility as frequentist semiparametric estimators and does not require converting conditional moments to unconditional moments. Importantly, I prove a semiparametric Bernstein-von Mises theorem, providing conditions under which, in large samples, the posterior for the structural parameter is approximately normal, centered at an efficient estimator, and has variance equal to the Chamberlain (1987) semiparametric efficiency bound. As byproducts, I show that Bayesian uncertainty quantification methods are asymptotically optimal frequentist confidence sets and derive low-level sufficient conditions for Gaussian process priors. The latter sheds light on a key prior stability condition and relates to the numerical aspects of the paper in which these priors are used to predict the welfare effects of price changes.