Estimating the Rational Expectations Model of Speculative Storage: A Monte Carlo Comparison of Three Simulation Estimators

The non-negativity constraint on inventories imposed on the rational expectations theory of speculative storage implies that the conditional mean and variance of commodity prices are nonlinear in lagged prices and have a kink at a threshold point. In this paper, the structural parameters of this model are estimated using three simulation based estimators. The finite sample properties of the Simulated Methods of Moments estimator of Duffie and Singleton (1993), the Indirect Inference estimator of Gourieroux, Monfort and Renault (1993), and the matching score estimator of Gallant and Tauchen (1996) are assessed. Exploiting the invariant distribution implied by the theory allows us to assess the error induced by simulations. Our results show that while all three estimators produce reasonably good estimates with properties that stack up well with those of the PMLE, there are tradeoffs among the three estimators in terms of bias, efficiency, and computation demands. Some estimators are more sensitive to the sample size and the number of simulations than others. A careful choice of the moments/auxiliary models can lead to a substantial reduction in bias and an improvement in efficiency. Increasing the number of simulated data points can sometimes reduce the bias and improve the efficiency of the estimates when the sample size is small.