An Algebraic Approach to Continuously Updating GMM: Estimation and Inference

Abstract

The continuously updating GMM estimator (CUE) minimizes a nonconvex objective. We give an algebraic solution in linear instrumental variables models with a fixed joint variance estimate that allows for heteroskedasticity, autocorrelation, or clustering. With one endogenous regressor and a positive definite variance estimate, the objective is a ratio of polynomials. Its global minimum is obtained by comparing the objective at no more than 4k-2 real stationary points and at infinity, with k instruments. The roots can be computed as eigenvalues of a companion matrix. We extend the results to singular variance estimates and characterize the multivariate minimum using finitely many stationary objective values. These results provide methods for computing CUE, overidentification statistics, and likelihood ratio-based tests. Simulations show that local optimization can lead to substantially different estimates and test decisions. The algebraic results are exact, although their numerical implementation requires care in computing polynomial coefficients and roots.