Kernel Minimum Distance Estimation and Testing with Conditional Moment Restrictions: A Unified Framework

We propose a unified Kernel Minimum Distance (KMD) framework for estimating and testing models defined by conditional moment restrictions.

By embedding conditional moments into a Reproducing Kernel Hilbert Space (RKHS), we construct a closed-form $V$-statistic objective function that quantifies the distance from the restrictions.

We establish the $\sqrt{n}$-consistency and asymptotic normality of the associated minimum distance estimator.

Within this framework, the minimized objective function naturally yields a consistent omnibus specification test.

Unlike projection-based methods that require auxiliary nonparametric estimation for Neyman orthogonalization, our test inherently captures the estimation effect via a projected kernel structure.

We derive asymptotic properties of the test statistics under the null hypothesis, the alternative hypothesis, and a sequence of local alternatives converging to the null at the parametric rate $n^{-1/2}$.

The validity of a computationally simple multiplier bootstrap is established to facilitate inference.

Simulation results demonstrate robust finite-sample performance, and the framework is illustrated by analyzing Engel curves using UK Family Expenditure Survey data.