Valid Inference in Partially Unstable GMM Models∗
نویسندگان
چکیده
The paper considers estimation and inference of time series GMM models where a subset of parameters are time varying. The magnitude of the time variation in the unstable parameters is such that efficient tests detect the instability with (possibly high) probability smaller than one, even in the limit. We show that for many forms of parameter instability and for a large class of GMM models, standard GMM inference on the subset of stable parameters, ignoring the partial instability in other parts of a model, remains asymptotically valid. Moreover, Monte Carlo simulations demonstrate that the asymptotic result provides reasonable guidance in finite samples. JEL Classification: C32
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