Valid Inference in Partially Unstable GMM Models

Li, Hong and Mueller, Ulrich (2006): Valid Inference in Partially Unstable GMM Models.

Abstract

The paper considers time series GMM models where a subset of the 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 the instability and a large class of GMM models, standard GMM inference on the subset of stable parameters, ignoring the partial instability, remains asymptotically valid.

• Item Type: MPRA Paper
• Institution: Princeton University
• Language: English
• Keywords: Structural Breaks; Parameter Stability Test; Contiguity; Euler Condition; New Keynesian Phillips Curve
• Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models