Identification‐Robust Minimum Distance Estimation of the New Keynesian Phillips Curve
提出识别稳健的最小距离方法估计新凯恩斯菲利普斯曲线的指数化和价格粘性参数，相比有限信息方法得到更窄的置信区间，发现部分而非完全指数化证据。
Limited‐information identification‐robust methods on the indexation and price rigidity parameters of the New Keynesian Phillips Curve yield very wide confidence intervals. Full‐information methods impose more restrictions on the reduced‐form dynamics and thus make more efficient use of the information in the data. However, such methods are also subject to weak instrument problems. We propose identification‐robust minimum distance methods for exploiting these additional restrictions and show that they yield considerably smaller confidence intervals for the coefficients of the model compared to their limited‐information generalized method of moments counterparts. In contrast to previous studies, we find evidence of partial but not full indexation, and obtain sharper inference on the degree of price stickiness. However, this parameter remains weakly identified.