Generalized band spectrum estimation with an application to the New Keynesian Phillips curve
提出广义频带谱估计方法，解决线性动态结构模型中的内生性和非线性问题，应用于美国战后数据的新凯恩斯菲利普斯曲线，发现短期边际成本系数稳定且显著，估计不确定性小于GMM。
Summary This paper proposes a new method for estimating linear dynamic structural models. The proposed generalized band spectrum estimator (GBSE) generalizes band spectrum regression to simultaneously account for endogeneity and nonlinearities of unknown form in the first stage, while having a computationally convenient closed‐form expression in the time domain. We apply the GBSE to the hybrid New Keynesian Phillips curve (NKPC) with U.S. postwar data. We find a stable marginal cost coefficient in the short run, with relatively large and statistically significant values, and small and insignificant values when both short‐run and long‐run frequencies are included. The forward‐looking component and the inflation inertia are relatively stable and equally quantitatively important. Overall, our estimates present much less sampling uncertainty than the corresponding generalized method of moment (GMM) estimates, both in extensive Monte Carlo simulations and the empirical application, and they provide formal empirical support for misspecification of the NKPC as a model for all frequencies.