MIXED‐FREQUENCY STRUCTURAL MODELS: IDENTIFICATION, ESTIMATION, AND POLICY ANALYSIS
针对DSGE模型与数据频率不匹配导致的识别和估计偏差问题，提出基于混频数据的估计策略，并在两个货币政策模型中验证其优势。
SUMMARY The mismatch between the timescale of DSGE (dynamic stochastic general equilibrium) models and the data used in their estimation translates into identification problems, estimation bias, and distortions in policy analysis. We propose an estimation strategy based on mixed‐frequency data to alleviate these shortcomings. The virtues of our approach are explored for two monetary policy models. Copyright © 2014 John Wiley & Sons, Ltd.