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可靠性随时间变化的高频工具变量：理解宏观经济识别AI 解读

ABSTRACT The effects of monetary policy shocks are regularly estimated using high‐frequency surprises in asset prices around central bank meetings as an instrument. These studies, insofar as they explicitly model the relationship between instrument and structural shock, assume a constant relationship between the instrument and the monetary policy shock. By allowing for time variation in this relationship, we show that only a few distinct periods are informative about…

带多重符号与排序限制的大型结构 VARAI 解读

Large VARs are increasingly used in structural analysis as a unified framework to study the impacts of multiple structural shocks simultaneously. However, the concurrent identification of multiple shocks using sign and ranking restrictions poses significant practical challenges to the point where existing algorithms cannot be used with such large VARs. To address this, we introduce a new numerically efficient algorithm that facilitates the estimation of impulse responses and…

结构宏观计量模型中的误设处理AI 解读

We consider a set of potentially misspecified structural models, geometrically combine their likelihood functions, and estimate the parameters using composite methods. In a Monte Carlo study, composite estimators dominate likelihood‐based estimators in mean squared error and composite models are superior to individual models in the Kullback–Leibler sense. We describe Bayesian quasi‐posterior computations and compare our approach to Bayesian model averaging, finite mixture,…

先验超参数的选择及其在时变参数模型中的应用AI 解读

Time-varying parameter models with stochastic volatility are widely used to study macroeconomic and financial data. These models are almost exclusively estimated using Bayesian methods. A common practice is to focus on prior distributions that themselves depend on relatively few hyperparameters such as the scaling factor for the prior covariance matrix of the residuals governing time variation in the parameters. The choice of these hyperparameters is crucial because their…

测量误差下的漂移与波动率：评估过去一个世纪的货币政策冲击AI 解读

How much have the dynamics of U.S. time series changed over the last century? Has the evolution of the Federal Reserve as an institution over the 100 years altered the transmission of monetary policy shocks? To tackle these questions, we build a multivariate time series model with time-varying parameters and stochastic volatility that features measurement errors in observables. We find substantial changes in the structure of the economy. There is also large variation in the…

奇异DSGE模型估计中的变量选择AI 解读

SUMMARY We propose two methods to choose the variables to be used in the estimation of the structural parameters of a singular DSGE model. The first selects the vector of observables that optimizes parameter identification; the second selects the vector that minimizes the informational discrepancy between the singular and non‐singular model. An application to a standard model is discussed and the estimation properties of different setups compared. Practical suggestions for…