Creator: Croushore, Dean Darrell, 1956- and Evans, Charles, 1958- Series: Joint committee on business and financial analysis Abstract:
Monetary policy research using time series methods has been criticized for using more information than the Federal Reserve had available in setting policy. To quantify the role of this criticism, we propose a method to estimate a VAR with real-time data while accounting for the latent nature of many economic variables, such as output. Our estimated monetary policy shocks are closely correlated with a typically estimated measure. The impulse response functions are broadly similar across the methods. Our evidence suggests that the use of revised data in VAR analyses of monetary policy shocks may not be a serious limitation.
关键词: Monetary policy, Identification, VARs, Data revisions, Real-time data, and Shocks 学科: C82 - Data collection and data estimation methodology ; Computer programs - Methodology for collecting, estimating, and organizing macroeconomic data, C32 - Multiple or simultaneous equation models - Time-series models ; Dynamic quantile regressions, and E52 - Monetary policy, central banking, and the supply of money and credit - Monetary policy
Creator: Schorfheide, Frank and Song, Dongho Series: Working paper (Federal Reserve Bank of Minneapolis. Research Department) Number: 701 Abstract:
This paper develops a vector autoregression (VAR) for macroeconomic time series which are observed at mixed frequencies – quarterly and monthly. The mixed-frequency VAR is cast in state-space form and estimated with Bayesian methods under a Minnesota-style prior. Using a real-time data set, we generate and evaluate forecasts from the mixed-frequency VAR and compare them to forecasts from a VAR that is estimated based on data time-aggregated to quarterly frequency. We document how information that becomes available within the quarter improves the forecasts in real time.
关键词: Vector autoregressions, Real-time data, Bayesian methods, and Macroeconomic forecasting 学科: C11 - Bayesian Analysis: General, C32 - Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models, and C53 - Forecasting Models; Simulation Methods