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.
Keyword: Vector autoregressions, Real-time data, Bayesian methods, and Macroeconomic forecasting Subject (JEL): 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
Creator: Doan, Thomas, Litterman, Robert B., and Sims, Christopher A. Series: Working paper (Federal Reserve Bank of Minneapolis. Research Department) Number: 243 Abstract:
This paper develops a forecasting procedure based on a Bayesian method for estimating vector autoregressions. The procedure is applied to ten macroeconomic variables and is shown to improve out-of-sample forecasts relative to univariate equations. Although cross-variables responses are damped by the prior, considerable interaction among the variables is shown to be captured by the estimates. We provide unconditional forecasts as of 1982:12 and 1963:3* We also describe how a model such as this can be used to make conditional projections and to analyse policy alternatives. As an example, we analyze a Congressional Budget Office forecast made in 1982:12. While no automatic causal interpretations arise from models like ours, they provide a detailed characterization of the dynamic statistical interdependence of a set of economic variables, which may help in evaluating causal hypotheses, without containing any such hypotheses themselves.
Keyword: Forecasting, Macroeconomics, and Bayesian methods Subject (JEL): E27 - Macroeconomics: Consumption, Saving, Production, Employment, and Investment: Forecasting and Simulation: Models and Applications and C11 - Bayesian Analysis: General
Creator: Beauchemin, Kenneth Ronald Series: Staff report (Federal Reserve Bank of Minneapolis. Research Department) Number: 493 Abstract:
This paper describes recent modifications to the mixed-frequency model vector autoregression (MF-VAR) constructed by Schorfheide and Song (2012). The changes to the model are restricted solely to the set of variables included in the model; all other aspects of the model remain unchanged. Forecast evaluations are conducted to gauge the accuracy of the revised model to standard benchmarks and the original model.
Keyword: Bayesian Vector Autoregression and Forecasting Subject (JEL): 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
Creator: Roberds, William Series: Business analysis committee meeting Abstract:
One of the more significant developments in econometric modeling over the past decade has been the invention of the forecasting technique known as Bayesian vector autoregression (BVAR). This paper provides a detailed description of the process of specifying a BVAR model of quarterly time series on the U.S. macroeconomy. The postsample forecasting performance of the model is evaluated at an informal level by comparing the model's performance to certain naive forecasting methods, and is evaluated at a formal level by means of efficiency tests. Although the null hypothesis of efficiency is rejected for the model's forecasts, the accuracy of the model exceeds that of naive forecasting methods, and seems comparable to that of commercial forecasting firms for early quarter forecasts.
Keyword: BVAR, Vector autoregression, and Bayesian analysis Subject (JEL): C11 - Bayesian Analysis: General and C53 - Forecasting Models; Simulation Methods
Creator: Duprey, James N. and Litterman, Robert B. Series: Working paper (Federal Reserve Bank of Minneapolis. Research Department) Number: 128 Keyword: Vector autoregression, Money market model, and Monetary policy Subject (JEL): C53 - Forecasting Models; Simulation Methods and C11 - Bayesian Analysis: General