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Creator: Duprey, James N. and Litterman, Robert B. Series: Working paper (Federal Reserve Bank of Minneapolis. Research Department) Number: 128 Palavra-chave: Vector autoregression, Money market model, and Monetary policy Sujeito: C53 - Forecasting Models; Simulation Methods and C11 - Bayesian Analysis: General
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.
Palavra-chave: BVAR, Vector autoregression, and Bayesian analysis Sujeito: C11 - Bayesian Analysis: General and C53 - Forecasting Models; Simulation Methods
Creator: Geweke, John Series: Working paper (Federal Reserve Bank of Minneapolis. Research Department) Number: 532 Abstract:
This paper integrates and extends some recent computational advances in Bayesian inference with the objective of more fully realizing the Bayesian promise of coherent inference and model comparison in economics. It combines Markov chain Monte Carlo and independence Monte Carlo with importance sampling to provide an efficient and generic method for updating posterior distributions. It exploits the multiplicative decomposition of marginalized likelihood into predictive factors, to compute posterior odds ratios efficiently and with minimal further investment in software. It argues for the use of predictive odds ratios in model comparison in economics. Finally, it suggests procedures for public reporting that will enable remote clients to conveniently modify priors, form posterior expectations of their own functions of interest, and update the posterior distribution with new observations. A series of examples explores the practicality and efficiency of these methods.
This paper was prepared for the inaugural Colin Clark Lecture, Australasian Meetings of the Econometric Society, July 1994.
Palavra-chave: Computation, Model comparison, Bayesian inference, and Econometric modeling Sujeito: C53 - Forecasting Models; Simulation Methods and C11 - Bayesian Analysis: General
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.
Palavra-chave: Macroeconomic forecasting, Bayesian methods, Real-time data, and Vector autoregressions Sujeito: C32 - Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models, C11 - Bayesian Analysis: General, and C53 - Forecasting Models; Simulation Methods
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.
Palavra-chave: Forecasting and Bayesian Vector Autoregression Sujeito: C32 - Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models, C11 - Bayesian Analysis: General, and C53 - Forecasting Models; Simulation Methods