| Title: | Bayesian Methods for Dynamic Multivariate Models |
| Author: | Sims, Christopher A.; Zha, Tao |
| Author Affiliation: | Yale U; Federal Reserve Bank of Atlanta |
| Source: | International Economic Review, November 1998, v. 39, iss. 4, pp. 949-68 |
| Publication Date: | November 1998 |
| Abstract: | If dynamic multivariate models are to be used to guide decisionmaking, it is important that probability assessments of forecasts or policy projections be provided. When identified Bayesian vector autoregression (VAR) models are presented with error bands in the existing literature, both conceptual and numerical problems have not been dealt with in an internally consistent way. In this paper, the authors develop methods to introduce prior information in both reduced-form and structural VAR models without introducing substantial new computational burdens. Their approach makes it feasible to use a single, large dynamic framework (for example, twenty-variable models) for tasks of policy projections. |
| Descriptors: | Time Series and Spectral Analysis Econometric and Statistical Methods and Models: Multivariate Analysis, Statistical Information Theory, and Other Special Inferential Problems; Queuing Theory; Markov Chains Inferential Problems in Simultaneous Equation Systems Domestic Monetary Theory; Empirical Studies Illustrating Theory General Forecasts and Models Econometric Methods: Multiple/Simultaneous Equation Models: Time-Series Models General Aggregative Models: Forecasting and Simulation Monetary Policy (Targets, Instruments, and Effects) |
| Geographic Descriptors: | U.S. |
| ISSN: | 00206598 |
| Publication Type: | Journal Article |
| Availability: | http://www.econ.upenn.edu/Centers/iereview/ |