Working Paper

Simulation-based Bayesian inference for economic time series

Abstract: This paper surveys recently developed methods for Bayesian inference and their use in economic time series models. It begins by reviewing aspects of Bayesian inference essential to understanding the implications of the Bayesian paradigm for time series analysis. It next describes the use of posterior simulators to solve otherwise intractable analytical problems. The theory and the computational advances are brought together in setting forth a practical framework for decision-making and forecasting. These developments are illustrated in the context of the vector autoregressions, stochastic volatility models, and models of changing regimes.

Status: Published in Simulation- based inference in econometrics (2000, pp. 255-299)

Bibliographic Information

Provider: Federal Reserve Bank of Minneapolis

Part of Series: Working Papers

Publication Date: 1996

Number: 570