Simulation-based Bayesian inference for economic time series
AbstractThis 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.
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Bibliographic InfoPaper provided by Federal Reserve Bank of Minneapolis in its series Working Papers with number 570.
Date of creation: 1996
Date of revision:
Publication status: Published in Simulation- based inference in econometrics (2000, pp. 255-299)
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- Tsionas, E.G., 2001.
"Stochastic Frontier Models with Random Coefficients,"
Athens University of Economics and Business
130, Athens University of Economics and Business, Department of International and European Economic Studies.
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