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• W3144476404 abstract "This paper proposes a mean field variational Bayes algorithm for efficient posterior and predictive inference in time-varying parameter models. Our approach involves: i) computationally trivial Kalman filter updates of regression coefficients, ii) a dynamic variable selection prior that removes irrelevant variables in each time period, and iii) a fast approximate state-space estimator of the regression volatility parameter. In an exercise involving simulated data we evaluate the new algorithm numerically and establish its computational advantages. Using macroeconomic data for the US we find that regression models that combine time-varying parameters with the information in many predictors have the potential to improve forecasts over a number of alternatives." @default.
• W3144476404 created "2021-04-13" @default.
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• W3144476404 date "2018-01-01" @default.
• W3144476404 modified "2023-09-27" @default.
• W3144476404 title "Variational Bayes Inference in High-Dimensional Time-Varying Parameter Models" @default.
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• W3144476404 doi "https://doi.org/10.2139/ssrn.3246472" @default.
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