Dynamic Shrinkage Priors for Large Time-varying Parameter Regressions using Scalable Markov Chain Monte Carlo Methods
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- Niko Hauzenberger & Florian Huber & Gary Koop, "undated". "Dynamic Shrinkage Priors for Large Time-varying Parameter Regressions using Scalable Markov Chain Monte Carlo Methods," Working Papers 2305, University of Strathclyde Business School, Department of Economics.
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- Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
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More about this item
JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
- C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
- E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
- E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ECM-2020-05-18 (Econometrics)
- NEP-ETS-2020-05-18 (Econometric Time Series)
- NEP-ORE-2020-05-18 (Operations Research)
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