Empirical Methods: Bayesian Estimation

Alfonso Novales, Esther Fernández and Jesus Ruiz

Chapter 11 in Economic Growth, 2022, pp 581-619 from Springer

Abstract: Abstract The chapter starts with an introduction to Bayesian inference, and two applications examples in the context of regression models. After that, we introduce Markov Chain Monte Carlo Methods and provide a theoretical discussion of two families of such methods: Gibbs-sampling and Metropolis-Hastings algorithms. We estimate the parameters of a linear regression model using the Gibbs-sampling algorithm. Three applications of the Metropolis-Hastings algorithm are considered: random number generation from a Cauchy distribution; estimation of a GARCH(1,1) model, and estimation of a DSGE model which has been already estimated in Chap. 10 under a frequentist approach, so that the reader can compare the two different methodologies for the estimation of Growth models.

Keywords: Markov Chain Monte Carlo Methods; Gibbs-sampling; Metropolis-Hastings algorithms; GARCH(1; 1) (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sptchp:978-3-662-63982-5_11

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DOI: 10.1007/978-3-662-63982-5_11

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