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Empirical Methods: Bayesian Estimation

In: Economic Growth

Author

Listed:
  • Alfonso Novales

    (Complutense University of Madrid)

  • Esther Fernández

    (Complutense University of Madrid)

  • Jesús Ruiz

    (Complutense University of Madrid)

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.

Suggested Citation

  • Alfonso Novales & Esther Fernández & Jesús Ruiz, 2022. "Empirical Methods: Bayesian Estimation," Springer Texts in Business and Economics, in: Economic Growth, edition 3, chapter 11, pages 581-619, Springer.
    • Handle: RePEc:spr:sptchp:978-3-662-63982-5_11
    DOI: 10.1007/978-3-662-63982-5_11
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