Bayesian econometric methods /Joshua Chi Chun Chan

By: Joshua Chi Chun Chan ; Gary Koop ; Dale J Poirier ; Justin L TobiasContributor(s): Gary Koop | ; Dale J Poirier | ; Justin L TobiasMaterial type: TextTextPublisher number: International Book Distributors | ;Flat No.17,Prakash Apartments,5 Ansari Road,New Delhi-110002Publication details: New York, NY, USA : Cambridge University Press , 2020Description: xxiii, 466 pages ; 26 cmISBN: 9781108437493Subject(s): Social SciencesGenre/Form: ;EconometricsDDC classification: 330.0151 CHA
Contents:
1. The subjective interpretation of probability; 2. Bayesian inference; 3. Point estimation; 4. Frequentist properties of Bayesian estimators; 5. Interval estimation; 6. Hypothesis testing; 7. Prediction; 8. Choice of prior; 9. Asymptotic Bayes; 10. The linear regression model; 11. Basics of random variate generation and posterior simulation; 12. Posterior simulation via Markov chain Monte Carlo; 13. Hierarchical models; 14. Latent variable models; 15. Mixture models; 16. Bayesian methods for model comparison, selection and big data; 17. Univariate time series methods; 18. State space and unobserved components models; 19. Time series models for volatility; 20. Multivariate time series methods; Appendix; Bibliography; Index.
Summary: The past two decades have seen econometrics grow into a vast discipline. Many different branches of the subject now happily coexist with one another. These branches interweave econometric theory and empirical applications and bring econometric method to bear on a myriad of economic issues
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Item type Current library Call number Status Date due Barcode Item holds
Books Books SNU LIBRARY
330.0151 CHA (Browse shelf(Opens below)) Checked out to Suchismita Tarafdar (20500106) 04/09/2024 00:00 27232
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1. The subjective interpretation of probability; 2. Bayesian inference; 3. Point estimation; 4. Frequentist properties of Bayesian estimators; 5. Interval estimation; 6. Hypothesis testing; 7. Prediction; 8. Choice of prior; 9. Asymptotic Bayes; 10. The linear regression model; 11. Basics of random variate generation and posterior simulation; 12. Posterior simulation via Markov chain Monte Carlo; 13. Hierarchical models; 14. Latent variable models; 15. Mixture models; 16. Bayesian methods for model comparison, selection and big data; 17. Univariate time series methods; 18. State space and unobserved components models; 19. Time series models for volatility; 20. Multivariate time series methods; Appendix; Bibliography; Index.

The past two decades have seen econometrics grow into a vast discipline. Many different branches of the subject now happily coexist with one another. These branches interweave econometric theory and empirical applications and bring econometric method to bear on a myriad of economic issues

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