Bayesian Inference in Econometrics - Info and Reading Options
By Avanindra Narayan Bhat
"Bayesian Inference in Econometrics" was published by B.R. Publishing Corporation in 1999 - Delhi, India, it has 216 pages and the language of the book is English.
“Bayesian Inference in Econometrics” Metadata:
- Title: ➤ Bayesian Inference in Econometrics
- Author: Avanindra Narayan Bhat
- Language: English
- Number of Pages: 216
- Publisher: B.R. Publishing Corporation
- Publish Date: 1999
- Publish Location: Delhi, India
“Bayesian Inference in Econometrics” Subjects and Themes:
- Subjects: ➤ Bayesian statistical decision theory - Bayesian statistics - Bayesian inference - Econometric models - Econometrics -- Congresses - Estimation theory - Statistical methods - Mathematical statistics
Edition Specifications:
- Format: Hardcover
- Weight: 1 pounds
- Pagination: XIII, 191 pages
Edition Identifiers:
- The Open Library ID: OL13149221M - OL10234920W
- Online Computer Library Center (OCLC) ID: 247883571
- ISBN-10: 8176460737
- All ISBNs: 8176460737
AI-generated Review of “Bayesian Inference in Econometrics”:
"Bayesian Inference in Econometrics" Table Of Contents:
- 1- - Acknowledgements
- 2- - Foreword
- 3- - Our Words
- 4- - Introduction
- 5- - Elements of Bayesian Inference in Econometrics
- 6- - Bayesian Inference in the Linear Model
- 7- - A Multivariate Transformation and Bayesian Inference in the Linear Regression Model
- 8- - Extensions to the Linear Regression Model with Nonspherical Disturbances
- 9- - Modelling of Expectations and Uncertainty in Economic Models: A Bayesian Approach
- 10- - A Model of Labour Supply Under Uncertainty
- 11- - Unobservable Variables and Measurement Errors in Econometric Models
- 12- - Conclusions
- 13- - Appendices
- 14- - Bibliography
- 15- - Index
Snippets and Summary:
This study proposes the application of the Bayesian standpoint and approach to economics and econometric methods employed in economic research.
"Bayesian Inference in Econometrics" Description:
The Open Library:
For a very long time there has been a stark dichotomy between the classical (or frequentist) and Bayesian (or subjectivist) approaches to statistical inference, the former being the more commonly followed approach is that in real world applications in statistics and econometrics. A distinctive feature of the Bayesian approach is that it involves the systematic use of prior information in addition to sample information, through the Bayes theorem. In recent years the Bayesian approach has gone beyond merely replicating the well known results of the classical approach in Bayesian setting to working out solutions to problems that were once regarded as intractable. Monte Carlo integration techniques have enabled researchers to deal with some complex problems in the Bayesian context. Bayesian procedures have been found to be useful in solving many decision problems. Applications of the Bayesian methodology have become quite extensive and special in economics and finance.
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