Experimetrics: Econometrics For Experimental Economics by Peter G. Moffatt

Experimetrics: Econometrics For Experimental Economics

byPeter G. Moffatt

Paperback | January 1, 2016

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Experimetrics is an essential guide to discovering new and more illuminating ways to analyse experimental econometric data.

Peter Moffatt, one of the world's experts in the field, covers a range of techniques: from the familiar, such as treatment testing, to lesser known ones such as finite mixture models and the method of maximum simulated likelihood. The book takes a hands-on approach by explaining STATA commands in detail. In addition, difficult problems inherent in the methodology are addressed, such as the parametric estimation of social preference models, quantal response models, and learning models.

An indispensable book for researchers and advanced students in experimental and behavioural economics who want to come to grips with the field of Experimetrics.
 
The companion website www.palgrave.com/moffatt contains:
– all data sets (in Stata format) used as examples in the book;
– an executable Stata 'do-file' containing stata commands and programs used in examples; and
– an Excel file containing some Excel calculations presented in the text

About The Author

Peter G. Moffatt is Professor of Econometrics at the University of East Anglia, UK.

Details & Specs

Title:Experimetrics: Econometrics For Experimental EconomicsFormat:PaperbackDimensions:496 pages, 9.1 × 6.1 × 1.1 inPublished:January 1, 2016Publisher:Palgrave MacmillanLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0230250238

ISBN - 13:9780230250239

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Extra Content

Table of Contents

1. Introduction and Overview
2. Statistical Aspects of Experimental Design in Experimental Econometrics
3. Treatment Testing
4. Theory Testing, Regression and Dependence
5. Modelling of Decision Times using Regression Analysis
6. Dealing with Discreteness in Experimental Data
7. Ordinal Data in Experimetrics
8. Dealing with Heterogeneity: Finite Mixture Models
9. Simulating Experimental Data, and the Monte-Carlo Method
10. Introduction to the Method of Maximum Simulated Likelihood
11. Dealing with Zeros: Hurdle Models
12. Choice under Risk: Theoretical Issues
13. Choice under Risk: Econometric Modelling
14. Optimal Design in Binary Choice Experiments
15. Social Preference Models
16. Repeated Games and Quantal Response Models
17. Depth of Reasoning Models
18. Learning Models
19. Summary and Conclusion
Appendix A: List of Data Files and Other Files
Appendix B: List of STATA Commands
Appendix C: Choice Problems used in Chapters 5 and 13
References