View by Category

The presentation will start after a short

(15 second) video ad from one of our sponsors.

Hot tip: Video ads won’t appear to registered users who are logged in. And it’s free to register and free to log in!

(15 second) video ad from one of our sponsors.

Hot tip: Video ads won’t appear to registered users who are logged in. And it’s free to register and free to log in!

Loading...

PPT – Expected Utility Theory and Prospect Theory: One Wedding and A Decent Funeral PowerPoint presentation | free to download - id: b5d70-ZDc1Z

The Adobe Flash plugin is needed to view this content

About This Presentation

Write a Comment

User Comments (0)

Transcript and Presenter's Notes

Expected Utility Theoryand Prospect TheoryOne

Wedding and A Decent Funeral

- Glenn W. Harrison E. Elisabet Rutström
- Economics, UCF

Overview

- Hypothesis tests assume just one data generating

process - Whichever DGP explains more of the data is

declared the DGP, and the others discarded - Consider lottery choice behavior
- Assume EUT Prospect Theory
- Assume certain functional forms for the models

generating the data - Allow for multiple DGP, united using mixture

models and a grand likelihood function - Solve the model
- Identify which subjects are better described by

which DGP

Overview

- Hypothesis tests assume just one data generating

process - Whichever DGP explains more of the data is

declared the DGP, and the others discarded - Consider lottery choice behavior the Chapel in

Vegas - Assume EUT Prospect Theory the Bride Groom
- Assume certain functional forms for the models

generating the data the Prenuptual Agreement - Allow for multiple DGP, united using mixture

models and a grand likelihood function the

Wedding - Solve the model Consummating the marriage
- Identify which subjects are better described by

which DGP a Decent Funeral for the

Representative Agent

Experimental Design

- 158 UCF subjects make 60 lottery choices
- Three selected at random and played out
- Each subject received an initial endowment
- Random endowment 1, 2, 10
- Three frames
- Gain frame prizes 0, 5, 10 and 15 N63
- Loss frame endowment of 15 and prizes -15,

-10, -5 and 0 N58 - Mixed frame endowment of 8 and prizes -8,

-3, 3 and 8 N37 - Loss frames versus loss domains

Typical Screen Display

The Bride EUT

- Assume U(s,x) (sx)r
- Assume probabilities for lottery as induced
- EU ?k pk x Uk
- Define latent index ?EU EUR - EUL
- Define cumulative probability of observed choice

by logistic G(?EU) - Conditional log-likelihood of EUT then defined

?i (lnG(?EU)yi1)(ln(1-G(?EU))yi0) - Need to estimate r

The Groom PT

- Assume U(x) xá if x 0
- Assume U(x) -?(-x)â if xlt0
- Assume w(p) p?/ p? (1-p)? 1/?
- PU ?k w(pk) x Uk
- Define latent index ?PU PUR - PUL
- Define cumulative probability of observed choice

by logistic G(?PU) - Conditional log-likelihood of PT then defined

?i (lnG(?PU)yi1)(ln(1-G(?PU))yi0) - Need to estimate á, â, ? and ?

The Nuptial

- Grand-likelihood is just the probability weighted

conditional likelihoods - Probability of EUT pEUT
- Probability of PT pPT 1- pEUT
- Ln L(r, á, â, ?, ?, pEUT y, X) ?i ln (pEUT

x LiEUT) (pPT x LiPT) - Need to jointly estimate r, á, â, ?, ? and pEUT
- Two DGPs not nested, but could be
- Easy to extend in principle to 3 DGPs

Consummating the Marriage

- Standard errors corrected for possible

correlation of responses by same subject - The little pitter-patter of covariates
- X Female, Black, Hispanic, Age, Business,

GPAlow - Each parameter estimated as a linear function of

X - Numerical issues

Result 1 Equal Billing for EUT PT

- Initially only assume heterogeneity of DGP
- pEUT 0.55
- So EUT wins by a (quantum) nose, but we do not

declare winners that way - H0 pEUT pPT ½ has p-value of 0.49

Result 2 Estimates Are Better

- When PT is assumed to characterize every data

point, estimates are not so hot - Very little loss aversion
- No probability weighting
- But when estimated in mixture model, and only

assumed to account for some of the choices, much

more consistent with a priori beliefs

Result 3 Classifying Subjects

- Now move to include covariates X
- Subjects are either clearly EUT or probably

PT, not two distinct modes

(No Transcript)

(No Transcript)

(No Transcript)

(No Transcript)

(No Transcript)

Conclusion

- Sources of heterogeneity
- Observable characteristics
- Unobservable characteristics
- Unobservable processes
- Great potential to resolve some long-standing

disputes - EUT versus PT
- Exponential versus Hyperbolicky discounting
- Calibrating for hypothetical bias in CVM
- Serious technical issues to be addressed
- Data needs just increase N
- Estimation problems

Recommended

«

/ »

Page of

«

/ »

Promoted Presentations

Related Presentations

CrystalGraphics Sales Tel: (800) 394-0700 x 1 or Send an email

Home About Us Terms and Conditions Privacy Policy Contact Us Send Us Feedback

Copyright 2015 CrystalGraphics, Inc. — All rights Reserved. PowerShow.com is a trademark of CrystalGraphics, Inc.

Copyright 2015 CrystalGraphics, Inc. — All rights Reserved. PowerShow.com is a trademark of CrystalGraphics, Inc.

The PowerPoint PPT presentation: "Expected Utility Theory and Prospect Theory: One Wedding and A Decent Funeral" is the property of its rightful owner.

Do you have PowerPoint slides to share? If so, share your PPT presentation slides online with PowerShow.com. It's FREE!