Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Essays in Applied Economics and Machine Learning

Huang, Ying-Kai (2021) Essays in Applied Economics and Machine Learning. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

This is the latest version of this item.

Download (18MB) | Preview


This dissertation consists of three chapters in applied behavioral economics and machine learning applications in economics.
The first chapter studies how reference-dependent utilities influence people's behaviors on crowd-sourced review websites and cause attribution bias. Using data from Yelp, I tested how potential disappointments may affect customers' reviews by applying a regression discontinuity design to control for unobserved factors that may also simultaneously influence ratings. This chapter links to an emerging literature of attribution bias in economics and provides empirical evidence and implications of attribution bias on online reputation systems.
The second chapter extends the work of first study and explores attribution bias when both reference dependence and state dependence are possible to appear. I specifically use the scenario of special occasions to test two leading theories of attribution bias empirically. The empirical results can be explained by one theory of attribution bias where people have higher expectations about restaurants on special occasions and then misattribute their disappointments to the qualities of the restaurants. From the connection between our empirical analyses and theories of attribution bias, this chapter provides another piece of evidence of how attribution bias influences people's perceptions and behaviors.
The third chapter connects machine learning with financial forecasting. I construct a model with recurrent neural networks and focus on the point forecasting of the yield curve to explore the possibility of having better forecasts for the term structure. While allowing similar interpretation as previous econometric methods, the neural network model in this paper shows better forecasting accuracy.


Social Networking:
Share |


Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Huang, Ying-Kaiyih49@pitt.eduyih49
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairHanley, Douglasdoughanley@pitt.edudoughanley
Committee ChairIyengar, Satishssi@pitt.edussi
Committee MemberBeresteanu, Ariearie@pitt.eduarie
Committee MemberRichard, Jean-Francoisfantin@pitt.edufantin
Date: 8 October 2021
Date Type: Publication
Defense Date: 14 June 2021
Approval Date: 8 October 2021
Submission Date: 16 July 2021
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 128
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Economics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Behavioral Economics, Attribution Bias, Learning with Misattribution, Yield Curve, Term Structure, Forecasting, Recurrent Neural Network
Date Deposited: 08 Oct 2021 19:45
Last Modified: 08 Oct 2021 19:45

Available Versions of this Item


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item