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dc.contributor.authorPANK ROULUND, Rasmus
dc.date.accessioned2019-05-21T12:54:59Z
dc.date.available2019-05-21T12:54:59Z
dc.date.issued2019
dc.identifier.citationFlorence : European University Institute, 2019en
dc.identifier.urihttps://hdl.handle.net/1814/62944
dc.descriptionDefence date: 20 May 2019en
dc.descriptionExamining Board: Prof. Jerome Adda (Supervisor); Prof. Piero Gottardi,University of Essex; Prof. Rosemarie Nagel, Universitat Pompeu Fabra; Prof. Glenn W. Harrison, Georgia State Universityen
dc.description.abstractThis first chapter is co-authored with Nicolás Aragón and examines how participant and market confidence affect the outcomes in an experimental asset market where the fundamental value is known by all participants. Such a market should, in theory, clear at the expected value in each period. However, the literature has shown that bubbles often occur in these markets. We measure the confidence of each participant by asking them to forecast the one-period-ahead price as a discrete probability mass distribution. We find that confidence not only affects price-formation in markets, but is important in explaining the dynamics of bubbles. Moreover, as traders’ confidence grows, they become increasingly more optimistic, thus increasing the likelihood of price bubbles. The second chapter also deals with expectations and uncertainty, but from a different angle. It asks how increased uncertainty affects economic demand in a particular sector, using a discrete-choice demand framework. To investigate this issue I examine empirically to what extent varying uncertainty affects the consumer demand for flight traffic using us micro demand data. I find that the elasticity of uncertainty on demand is economically and statistically significant. The third chapter presents a more practical side to the issue examined in the first chapter. It describes how to elicit participants’ expectations in an economic experiment. The methodology is based on Harrison et al. (2017). The tool makes it easier for participants in economic experiments to forecast the movements of a key variable as discrete values using a discrete probability mass distribution that can be “drawn” on a virtual canvas using the mouse. The module I wrote is general enough that it can be included in other economic experiments.en
dc.description.tableofcontents1. Certainty and Decision-Making in Experimental Asset Markets 1.1. Literature Review 1.2. Hypotheses 1.3. Experimental Design 1.3.1. The asset market 1.3.2. Eliciting traders’ beliefs 1.3.3. Risk, Ambiguity and Hedging 1.4. Overview of experimental data 1.4.1. Summary of the trade data 1.4.2. Expectation data 1.5. Results 1.5.1. Predictions and forecast 1.5.2. Convergence of expectations 1.5.3. Market volatility and initial expectations 1.5.4. Explanatory power of certainty on price formation 1.6. Conclusion 2. The impact of macroeconomic uncertainty on demand: 2.1. Introduction 2.2. Literature review 2.3. A model of demand for flights 2.3.1. Demand 2.3.2. Firms 2.4. Data 2.4.1. The characteristics of the products 2.4.2. Market and macroeconomic characteristics 2.4.3. Instruments 2.4.4. Product shares 2.5. Results 2.6. Conclusion 3. forecast.js: a module for measuring expectation in economic experiments 3.1. Background 3.1.1. Elicitating Expectations in Experimental Finance 3.1.2. Eliciting a Distribution of Beliefs: Theoretical Considerations 3.2. Using the forecast.js module 3.2.1. Calibration 3.2.2. Accessing the forecast data 3.3. The generated data 3.3.1. Example of individual expectations 3.3.2. Timing Considerations 3.3.3. Prediction precision over time 3.4. Conclusion Bibliography A. Appendix to Chapter 1 A.1. Further robustness checks A.1.1. Additional graph for Hypothesis 2 A.1.2. Increased agreement with the Bhattacharyya coefficient A.1.3. Additional robustness checks for Hypothesis 3 A.2. Instructions for experiment A.2.1. General Instructions A.2.2. How to use the computerized market A.3. Questionnaire A.3.1. Before Session A.3.2. After Session B. Appendix to Chapter 3 99 B.1. Robustness check of precision B.2. Using forecast.js in a standalone HTML page B.3. Using forecast.js with oTree B.3.1. Setting up models.py B.3.2. The pages.py file B.3.3. Display forecast modules on the pagesen
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.publisherEuropean University Instituteen
dc.relation.ispartofseriesEUIen
dc.relation.ispartofseriesECOen
dc.relation.ispartofseriesPhD Thesisen
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.subject.lcshUncertainty
dc.subject.lcshRational expectations (Economic theory)
dc.subject.lcshRisk
dc.subject.lcshUncertainty
dc.subject.lcshRational expectations (Economic theory)
dc.subject.lcshRisk
dc.titleEssays in empirical economicsen
dc.typeThesisen
dc.identifier.doi10.2870/868062
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