Barcelona LeeX Experimental Economics Summer School in Macroeconomics in Universitat Pompeu Fabra.
June 11-15, 2012:
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Charles Noussair | Shyam Sunder | John Duffy | Frank Heinemann | Rosemarie Nagel
Charles Noussair
- Experimental Studies on Economic Growth and DSGE models
Understanding the mechanisms behind economic growth is a fundamental task of macroeconomics. A class of models, called growth models, has been proposed to address this challenge. This lecture will describe how experimental methods have been used to evaluate the predictions of these models. Empirical evidence from field studies supports the view that institutions can influence the rate of economic growth. This lecture will cover how the role of institutions in economic growth can be studied using experimental methods. A particular type of model, the dynamic stochastic general equilibrium (DSGE) model, has become a standard tool of policy analysis. This lecture will describe how experiments with the same structure can be constructed and used to address policy questions.
Vivian Lei and Charles Noussair “An Experimental Test of an Optimal Growth Model", American Economic Review, Vol. 92, no 3, June 2002, pages 549-570.
C. Monica Capra, Colin Camerer, Tomomi Tanaka, Lauren Feiler, Veronica Sovero, and Charles Noussair “The Impact of Simple Institutions in Experimental Economies with Poverty Traps", Economic Journal 119, 539, July 2009, pages 977 – 1009.
Charles Noussair, Damjan Pfajfar, and Janos Zsiros, “Frictions, Persistence, and Central Bank Policy in an Experimental Dynamic Stochastic General Equilibrium Economy", Tilburg University working paper, 2011.
Shyam Sunder
- Complexity and Abstraction: Designing Macro Experiments
Relevant real world phenomena and relevant models of interest serve as two important benchmarks in designing laboratory experiments. With their fractal structure, phenomena in field are endlessly complex. Accordingly, realism (i.e., fidelity to the field environment) and theory (i.e., fidelity to the model) place important, often conflicting, demands on design of laboratory experiments. Do the details matter? Which ones do? How do we find out? Why do the details that “do not matter" exist in the field? If they are just matters of refinement, which refinements are and are not to be ignored in laboratory? How generalizable are the laboratory findings? How does an experimenter find his way through this maze that connects limitless complexity of the field to simple tidy models of economics to gain a better understanding of economic phenomena? We shall explore the practical problems of identifying interesting questions, and developing experimental designs to address them using some examples, notes, and some macro experiments.
Sunder, Shyam. “Determinants of Economic Interaction: Behavior or Structure." Journal of Economic Interaction and Coordination 1, no. 1 (May 2006): 21-32. Text (PDF).
Sunder, Shyam. “Real Phenomena, Theory and Design of Laboratory Experiments in Economics." Notes. Text (PDF).
Lim, Suk S., Edward C. Prescott and Shyam Sunder. “Stationary Solution to the Overlapping Generations Model of Fiat Money: Experimental Evidence." Empirical Economics 19, no. 2 (1994): 255-277. Text (PDF)
Marimon, Ramon and Shyam Sunder. “Indeterminacy of Equilibria in a Hyperinflationary World: Experimental Evidence." Econometrica 61, no. 5 (1993): 1073-1108. Text (PDF).
Marimon, Ramon and Shyam Sunder. “Expectations and Learning Under Alternative Monetary Regimes: An Experimental Approach." Economic Theory 4 (1994), 131-162. Text (PDF)
Huber, Juergen, Martin Shubik, and Shyam Sunder. “Financing of Public Goods through Taxation in a General Equilibrium Economy: Theory and Experimental Evidence," Cowles Foundation Discussion Paper 1830, October 23, 2011.
Huber, Juergen, Martin Shubik and Shyam Sunder. “Sufficiency of an Outside Bank and a Default Penalty to Support the Value of Fiat Money: Experimental Evidence." Cowles Foundation Discussion Paper No. 1675, Revised June 12, 2011.
- Experiments with Minimally Intelligent Agents and Minimal Institutions
Laboratory exploration of properties of economic institutions and policies has traditionally been done using profit motivated human traders. Outcomes of such experiments, when compared with outcomes of identical economies populated with minimally intelligent algorithmic agents yield valuable insights. We can isolate which of the properties of the economies of interest arise from their structure, and which ones are attributable to the behavior of agents. Starting with three micro applications, we shall study three macro applications of this human-algorithm hybrid approach to experimentation.
Gode, Dhananjay K. and Shyam Sunder. “Allocative Efficiency of Markets with Zero Intelligence Traders: Market as a Partial Substitute for Individual Rationality." The Journal of Political Economy 101, no. 1 (February 1993): 119-137.Text (PDF).
Gode, Dhananjay and Shyam Sunder. “What Makes Markets Allocationally Efficient?" Quarterly Journal of Economics 112, no. 2 (May 1997), 603-630. GSIA Reprint No. 1473.Abstract (PDF), Text (PDF).
Gode, Dhananjay K., and Shyam Sunder. “Double Auction Dynamics: Structural Effects of Non-Binding Price Controls."Journal of Economic Dynamics and Control 28, no. 9 (July 2004): 1707-1731. Abstract(PDF), Text (PDF).
Huber, Juergen, Martin Shubik and Shyam Sunder. “Three Minimal Market Institutions: Theory and Experimental Evidence." Games and Economic Behavior 70 (2010) 403-424.
Angerer, Martin, Juergen Huber, Martin Shubik and Shyam Sunder. “An Economy with Personal Currency: Theory and Experimental Evidence."Annals of Finance, Volume 6, Number 4, October 2010, pp.475-509.
Huber, Juergen, Martin Shubik and Shyam Sunder. “Default Penalty as a Selection Mechanism Among Multiple Equilibria."Cowles Foundation Discussion Paper 1730R, Revised February 6, 2011.
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John Duffy
- Overview of Macroeconomic Experiments
This lecture will expose participants to the breadth of macroeconomic topics and questions that have been explored using laboratory methods. The aim of this lecture will be to stimulate thinking about ideas for new projects that build on what has already been done. In addition, participants will be encouraged to extend laboratory methods to macroeconomic models or questions that have not been previously addressed. Methodological issues that are particularly relevant to macroeconomic experiments, e.g., implementation of discounting and infinite horizons, will also be addressed.
| Duffy, J. (fortcoming), “Macroeconomics: A Survey of Laboratory Research" to appear in Handbook of Experimental Economics, vol. 2, edited by John Kagel and Al Roth.
Ochs, J. (1995), “Coordination Problems," in J. Kagel and A.E. Roth, (Eds.), The Handbook of Experimental Economics, (Princeton: Princeton University Press).
Ricciuti, R. (2005), “Bringing Macroeconomics into the Lab," working paper, University of Siena.
Duffy, J. (1998), “Monetary Theory in the Laboratory," Federal Reserve Bank of St. Louis Review 80, 9-26. |
- Asset Pricing: Bubbles, Crashes and Expectations
Currently, economies around the world are experiencing an economic downturn brought about by the collapse of housing and equity prices and the deleveraging of the financial institutions that underwrote those assets. In this lecture we examine laboratory studies addressing asset pricing and the phenomenon of asset price bubbles and crashes. An understanding of the causes of asset price bubbles and cashes is of obvious importance to both policymakers and asset market participants. While there exists experimental designs that reliably yield asset price bubbles and crashes among inexperienced subjects, there remains much more work to be done on this topic, for instance, there is a need for an experimental design in which asset price bubbles and crashes are recurrent phenomena.
| Smith, Vernon, Gerry L. Suchanek and Arlington W. Williams, 1988. “Bubbles, Crashes, and Endogenous Expectations in Experimental Spot Asset Markets," Econometrica, 56, 1119-1151.
Lei, Vivian, Charles N. Noussair and Charles R. Plott 2001. “Nonspeculative Bubbles in Experimental Asset Markets: Lack of Common Knowledge of Rationality vs. Actual Irrationality," Econometrica, 69, 831-859.
Dufwenberg, Martin, Tobias Lindqvist and Evan Moore, 2005. “Bubbles and Experience: An Experiment," American Economic Review, 95, 1731-1737.
Hommes, Cars.H., Joep Sonnemans, Jan Tuinstra and Henk van de Velden, 2005. “Coordination of Expectations in Asset Pricing Experiments," Review of Financial Studies 18, 955-980.
Ernan Haruvy, Yaron Lahav and Charles N. Noussair, 2007. “Traders’ Expectations in Asset Markets: Experimental Evidence," American Economic Review 97, 1901-1920.
Crockett, Sean and John Duffy, 2009. “A General Equilibrium Approach to Asset Pricing Experiments." working paper. |
Among the central questions in monetary theory are why intrinsically worthless fiat money serves as a store of value and why it is used as a medium of exchange when other assets dominate it in rate of return. Various theories have been developed to address these fundamental questions. For instance, overlapping generations models of money may explain why fiat money has value, and search-theoretic approaches can rationalize why money is used when dominated in rate of return by other competing assets. However, the frictions in these models -overlapping generations and search frictions- make them difficult to take to field data. On the other hand, a number of laboratory studies of such models have been conducted. These lectures will outline the main findings from those studies and point out promising new extensions.
 Introduction:
| Duffy, J. (1998), “Monetary Theory in the Laboratory," Federal Reserve Bank of St. Louis Review 80 (September/October), 9-26. |
Theory:
Lucas, R.E. (1986), “Adaptive Behavior and Economic Theory," Journal of Business 59,
S401-S426.
Wallace, N. (1980), “The Overlapping Generations Model of Fiat Money," in J.H. Kareken and N. Wallace, Eds., Models of Monetary Economies, Federal Reserve Bank of Minneapolis
Kiyotaki, N. and R. Wright (1989), “On Money as a Medium of Exchange," Journal of Political Economy 97, 927-54
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Experiments:
| Bernasconi, M. and Kirchkamp, O. (2000), “Why Do Monetary Policies Matter? An Experimental Study of Saving and Inflation in an Overlapping Generations Model," Journal of Monetary Economics 46, 315-43.
Brown, P. (1996), “Experimental Evidence on Money as a Medium of Exchange," Journal of Economic Dynamics and Control 20, 583-600.
Camera, G., Noussair, C., and Tucker, S. (2003), “Rate-of-Return Dominance and Efficiency in an Experimental Economy," Economic Theory 22, 629-60.
Duffy, J. and J. Ochs (2002), “Intrinsically Worthless Objects as Media of Exchange: Experimental Evidence," International Economic Review 43, 637-73.
Duffy, J. and J. Ochs (1999), “Emergence of Money as a Medium of Exchange: An Experimental Study," American Economic Review 89, 847-77.
Lim, S. Prescott, E.C. and Sunder, S. (1994), “Stationary Solution to the Overlapping Generations Model of Fiat Money: Experimental Evidence," Empirical Economics 19, 255-77.
Marimon, R. and Sunder, S. (1994), “Expectations and Learning under Alternative Monetary Regimes: An Experimental Approach," Economic Theory 4, 131-62.
Marimon, R. and Sunder, S. (1993) “Indeterminacy of Equilibria in a Hyperinflationary World: Experimental Evidence," Econometrica 61, 1073-107. |
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Frank Heinemann
- Understanding Financial Crises: The Contribution of Experimental Economics
The patterns of financial crises are remarkably predictable. Minsky (1972) has described these patterns by phases, some of which contain behavioural hypotheses that can be tested by laboratory experiments. Under which conditions can bubbles arise? When do they burst? Why do people herd and does herding destabilize financial markets? What triggers a bank run and how do people coordinate in an environment with multiple equilibria? This lecture will lay out experimental evidence containing some answers to these questions. In particular, we will look at experiments on games with strategic complementarities. How predictable are choices if the game has multiple equilibria and which theory is well-suited to give advice for individual behavior? Managing information flow is one of the major challenges for central banks and bank supervisors. The lecture explains what we can learn from experiments for managing information flow in the presence of strategic complementarities.
Minsky, H.P. (1972), Financial Instability Revisited: the Economics of Disaster, http://fraser.stlouisfed.org/historicaldocs/dismech/download/59037/fininst_minsky.pdf
Brunnermeier, Markus, and John Morgan (2008), Clock Games: Theory and Experiments, Games and Economic Behavior, forthcoming, http://www.princeton.edu/~markus/research/papers/clock_games.pdf
Kübler, Dorothea, and Georg von Weizsäcker (2004), Limited Depth of Reasoning and Failure of Cascade Formation in the Laboratory, Review of Economic Studies 71, 425-442.
Schotter, Andrew, and Tanju Yorulmazer (2009), On the Severity of Bank Runs, Journal of Financial Intermediation 18, 217-241.
Heinemann, Frank, Rosemarie Nagel, and Peter Ockenfels (2009), Measuring Strategic Uncertainty in Coordination Games, Review of Economic Studies 76, 181-221.
Cornand, C., and F. Heinemann (2010), Measuring Agents’ Reaction to Private and Public Information in Games with Strategic Complementarities, CESifo Working Paper 2947, http://anna.ww.tu-berlin.de/~makro/Heinemann/download/ch_3.pdf
- Speculative Attacks and the Theory of Global Games – Experimental Tests of Global Game Predictions
Speculative attacks can be viewed upon as coordination games: if a sufficient number of traders (and a sufficient amount of capital) is involved in an attack, the pressure on foreign exchange markets forces the central bank to devaluate its currency. Then, all attacking traders gain from the devaluation. But, if the number of attackers is too small, the central bank can defend the peg, and attacking traders lose on transaction costs. Speculative-attack games have multiple equilibria if payoff functions are common knowledge. The theory of global embeds a coordination game in an environment with private information about parameters of the payoff function. If private information is sufficiently precise, the global game has a unique equilibrium. Hence, the theory of global games can be used for a unique prediction of the outcome of a speculative-attack game. This theory provides a number of hypotheses that can be tested in laboratory experiments. This lecture first presents some of the theoretical background and derives testable hypotheses. Then, it explains experiments that have been used for these tests and shows how they have been analyzed.
Introduction:
| Heinemann, Frank (2002), “Exchange-Rate Attack as a Coordination Game: Theory and Experimental Evidence," Oxford Review of Economic Policy 18, 462-478.
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Theory:
| Obstfeld, Maurice (1997), “Destabilizing Effects of Exchange-Rate Escape Clauses," Journal of International Economics, 61-77.
Carlsson, Hans and Eric van Damme (1993), “Global Games and Equilibrium Selection," Econometrica 61, 989-1018.
Morris, S., and H.S. Shin (1998), “Unique Equilibrium in a Model of Self-Fulfilling Currency Attacks," American Economic Review, 88, 587-597.
Heinemann, Frank (2000), “Unique Equilibrium in a Model of Self-Fulfilling Currency Attacks: Comment," American Economic Review 90, 316-318.
Hellwig, Christian (2002), “Public Information, Private Information, and the Multiplicity of Equilibria in Coordination Games," Journal of Economic Theory 107, pp. 191-222.
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Experiments:
| Heinemann, F., R. Nagel, and P. Ockenfels (2004), “The Theory of Global Games on Test: Experimental Analysis of Coordination Games with Public and Private Information," Econometrica 72 (5), 2004, pp. 1583-1599.
Cornand C. (2006), “Speculative Attacks and Informational Structure: An Experimental Study," Review of International Economics 14, 797-817. |
Rosemarie Nagel |
This lecture introduces the methods of experimental economics. We will discuss what is an economic experiment (field vs lab experiment), the different areas in experimental economics and behavioral economics, the link between experimental economics, theory and empirical work and important design issues. This introduction is meant to give a quick introduction to those who have never followed an experimental economic course. Prior to the course we will send the partipants of the summer school some classical experiments which they can do online.
| Akerlof, G.A. (2002), “Behavioral Macroeconomics and Macroeconomic Behavior, “American Economic Review," 92. 411-433.
Camerer, C. (2003), “Behavioral Game Theory," Princeton University Press
Friedman, D. and Sunder, S. (1994), Experimental Methods. Cambridge Univ. Press: Chapters 1-2: 1-20.
Roth, A.E. (1995), Introduction to Experimental Economics. In: Kagel, J.H. and Roth, A.E. (eds.): Handbook of Experimental Economics. Princeton Univ. Press: Princeton, N.J., Chapter 1: 3-109.
Plott, C. and Smith, V. (2003), Handbook of Experimental Economics Results, North-Holland, Amsterdam.
Porter, D. and Smith, V. L.Samuelson, L. (2005), “Economic Theory and Experimental Economics," Journal of Economic Literature 43(1): 65-107.
Smith, V.L. (2002), “Method in Experiment: Rhetoric and Reality." Experimental Economics 5(2): 91-110.
Special issue (2005), Experiment, Theory, World: A Symposium on the Role of Experiments in Economics. Journal of Economic Methodology 12(2)
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| Williams, A.W. (1987), “The Formation of Price Forecasts in Experimental Markets," Journal of Money, Credit and Banking 19, 1-18.
Dwyer, Jr., G.P., A.W. Williams, R.C. Battalio and T.I. Mason (1993), “Tests of Rational Expectations in a Stark Setting," Economic Journal 103, 586-601.
Marimon, R. and S. Sunder (1993) “Indeterminacy of Equilibria in a Hyperinflationary World: Experimental Evidence," Econometrica 61, 1073-1107.
Hommes, C.H., J. Sonnemans, J. Tuinstra and H. van de Velden (2007), “Learning in Cobweb Experiments," Macroeconomic Dynamics 11 (Supplement 1), 8-33.
Camerer, C. F. (2003). Chapter 5, Dominance Solvable Games. Behavioral game theory: Experiments on strategic interaction. Princeton, Princeton University Press.
Nagel Rosemarie (1995), “Unraveling in Guessing Games: An Experimental Study." American Economic Review 85,5, 1313-1326. |
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