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Robust Permanent Income and Pricing with Filtering
, 2000
"... A planner and agent in a permanent income economy cannot observe part of the state, regard their model as an approximation, and value decision rules that are robust across a set of models. They use robust decision theory to choose allocations. Equilibrium prices reflect the preference for robustness ..."
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Cited by 38 (14 self)
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A planner and agent in a permanent income economy cannot observe part of the state, regard their model as an approximation, and value decision rules that are robust across a set of models. They use robust decision theory to choose allocations. Equilibrium prices reflect the preference for robustness and so embody a `market price of Knightian uncertainty'. We compute market prices of risk and compare them with a model that assumes that the state is fully observed. We use detection error probabilities to constrain a single parameter that governs the taste for robustness.
Axiomatic foundations of multiplier preferences
, 2007
"... This paper axiomatizes the robust control criterion of multiplier preferences introduced by Hansen and Sargent (2001). The axiomatization relates multiplier preferences to other classes of preferences studied in decision theory. Some properties of multiplier preferences are generalized to the broade ..."
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Cited by 33 (3 self)
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This paper axiomatizes the robust control criterion of multiplier preferences introduced by Hansen and Sargent (2001). The axiomatization relates multiplier preferences to other classes of preferences studied in decision theory. Some properties of multiplier preferences are generalized to the broader class of variational preferences, recently introduced by Maccheroni, Marinacci and Rustichini (2006). The paper also establishes a link between the parameters of the multiplier criterion and the observable behavior of the agent. This link enables measurement of the parameters on the basis of observable choice data and provides a useful tool for applications. I am indebted to my advisor Eddie Dekel for his continuous guidance, support, and encouragement. I am grateful to Peter Klibanoff and Marciano Siniscalchi for many discussions which resulted in significant improvements of the paper. I would also like to thank Jeff Ely and Todd Sarver for helpful comments and suggestions. This project started after a very stimulating conversation with Tom Sargent and was further shaped by conversations with Lars Hansen. All errors are my own.
Robust Control and Filtering of ForwardLooking Models
, 2000
"... This paper shows how to compute robust Ramsey (aka Stackelberg) plans for linear models with forward looking private agents. We formulate a Bellman equation for the robust plan. We describe robust filtering for when some of the forcing variables (like potential GDP or trend growth) are not observed, ..."
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Cited by 12 (0 self)
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This paper shows how to compute robust Ramsey (aka Stackelberg) plans for linear models with forward looking private agents. We formulate a Bellman equation for the robust plan. We describe robust filtering for when some of the forcing variables (like potential GDP or trend growth) are not observed, and how the decision problem interacts with the filtering problem. We use a ‘new synthesis’ macro model of Woodford as an example.
Certainty equivalence and model uncertainty
 Federal Reserve Board Proceedings
, 2005
"... ABSTRACT Simon's and Theil's certainty equivalence property justifies a convenient algorithm for solving dynamic programming problems with quadratic objectives and linear transition laws: first, optimize under perfect foresight, then substitute optimal forecasts for unknown future values. ..."
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Cited by 4 (0 self)
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ABSTRACT Simon's and Theil's certainty equivalence property justifies a convenient algorithm for solving dynamic programming problems with quadratic objectives and linear transition laws: first, optimize under perfect foresight, then substitute optimal forecasts for unknown future values. A similar decomposition into separate optimization and forecasting steps prevails when a decision maker wants a decision rule that is robust to model misspecification. Concerns about model misspecification leave the first step of the algorithm intact and affect only the second step of forecasting the future. The decision maker attains robustness by making forecasts with a distorted model that twists probabilities relative to his approximating model. The appropriate twisting emerges from a twoplayer zerosum dynamic game.
Northwestern University
, 2005
"... We find a necessary and sufficient condition for exante trade when agents are nonexpected utility maximizers. The condition is that they share subjective beliefs. Our result holds for a class of convex preferences that contains many functional forms used in applications. In a special case of expec ..."
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We find a necessary and sufficient condition for exante trade when agents are nonexpected utility maximizers. The condition is that they share subjective beliefs. Our result holds for a class of convex preferences that contains many functional forms used in applications. In a special case of expected utility, the condition becomes exactly the common prior assumption. It can also be articulated in the language of other functional forms, confirming results existing in the literature, generating new results, and providing a useful tool for applications. Another contribution of this paper is a characterization of a general definition of beliefs for convex preferences. We show that this definition can be characterized in terms of market behavior. Moreover, it coincides with the usual one for an important class of convex invariant biseparable preferences. 1
Acknowledging Misspecification In Macroeconomic Theory
, 2001
"... . We explore methods for confronting model misspecication in macroeconomics. We construct dynamic equilibria in which private agents and policy makers recognize that models are approximations. We explore two generalizations of rational expectations equilibria. In one of these equilibria, decision ..."
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. We explore methods for confronting model misspecication in macroeconomics. We construct dynamic equilibria in which private agents and policy makers recognize that models are approximations. We explore two generalizations of rational expectations equilibria. In one of these equilibria, decisionmakers use dynamic evolution equations that are imperfect statistical approximations, and in the other misspecication is impossible to detect even from innite samples of time series data. In the rst of these equilibria, decision rules are tailored to be robust to the allowable statistical discrepancies. Using frequency domain methods, we show that robust decisionmakers treat model misspecication like time series econometricians. 1. Rational expectations versus misspecification Subgame perfect and rational expectations equilibrium models do not permit a selfcontained analysis of model misspecication. But sometimes model builders suspect misspecication, and so might the agent...