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作者：Andrews, Donald W. K.; Ely, Jeffrey C.
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作者：Sadzik, Tomasz; Stacchetti, Ennio
作者单位：University of California System; University of California Los Angeles; New York University
摘要：The paper analyzes dynamic principal-agent models with short period lengths. The two main contributions are: (i) an analytic characterization of the values of optimal contracts in the limit as the period length goes to 0, and (ii) the construction of relatively simple (almost) optimal contracts for fixed period lengths. Our setting is flexible and includes the pure hidden action or pure hidden information models as special cases. We show how such details of the underlying information structure...
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作者：Matzkin, Rosa L.
作者单位：University of California System; University of California Los Angeles
摘要：We introduce methods for estimating nonparametric, nonadditive models with simultaneity. The methods are developed by directly connecting the elements of the structural system to be estimated with features of the density of the observable variables, such as ratios of derivatives or averages of products of derivatives of this density. The estimators are therefore easily computed functionals of a nonparametric estimator of the density of the observable variables. We consider in detail a model wh...
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作者：Kochov, Asen
作者单位：University of Rochester
摘要：This paper axiomatizes an intertemporal version of the maxmin expected-utility model. It employs two axioms specific to a dynamic setting. The first requires that smoothing consumption across states of the world is more beneficial to the individual than smoothing consumption across time. Such behavior is viewed as the intertemporal manifestation of ambiguity aversion. The second axiom extends Koopmans' notion of stationarity from deterministic to stochastic environments.
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作者：Pakes, A.; Porter, J.; Ho, Kate; Ishii, Joy
作者单位：Harvard University; University of Wisconsin System; University of Wisconsin Madison; Columbia University
摘要：This paper provides conditions under which the inequality constraints generated by either single agent optimizing behavior or the best response condition of multiple agent problems can be used as a basis for estimation and inference. An application illustrates how the use of these inequality constraints can simplify the analysis of complex behavioral models.
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作者：Reny, Philip J.
作者单位：University of Chicago
摘要：For an arbitrary data set D = {(p, x)} ((m)(+)\ {0}) x (m)(+), finite or infinite, it is shown that the following three conditions are equivalent: (a) D satisfies GARP; (b) D can be rationalized by a utility function; (c) D can be rationalized by a utility function that is quasiconcave, nondecreasing, and that strictly increases when all its coordinates strictly increase. Examples of infinite data sets satisfying GARP are provided for which every utility rationalization fails to be lower semic...
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作者：Salanie, Bernard
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作者：Hanlon, W. Walker
作者单位：University of California System; University of California Los Angeles; National Bureau of Economic Research
摘要：This study provides causal evidence that a shock to the relative supply of inputs to production can (1) affect the direction of technological progress and (2) lead to a rebound in the relative price of the input that became relatively more abundant (the strong induced-bias hypothesis). I exploit the impact of the U.S. Civil War on the British cotton textile industry, which reduced supplies of cotton from the Southern United States, forcing British producers to shift to lower-quality Indian cot...
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作者：Noguchi, Yuichi
作者单位：Kanto Gakuin University
摘要：In this paper, I construct players' prior beliefs and show that these prior beliefs lead the players to learn to play an approximate Nash equilibrium uniformly in any infinitely repeated slightly perturbed game with discounting and perfect monitoring. That is, given any epsilon > 0, there exists a (single) profile of players' prior beliefs that leads play to almost surely converge to an epsilon-Nash equilibrium uniformly for any (finite normal form) stage game with slight payoff perturbation a...
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作者：Salanie, Bernard
