22 resultados para Rational approximations


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This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selecting (DMS) or averaging (DMA) in time-varying parameter regression models. DMS methods allow for model switching, where a different model can be chosen at each point in time. Thus, they allow for the explanatory variables in the time-varying parameter regression model to change over time. DMA will carry out model averaging in a time-varying manner. We compare our exact approach to DMA/DMS to a popular existing procedure which relies on the use of forgetting factor approximations. In an application, we use DMS to select different predictors in an in ation forecasting application. We also compare different ways of implementing DMA/DMS and investigate whether they lead to similar results.

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A number of different models with behavioral economics have a reduced form representation where potentially boundedly rational decision-makers do not necessarily internalize all the consequences of their actions on payoff relevant features (which we label as psychological states) of the choice environment. This paper studies the restrictions that such behavioral models impose on choice data and the implications they have for welfare analysis. First, we propose a welfare benchmark that is justified using standard axioms of rational choice and can be applied to a number of existing seminal behavioral economics models. Second, we show that Sen's axioms and fully characterize choice data consistent with behavioral decision-makers. Third, we show how choice data to infer information about the normative signi.cance of psychological states and establish the possibility of identifying welfare dominated choices.

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I develop a model of endogenous bounded rationality due to search costs, arising implicitly from the problems complexity. The decision maker is not required to know the entire structure of the problem when making choices but can think ahead, through costly search, to reveal more of it. However, the costs of search are not assumed exogenously; they are inferred from revealed preferences through her choices. Thus, bounded rationality and its extent emerge endogenously: as problems become simpler or as the benefits of deeper search become larger relative to its costs, the choices more closely resemble those of a rational agent. For a fixed decision problem, the costs of search will vary across agents. For a given decision maker, they will vary across problems. The model explains, therefore, why the disparity, between observed choices and those prescribed under rationality, varies across agents and problems. It also suggests, under reasonable assumptions, an identifying prediction: a relation between the benefits of deeper search and the depth of the search. As long as calibration of the search costs is possible, this can be tested on any agent-problem pair. My approach provides a common framework for depicting the underlying limitations that force departures from rationality in different and unrelated decision-making situations. Specifically, I show that it is consistent with violations of timing independence in temporal framing problems, dynamic inconsistency and diversification bias in sequential versus simultaneous choice problems, and with plausible but contrasting risk attitudes across small- and large-stakes gambles.

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We derive a rational model of separable consumer choice which can also serve as a behavioral model. The central construct is [lambda] , the marginal utility of money, derived from the consumer's rest-of-life problem. We present a robust approximation of [lambda], and show how to incorporate liquidity constraints, indivisibilities and adaptation to a changing environment. We fi nd connections with numerous historical and recent constructs, both behavioral and neoclassical, and draw contrasts with standard partial equilibrium analysis. The result is a better grounded, more flexible and more intuitive description of consumer choice.

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Interaction, the act of mutual influence between two or more individuals, is an essential part of daily life and economic decisions. Yet, micro-foundations of interaction are unexplored. This paper presents a first attempt to this purpose. We study a decision procedure for interacting agents. According to our model, interaction occurs since individuals seek influence for those issues that they cannot solve on their own. Following a choice-theoretic approach, we provide simple properties that aid to detect interacting individuals. In this case, revealed preference analysis not only grants the underlying preferences but also the influence acquired. Our baseline model is based on two interacting individuals, though we extend the analysis to multi-individual environments.

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Classical definitions of complementarity are based on cross price elasticities, and so they do not apply, for example, when goods are free. This context includes many relevant cases such as online newspapers and public attractions. We look for a complementarity notion that does not rely on price variation and that is: behavioural (based only on observable choice data); and model-free (valid whether the agent is rational or not). We uncover a conflict between properties that complementarity should intuitively possess. We discuss three ways out of the impossibility.

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The traditional view of the economic agent is of an individual who is self-interested, rational and perceives the world “correctly”. However, there is a lot of experimental and other evidence that undermines this view of agents. It is argued that an attempt to model these agents properly requires the cognitive science idea of a mental representation- a mental state with content. It is shown that this idea gives economists resources to discuss critiques of economics by Sandel (2012) and Grant (2012). In particular, it allows us to think clearly about the notion of goods being “corrupted” by a change in context from a non-market to a market situation.