A general framework is described specifying how boundedly rational decision makers generate their choices. Starting from a "Master Module" which keeps an inventory of previously successful and unsuccessful routines several submodules can be called forth which either allow one to adjust behavior...

The general framework of decision emergence (Güth, 2000a) is applied to the specific decision task of a proposer in ultimatum bargaining, i.e. to choosing how much the responder should be offered. For this purpose the "Master Module" as well as its submodules "New Problem Solver", "Adaptation...

Robust learning experiments confront participants with structurally different decision environments which they encounter, furthermore, repeatedly. Since the decision format does not depend on the rules (of game), forward looking deliberation (the shadow of the future) can be detected by...

Making use of restrictions imposed by equilibrium, theoretical progress has been made on the nonparametric and semiparametric estimation and identification of scalar additive hedonic models (Ekeland, Heckman, and Nesheim, 2002) and scalar nonadditive hedonic models (Heckman, Matzkin, and...

This paper considers the identification and estimation of hedonic models. We establish that in an additive version of the hedonic model, technology and preferences are generically identified up to affine transformations from data on demand and supply in a single hedonic market. For a very...

This paper tests the validity of Present Value (PV) models of stock prices by employing a two-step strategy for testing the null hypothesis of no cointegration against alternatives which are fractionally cointegrated. Monte Carlo simulations are conducted to evaluate the power and size...

This paper examines the relationship between unemployment, real oil price and real interest rates in Canada. Instead of following the classical approach based on I(0) stationarity or I(1) cointegrating relationships, we use fractional integration/cointegration techniques which allow for the...