EconStor Community: Sonderforschungsbereich 373: Quantification and Simulation of Economic Processes, Humboldt-Universität Berlin
http://hdl.handle.net/10419/129
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Optional decomposition and lagrange multipliers
http://hdl.handle.net/10419/66314
Title: Optional decomposition and lagrange multipliers
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<br/>Authors: Föllmer, Hans; Kabanov, Jurij M.
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<br/>Abstract: Let Q be the set of equivalent martingale measures for a given process S, and let X be a process which is a local supermartingale with respect to any measure in Q. The optional decomposition theorem for X states that there exists a predictable integrand ф such that the difference X−ф•S is a decreasing process. In this paper we give a new proof which uses techniques from stochastic calculus rather than functional analysis, and which removes any boundedness assumption.Indirect evolution versus strategic delegation: A comparison of two approaches to explaining economic institutions
http://hdl.handle.net/10419/66313
Title: Indirect evolution versus strategic delegation: A comparison of two approaches to explaining economic institutions
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<br/>Authors: Dufwenberg, Martin; Güth, Werner
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<br/>Abstract: The two major methods of explaining economic institutions, namely by strategic choices or by (indirect) evolution, are compared for the case of a homogenous quadratic duopoly market. Sellers either can provide incentives for their agents to care for sales (amounts) or evolve as sellers who care for sales in addition to profits. Whereas strategic delegation does not change the market results as compared to the usual duopoly solution, indirect evolution causes a more competitive behavior. Thus the case at hand suffices to demonstrate the difference between the two approaches in explaining economic institutions.Wild bootstrap versus moment-oriented bootstrap
http://hdl.handle.net/10419/66312
Title: Wild bootstrap versus moment-oriented bootstrap
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<br/>Authors: Sommerfeld, Volker
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<br/>Abstract: We investigate the relative merits of a moment-oriented bootstrap method of Bunke (1997) in comparison with the classical wild bootstrap of Wu (1986) in nonparametric heteroscedastic regression situations. The moment-oriented bootstrap is a wild bootstrap based on local estimators of higher order error moments that are smoothed by kernel smoothers. In this paper we perform an asymptotic comparison of these two dierent bootstrap procedures. We show that the moment-oriented bootstrap is in no case worse than the wild bootstrap. We consider the cases of bandwidths with MISE-optimal rates and of bandwidths with rates that perform an optimal bootstrap approximation. When the regression function has the same amount of smoothness as the second and the third order error moment, then it turns out that, in the former case, our method better approximates the distribution of the pivotal statistic than the usual wild bootstrap does. The reason for this behavior is the unavoidable bias in nonparametric regression estimation that permits only a suboptimal amount of smoothing in the classical wild bootstrap case. In the latter case we need more smoothness of the error moments to make the moment-oriented bootstrap better than wild bootstrap. These results are applied to the construction of pointwise confidence intervals where we prove that our bootstrap has a superior behavior for equal smoothness of the regression function and error moments.Polynomial regression and estimation function in the presence of multiplication measurement error, with application to nutrition
http://hdl.handle.net/10419/66311
Title: Polynomial regression and estimation function in the presence of multiplication measurement error, with application to nutrition
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<br/>Authors: Iturria, Stephen J.; Carroll, Raymond J.; Firth, David
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<br/>Abstract: In this paper we consider the polynomial regression model in the presence of multiplicative measurement error in the predictor. Consistent parameter estimates and their associated standard errors are derived. Two general methods are considered, with the methods differing in their assumptions about the distributions of the predictor and the measurement errors. Data from a nutrition study are analyzed using the methods. Finally, the results from a simulation study are presented and the performances of the methods compared.