Explosive Hyperinflation, Inflation Tax Laffer Curve and Modelling the use of Money

This paper analyzes the existence of an inflation tax Laffer curve (ITLC) in the context of two standard optimizing monetary models: a cash-in-advance model and a money in the utility function model. Agents’ preferences are characterized in the two models by a constant relative risk aversion utility function. Explosive hyperinflation rules out the presence of an ITLC. In the context of a cash-in-advance economy, this paper shows that explosive hyperinflation is feasible and thus an ITLC is ruled out whenever the relative risk aversion parameter is greater than one. In the context of an optimizing model with money in the utility function, this paper firstly shows that an ITLC is ruled out. Moreover, it is shown that explosive hyperinflations are more likely when the transactions role of money is more important. However, hyperinflationary paths are not feasible in this context unless certain restrictions are imposed.

A Supply Function Competition Model for the Spanish Wholesale Electricity Market

We model the Spanish wholesale market as a multiplant linear supply function competition model. According to the theory, the larger generators should have supply curves for each plant which are to the left of the supply curves of plants owned by smaller generators. We test this prediction for fuel plants using data from the Spanish Market Operator (OMEL) from May 2001 to December 2003. Our results indicate that the prediction of the model holds.

New methods for generating populations in Markov network based EDAs: Decimation strategies and model-based template recombination

Methods for generating a new population are a fundamental component of estimation of distribution algorithms (EDAs). They serve to transfer the information contained in the probabilistic model to the new generated population. In EDAs based on Markov networks, methods for generating new populations usually discard information contained in the model to gain in efficiency. Other methods like Gibbs sampling use information about all interactions in the model but are computationally very costly. In this paper we propose new methods for generating new solutions in EDAs based on Markov networks. We introduce approaches based on inference methods for computing the most probable configurations and model-based template recombination. We show that the application of different variants of inference methods can increase the EDAs’ convergence rate and reduce the number of function evaluations needed to find the optimum of binary and non-binary discrete functions.

Early Functional Deficit and Microglial Disturbances in a Mouse Model of Amyotrophic Lateral Sclerosis

11 p.

The DICER Model: Methodological Issues and Initial Results

31 p.

Climate change impacts on the water services in Costa Rica: a production function for the hydroenergy sector

23 p.