Firm-specific investment, sticky prices and the Taylor principle

According to the Taylor principle a central bank should adjust the nominal interest rate by more than one-for-one in response to changes in current inflation. Most of the existing literature supports the view that by following this simple recommendation a central bank can avoid being a source of unnecessary fluctuations in economic activity. The present paper shows that this conclusion is not robust with respect to the modelling of capital accumulation. We use our insights to discuss the desirability of alternative interest raterules. Our results suggest a reinterpretation of monetary policy under Volcker and Greenspan: The empirically plausible characterization of monetary policy can explain the stabilization of macroeconomic outcomes observed in the early eighties for the US economy. The Taylor principle in itself cannot.

A new chance-constrained maximum capture location problem

The paper presents a new model based on the basic Maximum Capture model,MAXCAP. The New Chance Constrained Maximum Capture modelintroduces astochastic threshold constraint, which recognises the fact that a facilitycan be open only if a minimum level of demand is captured. A metaheuristicbased on MAX MIN ANT system and TABU search procedure is presented tosolve the model. This is the first time that the MAX MIN ANT system isadapted to solve a location problem. Computational experience and anapplication to 55 node network are also presented.

On the receiver pays principle

This paper extends the theory of network competition betweentelecommunications operators by allowing receivers to derive a surplusfrom receiving calls (call externality) and to affect the volume ofcommunications by hanging up (receiver sovereignty). We investigate theextent to which receiver charges can lead to an internalization of thecalling externality. When the receiver charge and the termination(access) charge are both regulated, there exists an e±cient equilibrium.Effciency requires a termination discount. When reception charges aremarket determined, it is optimal for each operator to set the prices foremission and reception at their off-net costs. For an appropriately chosentermination charge, the symmetric equilibrium is again effcient. Lastly,we show that network-based price discrimination creates strong incentivesfor connectivity breakdowns, even between equal networks.

Nonlinear models and small sample performance of the generalized method of moments

In this paper I explore the issue of nonlinearity (both in the datageneration process and in the functional form that establishes therelationship between the parameters and the data) regarding the poorperformance of the Generalized Method of Moments (GMM) in small samples.To this purpose I build a sequence of models starting with a simple linearmodel and enlarging it progressively until I approximate a standard (nonlinear)neoclassical growth model. I then use simulation techniques to find the smallsample distribution of the GMM estimators in each of the models.

Statistical learning theory for geospatial data. Case study: Aral sea

In recent years there has been an explosive growth in the development of adaptive and data driven methods. One of the efficient and data-driven approaches is based on statistical learning theory (Vapnik 1998). The theory is based on Structural Risk Minimisation (SRM) principle and has a solid statistical background. When applying SRM we are trying not only to reduce training error ? to fit the available data with a model, but also to reduce the complexity of the model and to reduce generalisation error. Many nonlinear learning procedures recently developed in neural networks and statistics can be understood and interpreted in terms of the structural risk minimisation inductive principle. A recent methodology based on SRM is called Support Vector Machines (SVM). At present SLT is still under intensive development and SVM find new areas of application (www.kernel-machines.org). SVM develop robust and non linear data models with excellent generalisation abilities that is very important both for monitoring and forecasting. SVM are extremely good when input space is high dimensional and training data set i not big enough to develop corresponding nonlinear model. Moreover, SVM use only support vectors to derive decision boundaries. It opens a way to sampling optimization, estimation of noise in data, quantification of data redundancy etc. Presentation of SVM for spatially distributed data is given in (Kanevski and Maignan 2004).

Maximum-likelihood estimation of specific differential phase and attenuation in rain

Precise estimation of propagation parameters inprecipitation media is of interest to improve the performanceof communications systems and in remote sensing applications.In this paper, we present maximum-likelihood estimators ofspecific attenuation and specific differential phase in rain. Themodel used for obtaining the cited estimators assumes coherentpropagation, reflection symmetry of the medium, and Gaussianstatistics of the scattering matrix measurements. No assumptionsabout the microphysical properties of the medium are needed.The performance of the estimators is evaluated through simulateddata. Results show negligible estimators bias and variances closeto Cramer–Rao bounds.

Avaliação de Panicum maximum jacq. para tolerância ao alumínio em solução nutritiva

Em condições controladas de temperatura, umidade e iluminação, 30 genótipos de Panicum maximum foram avaliados para a verificação de tolerância às doses de alumínio de 0, 12 e 24 mg L-1 em solução nutritiva. Os efeitos do alumínio na inibição do alongamento radicular e no índice de tolerância possibilitaram a estratificação dos genótipos em três categorias: tolerantes, intermediários e sensíveis. A maior parte dos genótipos apresentou de média a baixa tolerância ao alumínio, destacando-se, como os mais tolerantes, os genótipos K191, T95, T84, T91 e Centenário e, como os mais sensíveis, os genótipos Centauro, K68, K214 e T46.

CP2 stars as viewed by the uvby Hbeta system

A new statistical parallax method using the Maximum Likelihood principle is presented, allowing the simultaneous determination of a luminosity calibration, kinematic characteristics and spatial distribution of a given sample. This method has been developed for the exploitation of the Hipparcos data and presents several improvements with respect to the previous ones: the effects of the selection of the sample, the observational errors, the galactic rotation and the interstellar absorption are taken into account as an intrinsic part of the formulation (as opposed to external corrections). Furthermore, the method is able to identify and characterize physically distinct groups in inhomogeneous samples, thus avoiding biases due to unidentified components. Moreover, the implementation used by the authors is based on the extensive use of numerical methods, so avoiding the need for simplification of the equations and thus the bias they could introduce. Several examples of application using simulated samples are presented, to be followed by applications to real samples in forthcoming articles.

Statistical analysis of maximum likelihood estimator images of human brain FDG PET studies

The work presented evaluates the statistical characteristics of regional bias and expected error in reconstructions of real positron emission tomography (PET) data of human brain fluoro-deoxiglucose (FDG) studies carried out by the maximum likelihood estimator (MLE) method with a robust stopping rule, and compares them with the results of filtered backprojection (FBP) reconstructions and with the method of sieves. The task of evaluating radioisotope uptake in regions-of-interest (ROIs) is investigated. An assessment of bias and variance in uptake measurements is carried out with simulated data. Then, by using three different transition matrices with different degrees of accuracy and a components of variance model for statistical analysis, it is shown that the characteristics obtained from real human FDG brain data are consistent with the results of the simulation studies.

A fully adaptive numerical approximation for a two-dimensional epidemic model with nonlinear cross-diffusion

An epidemic model is formulated by a reactionâeuro"diffusion system where the spatial pattern formation is driven by cross-diffusion. The reaction terms describe the local dynamics of susceptible and infected species, whereas the diffusion terms account for the spatial distribution dynamics. For both self-diffusion and cross-diffusion, nonlinear constitutive assumptions are suggested. To simulate the pattern formation two finite volume formulations are proposed, which employ a conservative and a non-conservative discretization, respectively. An efficient simulation is obtained by a fully adaptive multiresolution strategy. Numerical examples illustrate the impact of the cross-diffusion on the pattern formation.

Atributos químicos e físicos de um argissolo cultivado com Panicum maximum Jacq. cv. IPR-86 Milênio, sob lotação rotacionada e adubado com nitrogênio

A avaliação dos atributos químicos e físicos do solo é necessária em estudos que exploram intensivamente os sistemas de pastejo. No presente trabalho, objetivou-se avaliar as variações nos atributos físicos e químicos de um Argissolo cultivado com capim-milênio, sob pastejo, no intervalo compreendido entre outubro/2001 e maio/2002, no quinto ano de aplicação sucessiva das seguintes doses de N: 0, 150, 300 e 450 kg ha-1. Após os cinco anos de aplicação de 450 kg ha-1 de N, o teor de MO aumentou em 5 g dm-3, o que resultou em aumento da CTC. No período de outubro/2001 a maio/2002 houve diminuição nos valores de pH e de V com a aplicação de N. A densidade do solo variou pouco com a adubação nitrogenada; a percentagem de microporos aumentou e a de macroporos diminuiu na camada de 0-5 cm com o aumento das doses de N, mas a porosidade total permaneceu constante; e a resistência à penetração, quando o solo apresentava 90 g kg-1 de água, não foi afetada pelas doses de N e não foi restritiva ao crescimento das plantas.

Safe and effective variability-a criterion for dose individualization.

BACKGROUND: : A primary goal of clinical pharmacology is to understand the factors that determine the dose-effect relationship and to use this knowledge to individualize drug dose. METHODS: : A principle-based criterion is proposed for deciding among alternative individualization methods. RESULTS: : Safe and effective variability defines the maximum acceptable population variability in drug concentration around the population average. CONCLUSIONS: : A decision on whether patient covariates alone are sufficient, or whether therapeutic drug monitoring in combination with target concentration intervention is needed, can be made by comparing the remaining population variability after a particular dosing method with the safe and effective variability.

The generalized index of maximum and minimum level and its application in decision making

[spa] El índice del máximo y el mínimo nivel es una técnica muy útil, especialmente para toma de decisiones, que usa la distancia de Hamming y el coeficiente de adecuación en el mismo problema. En este trabajo, se propone una generalización a través de utilizar medias generalizadas y cuasi aritméticas. A estos operadores de agregación, se les denominará el índice del máximo y el mínimo nivel medio ponderado ordenado generalizado (GOWAIMAM) y cuasi aritmético (Quasi-OWAIMAM). Estos nuevos operadores generalizan una amplia gama de casos particulares como el índice del máximo y el mínimo nivel generalizado (GIMAM), el OWAIMAM, y otros. También se desarrolla una aplicación en la toma de decisiones sobre selección de productos.

Suboptimal filtering and nonlinear time scale transformation for the analysis of multiexponential decays

Multiexponential decays may contain time-constants differing in several orders of magnitudes. In such cases, uniform sampling results in very long records featuring a high degree of oversampling at the final part of the transient. Here, we analyze a nonlinear time scale transformation to reduce the total number of samples with minimum signal distortion, achieving an important reduction of the computational cost of subsequent analyses. We propose a time-varying filter whose length is optimized for minimum mean square error