Hypotheses Testing on a Multivariate Null Intercept Errors-in-Variables Model

Considering the Wald, score, and likelihood ratio asymptotic test statistics, we analyze a multivariate null intercept errors-in-variables regression model, where the explanatory and the response variables are subject to measurement errors, and a possible structure of dependency between the measurements taken within the same individual are incorporated, representing a longitudinal structure. This model was proposed by Aoki et al. (2003b) and analyzed under the bayesian approach. In this article, considering the classical approach, we analyze asymptotic test statistics and present a simulation study to compare the behavior of the three test statistics for different sample sizes, parameter values and nominal levels of the test. Also, closed form expressions for the score function and the Fisher information matrix are presented. We consider two real numerical illustrations, the odontological data set from Hadgu and Koch (1999), and a quality control data set.

Random sums of exchangeable variables and actuarial applications

In this paper we study the accumulated claim in some fixed time period, skipping the classical assumption of mutual independence between the variables involved. Two basic models are considered: Model I assumes that any pair of claims are equally correlated which means that the corresponding square-integrable sequence is exchangeable one. Model 2 states that the correlations between the adjacent claims are the same. Recurrence and explicit expressions for the joint probability generating function are derived and the impact of the dependence parameter (correlation coefficient) in both models is examined. The Markov binomial distribution is obtained as a particular case under assumptions of Model 2. (C) 2007 Elsevier B.V. All rights reserved.

Improved maximum likelihood estimators in a heteroskedastic errors-in-variables model

This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in areas such as astrophysics, epidemiology and analytical chemistry, where the variables are subject to measurement errors and the variances vary with the observations. We conduct Monte Carlo simulations to investigate the performance of the corrected estimators. The numerical results show that the bias correction scheme yields nearly unbiased estimates. We also give an application to a real data set.

A multivariate ultrastructural errors-in-variables model with equation error

This paper deals with asymptotic results on a multivariate ultrastructural errors-in-variables regression model with equation errors Sufficient conditions for attaining consistent estimators for model parameters are presented Asymptotic distributions for the line regression estimators are derived Applications to the elliptical class of distributions with two error assumptions are presented The model generalizes previous results aimed at univariate scenarios (C) 2010 Elsevier Inc All rights reserved

Hypothesis testing in an errors-in-variables model with heteroscedastic measurement errors

In many epidemiological studies it is common to resort to regression models relating incidence of a disease and its risk factors. The main goal of this paper is to consider inference on such models with error-prone observations and variances of the measurement errors changing across observations. We suppose that the observations follow a bivariate normal distribution and the measurement errors are normally distributed. Aggregate data allow the estimation of the error variances. Maximum likelihood estimates are computed numerically via the EM algorithm. Consistent estimation of the asymptotic variance of the maximum likelihood estimators is also discussed. Test statistics are proposed for testing hypotheses of interest. Further, we implement a simple graphical device that enables an assessment of the model`s goodness of fit. Results of simulations concerning the properties of the test statistics are reported. The approach is illustrated with data from the WHO MONICA Project on cardiovascular disease. Copyright (C) 2008 John Wiley & Sons, Ltd.

A Note on Proxy Variables and Instrument Variable Regression

This is a note about proxy variables and instruments for identification of structural parameters in regression models. We have experienced that in the econometric textbooks these two issues are treated separately, although in practice these two concepts are very often combined. Usually, proxy variables are inserted in instrument variable regressions with the motivation they are exogenous. Implicitly meaning they are exogenous in a reduced form model and not in a structural model. Actually if these variables are exogenous they should be redundant in the structural model, e.g. IQ as a proxy for ability. Valid proxies reduce unexplained variation and increases the efficiency of the estimator of the structural parameter of interest. This is especially important in situations when the instrument is weak. With a simple example we demonstrate what is required of a proxy and an instrument when they are combined. It turns out that when a researcher has a valid instrument the requirements on the proxy variable is weaker than if no such instrument exists

Meteomap: Generation Of Meteorological Variables For Distributed Physically-Based Hydrological Modeling

Distributed energy and water balance models require time-series surfaces of the meteorological variables involved in hydrological processes. Most of the hydrological GIS-based models apply simple interpolation techniques to extrapolate the point scale values registered at weather stations at a watershed scale. In mountainous areas, where the monitoring network ineffectively covers the complex terrain heterogeneity, simple geostatistical methods for spatial interpolation are not always representative enough, and algorithms that explicitly or implicitly account for the features creating strong local gradients in the meteorological variables must be applied. Originally developed as a meteorological pre-processing tool for a complete hydrological model (WiMMed), MeteoMap has become an independent software. The individual interpolation algorithms used to approximate the spatial distribution of each meteorological variable were carefully selected taking into account both, the specific variable being mapped, and the common lack of input data from Mediterranean mountainous areas. They include corrections with height for both rainfall and temperature (Herrero et al., 2007), and topographic corrections for solar radiation (Aguilar et al., 2010). MeteoMap is a GIS-based freeware upon registration. Input data include weather station records and topographic data and the output consists of tables and maps of the meteorological variables at hourly, daily, predefined rainfall event duration or annual scales. It offers its own pre and post-processing tools, including video outlook, map printing and the possibility of exporting the maps to images or ASCII ArcGIS formats. This study presents the friendly user interface of the software and shows some case studies with applications to hydrological modeling.

Descentralizacion y participacion: dos variables democraticas tensiones y distennsiones en el sector salud, referencias a Brasil y Colombia

Este estudo tem, como tema central, a análise da descentralização e da participação como categorias democratizantes da reforma do Estado, particularmente, na área das políticas de saúde no Brasil e na Colômbia. Foi realizada uma análise teórica de ambas as categorias e de seu impacto na reformulação da relação Estado e sociedade para examinar, no último capítulo, sua conjunção na formulação, implementação e controle das políticas de saúde. Os resultados obtidos permitiram elaborar um marco analítico de gradação dos níveis de descentralização e participação assim como a importância destas na prática de uma gestão de saúde mais democrática.

Price setting and macroeconomic variables: evidence from Brazilian CPI

Esta tese investiga as estratégias de precificação em ambientes macroeconômicos distintos, utilizando uma base de dados única para o IPC da Fundação Getulio Vargas. A base de dados primária consiste em um painel de dados individuais para bens e serviços representando 100% do IPC para o período de 1996 a 2008. Durante este período, diversos eventos produziram uma variabilidade macroeconômica substancial no Brasil: duas crises em países emergentes, uma mudança de regime cambial e monetário, racionamento de energia, uma crise de expectativas eleitorais e um processo de desinflação. Como consequência, a inflação, a incerteza macroeconômica, a taxa de câmbio e o produto exibiram uma variação considerável no período. No primeiro capítulo, nós descrevemos a base de dados e apresentamos as principais estatísticas de price-setting para o Brasil. Em seguida, nos capítulos 2 e 3, nos construímos as séries de tempo destas estatísticas e das estatísticas de promoções, e as relacionamos com as variáveis macroeconômicas utilizando análises de regressões. Os resultados indicam que há uma relação substancial entre as estatísticas de price-setting e o ambiente macroeconômico para a economia brasileira.

A study of the impacts of quantitative easing on the macroeconomics variables

Neste trabalho, propusemos um modelo DSGE que busca responder algumas questões sobre políticas de afrouxamento monetário (Quantitative Easing - QE) recentemente implementadas em resposta à crise de 2008. Desenvolvemos um modelo DSGE com agentes heterogêneos e preferred-habitat nas compras de títulos do governo. Nosso modelo permite o estudo da otimalidade da compra de portfolio (em termos de duration dos títulos) para os bancos centrais quando estão implementando a política. Além disso, a estrutura heterogênea nos permite olhar para distribuição de renda provocada pelas compras de títulos. Nossos resultados preliminares evidenciam o efeito distributivo do QE. No entanto, nosso modelo expandido apresentou alguns problemas de estabilidade.