Differentiated effect of advertising: Joint vs. separate consumption

In a context of intense competition, cooperative advertising between firms is critical. Accordingly, the objective of this article is to analyze the potential differentiated effect of advertising on two basic consumption patterns: individual products (i.e. hotel, restaurant) vs. bundle (i.e. hotel + restaurant). This research adds to the extant literature in that, for the first time, this potential differentiated effect is examined through a hierarchical modelling framework that reflects the way people make their decisions: first, they decide whether to visit or not a region; second, whether to purchase an advertised product in that region; and third, whether to buy products together or separately at the region. The empirical analysis, applied to a sample of 11,288 individuals, shows that the influence of advertising is positive for the decisions to visit and to purchase; however, when it comes to the joint or separate consumption, advertising has a differentiated effect: its impact is much greater on the joint alternative (“hotel + restaurant”) than the separate options (“hotel” and “restaurant”). Also, the variable distance moderates the advertising effect.

Branching Processes in Autoregressive Random Environment

2000 Mathematics Subject Classification: 60J80, 60K05.

Asymptotic Approximations in the Near-Integrated Model with a Non-Zero Initial Condition

This paper considers various asymptotic approximations in the near-integrated firstorder autoregressive model with a non-zero initial condition. We first extend the work of Knight and Satchell (1993), who considered the random walk case with a zero initial condition, to derive the expansion of the relevant joint moment generating function in this more general framework. We also consider, as alternative approximations, the stochastic expansion of Phillips (1987c) and the continuous time approximation of Perron (1991). We assess how these alternative methods provide or not an adequate approximation to the finite-sample distribution of the least-squares estimator in a first-order autoregressive model. The results show that, when the initial condition is non-zero, Perron's (1991) continuous time approximation performs very well while the others only offer improvements when the initial condition is zero.

Critical points on growth curves in autoregressive and mixed models

Adjusting autoregressive and mixed models to growth data fits discontinuous functions, which makes it difficult to determine critical points. In this study we propose a new approach to determine the critical stability point of cattle growth using a first-order autoregressive model and a mixed model with random asymptote, using the deterministic portion of the models. Three functions were compared: logistic, Gompertz, and Richards. The Richards autoregressive model yielded the best fit, but the critical growth values were adjusted very early, and for this purpose the Gompertz model was more appropriate.

Statistical learning of random probability measures

The study of random probability measures is a lively research topic that has attracted interest from different fields in recent years. In this thesis, we consider random probability measures in the context of Bayesian nonparametrics, where the law of a random probability measure is used as prior distribution, and in the context of distributional data analysis, where the goal is to perform inference given avsample from the law of a random probability measure. The contributions contained in this thesis can be subdivided according to three different topics: (i) the use of almost surely discrete repulsive random measures (i.e., whose support points are well separated) for Bayesian model-based clustering, (ii) the proposal of new laws for collections of random probability measures for Bayesian density estimation of partially exchangeable data subdivided into different groups, and (iii) the study of principal component analysis and regression models for probability distributions seen as elements of the 2-Wasserstein space. Specifically, for point (i) above we propose an efficient Markov chain Monte Carlo algorithm for posterior inference, which sidesteps the need of split-merge reversible jump moves typically associated with poor performance, we propose a model for clustering high-dimensional data by introducing a novel class of anisotropic determinantal point processes, and study the distributional properties of the repulsive measures, shedding light on important theoretical results which enable more principled prior elicitation and more efficient posterior simulation algorithms. For point (ii) above, we consider several models suitable for clustering homogeneous populations, inducing spatial dependence across groups of data, extracting the characteristic traits common to all the data-groups, and propose a novel vector autoregressive model to study of growth curves of Singaporean kids. Finally, for point (iii), we propose a novel class of projected statistical methods for distributional data analysis for measures on the real line and on the unit-circle.

Multi-trait and random regression mature weight heritability and breeding value estimates in Nelore cattle

Mature weight breeding values were estimated using a multi-trait animal model (MM) and a random regression animal model (RRM). Data consisted of 82 064 weight records from 8 145 animals, recorded from birth to eight years of age. Weights at standard ages were considered in the MM. All models included contemporary groups as fixed effects, and age of dam (linear and quadratic effects) and animal age as covariates. In the RRM, mean trends were modelled through a cubic regression on orthogonal polynomials of animal age and genetic maternal and direct and maternal permanent environmental effects were also included as random. Legendre polynomials of orders 4, 3, 6 and 3 were used for animal and maternal genetic and permanent environmental effects, respectively, considering five classes of residual variances. Mature weight (five years) direct heritability estimates were 0.35 (MM) and 0.38 (RRM). Rank correlation between sires' breeding values estimated by MM and RRM was 0.82. However, selecting the top 2% (12) or 10% (62) of the young sires based on the MM predicted breeding values, respectively 71% and 80% of the same sires would be selected if RRM estimates were used instead. The RRM modelled the changes in the (co) variances with age adequately and larger breeding value accuracies can be expected using this model.

On the basic reproduction number and the topological properties of the contact network: An epidemiological study in mainly locally connected cellular automata

We study the spreading of contagious diseases in a population of constant size using susceptible-infective-recovered (SIR) models described in terms of ordinary differential equations (ODEs) and probabilistic cellular automata (PCA). In the PCA model, each individual (represented by a cell in the lattice) is mainly locally connected to others. We investigate how the topological properties of the random network representing contacts among individuals influence the transient behavior and the permanent regime of the epidemiological system described by ODE and PCA. Our main conclusions are: (1) the basic reproduction number (commonly called R(0)) related to a disease propagation in a population cannot be uniquely determined from some features of transient behavior of the infective group; (2) R(0) cannot be associated to a unique combination of clustering coefficient and average shortest path length characterizing the contact network. We discuss how these results can embarrass the specification of control strategies for combating disease propagations. (C) 2009 Elsevier B.V. All rights reserved.

beta 1 Integrin and VEGF expression in an experimental model of brain tissue heterotopia in the lung

Integrins and vascular endothelial growth factor (VEGF) are crucially involved in interaction, proliferation, migration, and survival of the cells. However, there is no report in the literature about beta 1 integrin and VEGF expression in heterotopic brain tissue. The aim of this study was to assess beta 1 integrin and VEGF expression in experimental brain tissue heterotopia in the lung during both fetal and neonatal periods. Twenty-four pregnant female Swiss mice were used to induce brain tissue heterotopia on the 15th gestational day. Briefly, the brain of one fetus of each dam was extracted, disaggregated, and injected into the right hemithorax of siblings. Six of these fetuses with pulmonary brain tissue implantation were collected on the 18th gestational day (group E18) and six other on the eighth postnatal day (group P8). Immunohistochemistry of the fetal trunks showed implantation of glial fibrillary acidic protein- and neuronal nuclei-positive heterotopic brain tissue, which were also positive for beta 1 integrin and VEGF in both groups E18 and P8. These results indicate that brain tissue heterotopia during fetal and postnatal period is able to complete integration with the lung tissue as well as to induce vascular proliferation which are the necessary steps for a successful implantation.

Inércia da taxa de juros : teoria e evidência para a economia brasileira (2005-2013)

No âmbito da condução da política monetária, as funções de reação estimadas em estudos empíricos, tanto para a economia brasileira como para outras economias, têm mostrado uma boa aderência aos dados. Porém, os estudos mostram que o poder explicativo das estimativas aumenta consideravelmente quando se inclui um componente de suavização da taxa de juros, representado pela taxa de juros defasada. Segundo Clarida, et. al. (1998) o coeficiente da taxa de juros defasada (situado ente 0,0 e 1,0) representaria o grau de inércia da política monetária, e quanto maior esse coeficiente, menor e mais lenta é a resposta da taxa de juros ao conjunto de informações relevantes. Por outro lado, a literatura empírica internacional mostra que esse componente assume um peso expressivo nas funções de reação, o que revela que os BCs ajustam o instrumento de modo lento e parcimonioso. No entanto, o caso brasileiro é de particular interesse porque os trabalhos mais recentes têm evidenciado uma elevação no componente inercial, o que sugere que o BCB vem aumentando o grau de suavização da taxa de juros nos últimos anos. Nesse contexto, mais do que estimar uma função de reação forward looking para captar o comportamento global médio do Banco Central do Brasil no período de Janeiro de 2005 a Maio de 2013, o trabalho se propôs a procurar respostas para uma possível relação de causalidade dinâmica entre a trajetória do coeficiente de inércia e as variáveis macroeconômicas relevantes, usando como método a aplicação do filtro de Kalman para extrair a trajetória do coeficiente de inércia e a estimação de um modelo de Vetores Autorregressivos (VAR) que incluirá a trajetória do coeficiente de inércia e as variáveis macroeconômicas relevantes. De modo geral, pelas regressões e pelo filtro de Kalman, os resultados mostraram um coeficiente de inércia extremamente elevado em todo o período analisado, e coeficientes de resposta global muito pequenos, inconsistentes com o que é esperado pela teoria. Pelo método VAR, o resultado de maior interesse foi o de que choques positivos na variável de inércia foram responsáveis por desvios persistentes no hiato do produto e, consequentemente, sobre os desvios de inflação e de expectativas de inflação em relação à meta central.

Persistent random walk model for transport trough thin slabs

We present a model for transport in multiply scattering media based on a three-dimensional generalization of the persistent random walk. The model assumes that photons move along directions that are parallel to the axes. Although this hypothesis is not realistic, it allows us to solve exactly the problem of multiple scattering propagation in a thin slab. Among other quantities, the transmission probability and the mean transmission time can be calculated exactly. Besides being completely solvable, the model could be used as a benchmark for approximation schemes to multiple light scattering.

Properties of Anisotropic Superconductors in SR Model

The present thesis deals with the theoretical investigations on the effect of anisotropy on various properties of magnetically doped superconductors described by fihiba — Rusinov model.Chapter 1 is introductory. It contains a brief account of the current status of theory of superconductivity. In’ chapter 2 we give the formulation of the problem. Chapter 2.1 gives the BCS theory. The effect of magnetic impurities in superconductors as described by A8 theory is given in chapter 2.2A and that described by SR model is discussed in chapter 2.28. Chapter 2.2c deals with Kondo effect. In chapter 2.3 the anisotropy problem is reviewed. Our calculations, results and discussions are given in chapter 3. Chapter 3.1 deals with Josephson tunnel effect. In chapter 3.2 the thermodynamic critical field H62 is described. Chtpter 3.3 deals with the density of states. The ultrasonic attenuation coefficient and ufitlear spin relaxation are given in chapter 3.4 and 3.5 respectively. In chapter 3.6 we give the upper critical field calculations and chapter 3.7 deals with the response function. The Kondo effect is given in chapter 3.8. In chapter 4 we give the sumary of our results

Product autoregressive models for non-negative variables

When variables in time series context are non-negative, such as for volatility, survival time or wave heights, a multiplicative autoregressive model of the type Xt = Xα t−1Vt , 0 ≤ α < 1, t = 1, 2, . . . may give the preferred dependent structure. In this paper, we study the properties of such models and propose methods for parameter estimation. Explicit solutions of the model are obtained in the case of gamma marginal distribution

De lo micro a lo macro prudencial: Un análisis de la supervisión bancaria desde la econometría espacial

La crisis que se desató en el mercado hipotecario en Estados Unidos en 2008 y que logró propagarse a lo largo de todo sistema financiero, dejó en evidencia el nivel de interconexión que actualmente existe entre las entidades del sector y sus relaciones con el sector productivo, dejando en evidencia la necesidad de identificar y caracterizar el riesgo sistémico inherente al sistema, para que de esta forma las entidades reguladoras busquen una estabilidad tanto individual, como del sistema en general. El presente documento muestra, a través de un modelo que combina el poder informativo de las redes y su adecuación a un modelo espacial auto regresivo (tipo panel), la importancia de incorporar al enfoque micro-prudencial (propuesto en Basilea II), una variable que capture el efecto de estar conectado con otras entidades, realizando así un análisis macro-prudencial (propuesto en Basilea III).

Network externalities across financial institutions

We propose and estimate a financial distress model that explicitly accounts for the interactions or spill-over effects between financial institutions, through the use of a spatial continuity matrix that is build from financial network data of inter bank transactions. Such setup of the financial distress model allows for the empirical validation of the importance of network externalities in determining financial distress, in addition to institution specific and macroeconomic covariates. The relevance of such specification is that it incorporates simultaneously micro-prudential factors (Basel 2) as well as macro-prudential and systemic factors (Basel 3) as determinants of financial distress. Results indicate network externalities are an important determinant of financial health of a financial institutions. The parameter that measures the effect of network externalities is both economically and statistical significant and its inclusion as a risk factor reduces the importance of the firm specific variables such as the size or degree of leverage of the financial institution. In addition we analyze the policy implications of the network factor model for capital requirements and deposit insurance pricing.

Monthly average temperature modelling

This research is associated with the goal of the horticultural sector of the Colombian southwest, which is to obtain climatic information, specifically, to predict the monthly average temperature in sites where it has not been measured. The data correspond to monthly average temperature, and were recorded in meteorological stations at Valle del Cauca, Colombia, South America. Two components are identified in the data of this research: (1) a component due to the temporal aspects, determined by characteristics of the time series, distribution of the monthly average temperature through the months and the temporal phenomena, which increased (El Nino) and decreased (La Nina) the temperature values, and (2) a component due to the sites, which is determined for the clear differentiation of two populations, the valley and the mountains, which are associated with the pattern of monthly average temperature and with the altitude. Finally, due to the closeness between meteorological stations it is possible to find spatial correlation between data from nearby sites. In the first instance a random coefficient model without spatial covariance structure in the errors is obtained by month and geographical location (mountains and valley, respectively). Models for wet periods in mountains show a normal distribution in the errors; models for the valley and dry periods in mountains do not exhibit a normal pattern in the errors. In models of mountains and wet periods, omni-directional weighted variograms for residuals show spatial continuity. The random coefficient model without spatial covariance structure in the errors and the random coefficient model with spatial covariance structure in the errors are capturing the influence of the El Nino and La Nina phenomena, which indicates that the inclusion of the random part in the model is appropriate. The altitude variable contributes significantly in the models for mountains. In general, the cross-validation process indicates that the random coefficient model with spatial spherical and the random coefficient model with spatial Gaussian are the best models for the wet periods in mountains, and the worst model is the model used by the Colombian Institute for Meteorology, Hydrology and Environmental Studies (IDEAM) to predict temperature.