This thesis deals with the detection of change in the intercept in panel data re- gression model. We are interested in testing a null hypothesis that there was no change in the intercept during the observation period in case with no depen- dency between panels and with the number of panels and observations in each panel going to infinity. Based on the results for simplified case with no additional regressors we propose a statistical test and show its properties. We also derive a consistent estimate of the parameter of change based on the least squares me- thod. The main contribution of the thesis is the derivation of theoretical results of the proposed test while variances of errors are known and its modification for unknown variance parameters. A large simulation study is conducted to examine the results. Then we present an application to real data, particularly we use four factor CAPM model to detect change in monthly returns of US mutual funds during an observation period 2004-2011 and show a significant change during the sub-prime crisis in 2007-2008. This work expands existing results for de- tecting changes in the mean in panel data and offers many directions for further beneficial research. 1

Orientador: José Sílvio Govone / Resumo: As variáveis aleatórias no espaço estão definidas como funções aleatórias sujeitas à teoria das variáveis regionalizadas. Para assumir continuidade espacial com um número limitado de realizações da variável aleatória são necessárias as hipóteses de estacionariedade, as quais envolvem diferentes graus de homogeneidade espacial. Formalmente, uma variável regionalizada Z é estacionária se os momentos estatísticos de Z(s+h) forem os mesmos para qualquer vetor h. A hipótese de estacionariedade de primeira ordem é definida como a hipótese de que o momento de primeira ordem da distribuição da função aleatória Z(s) é constante em toda a área. A hipótese intrínseca é baseada no cálculo de médias globais das semivariancias, com a pressuposição de estacionariedade de 1a ordem e da estacionariedade da variância dos incrementos. Embora muitas variáveis sejam suscetível a dupla ou múltipla estacionariedade, estas estruturas espaciais não são levadas em consideração pelo semivariograma usual. Na perspectiva de solucionar o problema apontado, buscou-se identificar os locais dos pontos de mudança na média que definem mais de uma estrutura de semivariancia, com o objetivo de melhorar a qualidade dos mapas de Krigagem Ordinária. Para isso, foi utilizado o Método de Partição Produto (MPP), com enfoque espacial, denominado Método de Partição Produto Espacial (MPPs). Para separar os grupos, foi criada uma função de busca de ponto de mudança na média utilizando o modelo hierárquico bayesiano, denom... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: The random variables in space are defined by random functions subject to regionalized variable theory. To assume spatial continuity with a limited number of realization of the random variable, we need to assume stationarity hypotheses, which involve different degrees of spatial homogeneity. Formally, a regionalized variable Z is stationary if statistical moments of Z(s + h) are the same for any vector h. The first order stationarity hypothesis is defined to be the hypothesis that first order moment of the distribution of the random function Z(s) is constant throughout the area. The intrinsic hypothesis is based on the computation of global means of semivariate models, with the assumption of 1st order stationarity and incremental variation stationarity. Although many variables are capable of double or multiple stationarity, these spatial structures are not taken into account by the usual semivariogram, and, consequently, cause acuracy problems in Kriging maps. In order xvii to solve the described problem, it was identify the points of change in the average with the objective of improving the quality and accuracy of the maps of Ordinary Kriging. To separate the groups, a mean change point search function was created using the Bayesian hierarchical model, called the Space Product Partition Model (MPPs). Two databases were used to test the model’s potential to separate spatially dependent groups, in which the former suspected a change in mean while in the latter. “ Data2 ”, there... (Complete abstract click electronic access below) / Doutor