According to the 2003 National Household Survey, mean labour income for an indigenous worker is only 56% of that for a non-indigenous worker. Studies of ethnic discrimination in Peru’s labour markets generally find that discrimination is too low to explain inequalities of this magnitude. However, Sigma Theory (Figueroa 2003) predicts that social exclusion is a source of inter-ethnic inequality, and that has not been empirically tested. The primary aim of this paper is to fill this gap by estimating the extent to which exclusion and discrimination contribute to income inequality. Hurdle models are used to tackle down econometric endogeneity of years of schooling and truncation-at-zero of incomes. The results imply that exclusion plays a stronger role on inequality than discrimination: without exclusion, the Gini of labour income would decrease from 0.64 to 0.45, and without discrimination it would be reduced to 0.50. / De acuerdo a la Enaho 2003, el ingreso promedio de un trabajador indígena es solo 56% del de un trabajador no-indígena. Sin embargo, estudios sobre discriminación étnica en los mercados laborales de Perú usualmente hallan brechas demasiado pequeñas como para explicar la desigualdad observada. De acuerdo a Figueroa (2003), la exclusión social es una fuente importante de desigualdad interétnica, pero esto no ha sido contrastado empíricamente. El objetivo central de este documento es llenar esa brecha estimando qué porcentaje de la desigualdad se debe a exclusión y qué porcentaje a discriminación, comparando directamente los efectos. La metodología econométrica utilizada (hurdle models) permite incluir en el análisis a los trabajadores con ingresos nulos y contrarrestar problemas de endogeneidad econométrica. Los resultados implican que la exclusión juega un papel más importante que la discriminación. Sin exclusión, el Gini de ingresos laborales se reduciría de 0.64 a cerca de 0.45; sin discriminación, a alrededor de 0.50.

The aim of this thesis is to provide a comprehensive overview of the main approaches to modeling data loaded with redundant zeros. There are three main subclasses of zero modified models (ZMM) described here - zero inflated models (the main focus lies on models of this subclass), zero truncated models and hurdle models. Models of each subclass are defined and then a construction of maximum likelihood estimates of regression coefficients is described. ZMM models are mostly based on Poisson or negative binomial type 2 distribution (NB2). In this work, author has extended the theory to ZIM models generally based on any discrete distributions of exponential type. There is described a construction of MLE of regression coefficients of theese models, too. Just few of present works are interested in ZIM models based on negative binomial type 1 distribution (NB1). This distribution is not of exponential type therefore a common method of MLE construction in ZIM models cannot be used here. In this work provides modification of this method using quasi-likelihood method. There are two simulation studies concluding the work. 1

[pt] Demanda intermitente é definida por períodos de vendas nulas intercaladas com vendas positivas e de quantidade altamente variável. A maior parte das unidades de manutenção de estoque (stock keeping units, em inglês) ao nível loja pode ser caracterizada como contendo demanda desse tipo. Assim, modelos acurados para prever séries com demanda intermitente trazem grandes impactos em relação à gestão de estoque. Nesta dissertação nós propomos o uso do framework GAS com as distribuições adequadas para dados de contagem, além de suas versões com excesso de zeros, e aplicamos os modelos derivados a dados reais obtidos com uma grande rede varejista brasileira. Nós demonstramos que os modelos com excesso de zeros propostos são estimados de forma consistente por máxima verossimilhança e a distribuição dos estimadores é assintóticamente normal. A performance dos modelos propostos é comparada com benchmarks adequados das literaturas de séries temporais para dados de contagem e previsão de demanda intermitente. A avaliação das previsões é feita com base tanto na precisão da distribuição preditiva quanto na precisão das previsões pontuais. Nossos resultados mostram que os modelos propostos, em especial o modelo derivado sob distribuição hurdle Poisson, performam melhor do que os benchmarks analisados. / [en] Intermittent demand is defined by periods of zero sales interleaved with positive sales with highly variable quantities. Most stock keeping units at the store level can be characterized as containing such demand. Thus, accurate models for predicting series with intermittent demand have major impacts in relation to inventory management. In this dissertation we propose the use of the GAS framework with the appropriate distributions for count data, in addition to their versions with excess of zeroes, and apply the derived models to real data obtained from a large Brazilian retail chain. We demonstrate that the proposed models with excess of zeros are consistently estimated via maximum likelihood and the distribution of the estimator is asymptotically normal. The performance of the proposed models is compared to adequate benchmarks from the time series literature for count data and intermittent demand forecast. Forecasting is evaluated based on the accuracy of both the entire predictive distribution and point forecasts. Our results show that the proposed models, specially the one derived from hurdle Poisson distribution, perform better than the analyzed benchmarks.