Cherry picking with synthetic controls

The synthetic control (SC) method has been recently proposed as an alternative method to estimate treatment e ects in comparative case studies. Abadie et al. [2010] and Abadie et al. [2015] argue that one of the advantages of the SC method is that it imposes a data-driven process to select the comparison units, providing more transparency and less discretionary power to the researcher. However, an important limitation of the SC method is that it does not provide clear guidance on the choice of predictor variables used to estimate the SC weights. We show that such lack of speci c guidances provides signi cant opportunities for the researcher to search for speci cations with statistically signi cant results, undermining one of the main advantages of the method. Considering six alternative speci cations commonly used in SC applications, we calculate in Monte Carlo simulations the probability of nding a statistically signi cant result at 5% in at least one speci cation. We nd that this probability can be as high as 13% (23% for a 10% signi cance test) when there are 12 pre-intervention periods and decay slowly with the number of pre-intervention periods. With 230 pre-intervention periods, this probability is still around 10% (18% for a 10% signi cance test). We show that the speci cation that uses the average pre-treatment outcome values to estimate the weights performed particularly bad in our simulations. However, the speci cation-searching problem remains relevant even when we do not consider this speci cation. We also show that this speci cation-searching problem is relevant in simulations with real datasets looking at placebo interventions in the Current Population Survey (CPS). In order to mitigate this problem, we propose a criterion to select among SC di erent speci cations based on the prediction error of each speci cations in placebo estimations

A Influência do atributo de segurança na segmentação dos consumidores de carne suína da região fronteira noroeste do Rio Grande do Sul

This document constitutes itself a thesis for a master¿s, partial fulfillment for getting a master¿s degree in Public and Business Management. The subject is a research on the influence of the attribute of safety in the segmentation of pork in the northwest border of the State of Rio Grande do Sul, Brazil, involving a bibliographic review related to the theme, and also an explorative research in order to identify the characteristics of the sector. From this information, a descriptive research is carried out from data collected in stores wich commercialize pork products in Santa Rosa, reaching a comprehensive undestanding on the subject. The main results of the research are the theoretical building of the thematic support concerning the safety of the food; the use of interviews aiming at verifying the importance of different attributes of pork which the consumer consider important and also the identification of segments based on these assessments. The conclusions from the research show that the consumers in general consider of high importance the safety of the food, and it is due to the fact that pork is still considered a product which offers a high risk at consumption. Consumers still have a preconceived idea about pork, because years ago, hog breeding was conducted with precarious standards of hygiene. It is imperative that the related business and producers divulge the advances of the sector in order to destroy this negative concept. The study identified three segments of market to the consumer of in natura pork: the first emphasized attributes are the inspection and the presentation of the product (cluster 1), flavour and softness stand out as the second ones. Finally, the third most important attributes that consumers consider when picking a pork product are its price and its aspect. It also favours further studies wich can provide the formulation of new marketing strategies to order products that form part of this agrolimentary system, giving this segment opportunity to boost its business and improve its approach to its consumer.

Eficiência da magic formula de value investing no mercado brasileiro

O objetivo deste trabalho é realizar procedimento de back-test da Magic Formula na Bovespa, reunindo evidências sobre violações da Hipótese do Mercado Eficiente no mercado brasileiro. Desenvolvida por Joel Greenblatt, a Magic Formula é uma metodologia de formação de carteiras que consiste em escolher ações com altos ROICs e Earnings Yields, seguindo a filosofia de Value Investing. Diversas carteiras foram montadas no período de dezembro de 2002 a maio de 2014 utilizando diferentes combinações de número de ativos por carteira e períodos de permanência. Todas as carteiras, independentemente do número de ativos ou período de permanência, apresentaram retornos superiores ao Ibovespa. As diferenças entre os CAGRs das carteiras e o do Ibovespa foram significativas, sendo que a carteira com pior desempenho apresentou CAGR de 27,7% contra 14,1% do Ibovespa. As carteiras também obtiveram resultados positivos após serem ajustadas pelo risco. A pior razão retorno-volatilidade foi de 1,2, comparado a 0,6 do Ibovespa. As carteiras com pior pontuação também apresentaram bons resultados na maioria dos cenários, contrariando as expectativas iniciais e os resultados observados em outros trabalhos. Adicionalmente foram realizadas simulações para diversos períodos de 5 anos com objetivo de analisar a robustez dos resultados. Todas as carteiras apresentaram CAGR maior que o do Ibovespa em todos os períodos simulados, independentemente do número de ativos incluídos ou dos períodos de permanência. Estes resultados indicam ser possível alcançar retornos acima do mercado no Brasil utilizando apenas dados públicos históricos. Esta é uma violação da forma fraca da Hipótese do Mercado Eficiente.