Central limit theorem for asymmetric kernel functionals

Asymmetric kernels are quite useful for the estimation of density functions with bounded support. Gamma kernels are designed to handle density functions whose supports are bounded from one end only, whereas beta kernels are particularly convenient for the estimation of density functions with compact support. These asymmetric kernels are nonnegative and free of boundary bias. Moreover, their shape varies according to the location of the data point, thus also changing the amount of smoothing. This paper applies the central limit theorem for degenerate U-statistics to compute the limiting distribution of a class of asymmetric kernel functionals.

Inovação e criatividade nas pequenas e médias empresas

The importance of small and medium enterprises for the economy of a country is fundamental because they have several strategic social and economic roles. Besides contributing to the production of national wealth, they also counterbalance the vulnerabilities of large companies providing the necessary economic balance. Socially their contribution is directly related to the lessening of unemployment, functioning also as source of stability in the community, as a means of reducing inequalities in the distribution of income among regions and economic groups, and contributes, decisively, to limit migration to urbans area. The capacity to innovate is now a key component for the survival and development of small organizations. The future today is increasingly less predictable using past parameters and the business world is more turbulent. The objective of this is to point out the need to revise the models which serve as examples for their adoption of competitive alternatives of development and to offer theoretical-practical knowledge to make possible the implementation of the innovative culture in small enterprises. It emphasizes, moreover, that in the present context, flexibility and skills to work in ambiguous situations and to find creative solutions become central concerns of businessmen and managers.

Formation and destruction of coalition groups under economic growth

This paper introduces a model economy in which formation of coalition groups under technological progress is generated endogenously. The coalition formation depends crucially on the rate of arrival of new technologies. In the model, an agent working in the saroe technology for more than one period acquires skills, part of which is specific to this technology. These skills increase the agent productivity. In this case, if he has worked more than one period with the same technology he has incentives to construct a coalition to block the adoption of new technologies. Therefore, in every sector the workers have incentives to construct a coalition and to block the adoption of new technologies. They will block every time that a technology stay in use for more than one period.

Aspectos limitadores e incentivadores para a adoção do mobile payment na realização de operações de microcrédito

Este estudo tem por objetivo identificar os aspectos que podem promover ou limitar o surgimento de arranjos de pagamentos de mobile payment (pagamentos móveis) que possam ser utilizados nas iniciativas de microcrédito no Brasil. O tema microcrédito foi escolhido em função do enorme potencial para a realização de operações de microcrédito no Brasil, bem como pelo aspecto de integração econômico-social que as operações de microcrédito podem proporcionar. A escolha do tema mobile payment ocorreu em função da versatilidade e inovação desta modalidade de pagamento, com potencial para uma grande transformação dos meios de pagamentos no mercado brasileiro. A análise da utilização do mobile payment nas iniciativas de microcrédito surgiu em função das contribuições que tal modalidade de pagamento pode trazer para a realização de operações de microcrédito. O objetivo deste estudo qualitativo é auxiliar na compreensão dos aspectos limitadores e incentivadores para a utilização do mobile payment nas iniciativas de microcrédito, bem como verificar quais contribuições a utilização do mobile payment pode trazer para a realização de operações de microcrédito. Adicionalmente, por meio das informações obtidas junto aos Grupos Sociais Relevantes (GSR) envolvidos nos arranjos de pagamentos, a presente pesquisa pretende ainda identificar as influências destes agentes para a formação e adaptação desses arranjos. As informações para a realização deste estudo foram coletadas principalmente por meio de entrevistas em profundidade com representantes selecionados dos GSR participantes dos arranjos de pagamentos. As entrevistas realizadas foram transcritas e constam no final do trabalho, representando uma fonte importante de informação aos interessados no tema, em função da riqueza de detalhes apresentados. Para a compreensão e interpretação do objeto de estudo, foi utilizado o referencial teórico apresentado por Coase e Williamson (Teoria dos Custos de Transação), Gannamaneni e Ondrus (Multilevel Framework). Com a publicação da Resolução 4.282 do Banco Central do Brasil em novembro de 2013, instituindo o marco regulatório que disciplina a autorização e o funcionamento de arranjos e instituições de pagamento no Brasil, o objeto de análise em questão torna-se ainda mais relevante para a comunidade acadêmica, participantes do mercado de microcrédito e GSR integrantes dos arranjos de mobile payment. Espera-se com isso contribuir para um melhor entendimento das barreiras e facilitadores para a adoção e desenvolvimento dos arranjos de pagamentos de mobile payment, e, principalmente, que esta pesquisa possa contribuir para uma maior interação destes arranjos com as iniciativas de microcrédito.

Inference in differences-in-differences with few treated groups and heteroskedasticity

Differences-in-Differences (DID) is one of the most widely used identification strategies in applied economics. However, how to draw inferences in DID models when there are few treated groups remains an open question. We show that the usual inference methods used in DID models might not perform well when there are few treated groups and errors are heteroskedastic. In particular, we show that when there is variation in the number of observations per group, inference methods designed to work when there are few treated groups tend to (under-) over-reject the null hypothesis when the treated groups are (large) small relative to the control groups. This happens because larger groups tend to have lower variance, generating heteroskedasticity in the group x time aggregate DID model. We provide evidence from Monte Carlo simulations and from placebo DID regressions with the American Community Survey (ACS) and the Current Population Survey (CPS) datasets to show that this problem is relevant even in datasets with large numbers of observations per group. We then derive an alternative inference method that provides accurate hypothesis testing in situations where there are few treated groups (or even just one) and many control groups in the presence of heteroskedasticity. Our method assumes that we can model the heteroskedasticity of a linear combination of the errors. We show that this assumption can be satisfied without imposing strong assumptions on the errors in common DID applications. With many pre-treatment periods, we show that this assumption can be relaxed. Instead, we provide an alternative inference method that relies on strict stationarity and ergodicity of the time series. Finally, we consider two recent alternatives to DID when there are many pre-treatment periods. We extend our inference methods to linear factor models when there are few treated groups. We also derive conditions under which a permutation test for the synthetic control estimator proposed by Abadie et al. (2010) is robust to heteroskedasticity and propose a modification on the test statistic that provided a better heteroskedasticity correction in our simulations.

Inference in differences-in-differences with few treated groups and heteroskedasticity

Differences-in-Differences (DID) is one of the most widely used identification strategies in applied economics. However, how to draw inferences in DID models when there are few treated groups remains an open question. We show that the usual inference methods used in DID models might not perform well when there are few treated groups and errors are heteroskedastic. In particular, we show that when there is variation in the number of observations per group, inference methods designed to work when there are few treated groups tend to (under-) over-reject the null hypothesis when the treated groups are (large) small relative to the control groups. This happens because larger groups tend to have lower variance, generating heteroskedasticity in the group x time aggregate DID model. We provide evidence from Monte Carlo simulations and from placebo DID regressions with the American Community Survey (ACS) and the Current Population Survey (CPS) datasets to show that this problem is relevant even in datasets with large numbers of observations per group. We then derive an alternative inference method that provides accurate hypothesis testing in situations where there are few treated groups (or even just one) and many control groups in the presence of heteroskedasticity. Our method assumes that we know how the heteroskedasticity is generated, which is the case when it is generated by variation in the number of observations per group. With many pre-treatment periods, we show that this assumption can be relaxed. Instead, we provide an alternative application of our method that relies on assumptions about stationarity and convergence of the moments of the time series. Finally, we consider two recent alternatives to DID when there are many pre-treatment groups. We extend our inference method to linear factor models when there are few treated groups. We also propose a permutation test for the synthetic control estimator that provided a better heteroskedasticity correction in our simulations than the test suggested by Abadie et al. (2010).