Estimation of random coefficients logit demand models: an application to the Brazilian fixed income fund market

Estimation of demand and supply in differentiated products markets is a central issue in Empirical Industrial Organization and has been used to study the effects of taxes, merges, introduction of new goods, market power, among others. Logit and Random Coefficients Logit are examples of demand models used to study these effects. For the supply side it is generally supposed a Nash equilibrium in prices. This work presents a detailed discussion of these models of demand and supply as well as the procedure for estimation. Lastly, is made an application to the Brazilian fixed income fund market.

Bias from ignoring price dispersion in demand estimation

Consumers often pay different prices for the same product bought in the same store at the same time. However, the demand estimation literature has ignored that fact using, instead, aggregate measures such as the “list” or average price. In this paper we show that this will lead to biased price coefficients. Furthermore, we perform simple comparative statics simulation exercises for the logit and random coefficient models. In the “list” price case we find that the bias is larger when discounts are higher, proportion of consumers facing discount prices is higher and when consumers are more unwilling to buy the product so that they almost only do it when facing discount. In the average price case we find that the bias is larger when discounts are higher, proportion of consumers that have access to discount are similar to the ones that do not have access and when consumers willingness to buy is very dependent on idiosyncratic shocks. Also bias is less problematic in the average price case in markets with a lot of bargain deals, so that prices are as good as individual. We conclude by proposing ways that the econometrician can reduce this bias using different information that he may have available.

Essays on regulatory risk issues

Most studies around that try to verify the existence of regulatory risk look mainly at developed countries. Looking at regulatory risk in emerging market regulated sectors is no less important to improving and increasing investment in those markets. This thesis comprises three papers comprising regulatory risk issues. In the first Paper I check whether CAPM betas capture information on regulatory risk by using a two-step procedure. In the first step I run Kalman Filter estimates and then use these estimated betas as inputs in a Random-Effect panel data model. I find evidence of regulatory risk in electricity, telecommunications and all regulated sectors in Brazil. I find further evidence that regulatory changes in the country either do not reduce or even increase the betas of the regulated sectors, going in the opposite direction to the buffering hypothesis as proposed by Peltzman (1976). In the second Paper I check whether CAPM alphas say something about regulatory risk. I investigate a methodology similar to those used by some regulatory agencies around the world like the Brazilian Electricity Regulatory Agency (ANEEL) that incorporates a specific component of regulatory risk in setting tariffs for regulated sectors. I find using SUR estimates negative and significant alphas for all regulated sectors especially the electricity and telecommunications sectors. This runs in the face of theory that predicts alphas that are not statistically different from zero. I suspect that the significant alphas are related to misspecifications in the traditional CAPM that fail to capture true regulatory risk factors. On of the reasons is that CAPM does not consider factors that are proven to have significant effects on asset pricing, such as Fama and French size (ME) and price-to-book value (ME/BE). In the third Paper, I use two additional factors as controls in the estimation of alphas, and the results are similar. Nevertheless, I find evidence that the negative alphas may be the result of the regulated sectors premiums associated with the three Fama and French factors, particularly the market risk premium. When taken together, ME and ME/BE regulated sectors diminish the statistical significance of market factors premiums, especially for the electricity sector. This show how important is the inclusion of these factors, which unfortunately is scarce in emerging markets like Brazil.

A (semi-)parametric functional coefficient autoregressive conditional duration model

In this paper, we propose a class of ACD-type models that accommodates overdispersion, intermittent dynamics, multiple regimes, and sign and size asymmetries in financial durations. In particular, our functional coefficient autoregressive conditional duration (FC-ACD) model relies on a smooth-transition autoregressive specification. The motivation lies on the fact that the latter yields a universal approximation if one lets the number of regimes grows without bound. After establishing that the sufficient conditions for strict stationarity do not exclude explosive regimes, we address model identifiability as well as the existence, consistency, and asymptotic normality of the quasi-maximum likelihood (QML) estimator for the FC-ACD model with a fixed number of regimes. In addition, we also discuss how to consistently estimate using a sieve approach a semiparametric variant of the FC-ACD model that takes the number of regimes to infinity. An empirical illustration indicates that our functional coefficient model is flexible enough to model IBM price durations.

A model of private bank-note issue

A random-matching model (ofmoney) is formulated in which there is complete public knowledge of the trading histories of a subset of the population, called the banking sector, and no public knowledge of the trading histories of the complement of that subset, called the non bank sector. Each person, whether a banker or a non banker, is assumed to have the technological capability to create indivisible and durable objects called notes. If outside money is indivisible and sufficiently scarce, then the optimal mechanism is shown to have note issue and note destruction (redemption) by members of the banking sector.

In search of exchange rate predictability : a study about accuracy, consistency, and granger causality of forecasts generated by a Taylor Rule Model

Este estudo investiga o poder preditivo fora da amostra, um mês à frente, de um modelo baseado na regra de Taylor para previsão de taxas de câmbio. Revisamos trabalhos relevantes que concluem que modelos macroeconômicos podem explicar a taxa de câmbio de curto prazo. Também apresentamos estudos que são céticos em relação à capacidade de variáveis macroeconômicas preverem as variações cambiais. Para contribuir com o tema, este trabalho apresenta sua própria evidência através da implementação do modelo que demonstrou o melhor resultado preditivo descrito por Molodtsova e Papell (2009), o “symmetric Taylor rule model with heterogeneous coefficients, smoothing, and a constant”. Para isso, utilizamos uma amostra de 14 moedas em relação ao dólar norte-americano que permitiu a geração de previsões mensais fora da amostra de janeiro de 2000 até março de 2014. Assim como o critério adotado por Galimberti e Moura (2012), focamos em países que adotaram o regime de câmbio flutuante e metas de inflação, porém escolhemos moedas de países desenvolvidos e em desenvolvimento. Os resultados da nossa pesquisa corroboram o estudo de Rogoff e Stavrakeva (2008), ao constatar que a conclusão da previsibilidade da taxa de câmbio depende do teste estatístico adotado, sendo necessária a adoção de testes robustos e rigorosos para adequada avaliação do modelo. Após constatar não ser possível afirmar que o modelo implementado provém previsões mais precisas do que as de um passeio aleatório, avaliamos se, pelo menos, o modelo é capaz de gerar previsões “racionais”, ou “consistentes”. Para isso, usamos o arcabouço teórico e instrumental definido e implementado por Cheung e Chinn (1998) e concluímos que as previsões oriundas do modelo de regra de Taylor são “inconsistentes”. Finalmente, realizamos testes de causalidade de Granger com o intuito de verificar se os valores defasados dos retornos previstos pelo modelo estrutural explicam os valores contemporâneos observados. Apuramos que o modelo fundamental é incapaz de antecipar os retornos realizados.

Cálculo do Value at Risk (VaR) para o Ibovespa, pós crise de 2008, por meio dos modelos de heterocedasticidade condicional (GARCH) e de volatilidade estocástica (Local Scale Model - LSM)

O objetivo deste estudo é propor a implementação de um modelo estatístico para cálculo da volatilidade, não difundido na literatura brasileira, o modelo de escala local (LSM), apresentando suas vantagens e desvantagens em relação aos modelos habitualmente utilizados para mensuração de risco. Para estimação dos parâmetros serão usadas as cotações diárias do Ibovespa, no período de janeiro de 2009 a dezembro de 2014, e para a aferição da acurácia empírica dos modelos serão realizados testes fora da amostra, comparando os VaR obtidos para o período de janeiro a dezembro de 2014. Foram introduzidas variáveis explicativas na tentativa de aprimorar os modelos e optou-se pelo correspondente americano do Ibovespa, o índice Dow Jones, por ter apresentado propriedades como: alta correlação, causalidade no sentido de Granger, e razão de log-verossimilhança significativa. Uma das inovações do modelo de escala local é não utilizar diretamente a variância, mas sim a sua recíproca, chamada de “precisão” da série, que segue uma espécie de passeio aleatório multiplicativo. O LSM captou todos os fatos estilizados das séries financeiras, e os resultados foram favoráveis a sua utilização, logo, o modelo torna-se uma alternativa de especificação eficiente e parcimoniosa para estimar e prever volatilidade, na medida em que possui apenas um parâmetro a ser estimado, o que representa uma mudança de paradigma em relação aos modelos de heterocedasticidade condicional.

Practice location of physicians: a discrete choice model approach

Economists and policymakers have long been concerned with increasing the supply of health professionals in rural and remote areas. This work seeks to understand which factors influence physicians’ choice of practice location right after completing residency. Differently from previous papers, we analyse the Brazilian missalocation and assess the particularities of developing countries. We use a discrete choice model approach with a multinomial logit specification. Two rich databases are employed containing the location and wage of formally employed physicians as well as details from their post-graduation. Our main findings are that amenities matter, physicians have a strong tendency to remain in the region they completed residency and salaries are significant in the choice of urban, but not rural, communities. We conjecture this is due to attachments built during training and infrastructure concerns.