Using consumer search cost and loyalty to explain dispersion in banking fees

Pode-se observar uma considerável dispersão entre os preços que diferentes bancos comerciais no Brasil cobram por um mesmo pacote homogêneo de serviços— dispersão esta que é sustentada ao longo do tempo. Em uma tentativa de replicar esta observação empírica, foi desenvolvido um simples modelo que lança mão do arcabouço da literatura de custos de procura (search costs) e que baseia-se também na lealdade por parte dos consumidores. Em seguida, dados de preços referentes ao setor bancário brasileiro são aplicados ao modelo desenvolvido e alguns exercícios empíricos são então realizados. Esses exercícios permitem que: (i) os custos de procura incorridos pelos consumidores sejam estimados, ao fixar-se os valores dos demais parâmetros e (ii) as correspondentes perdas de peso-morto que surgem como consequência dos custos de procura incorridos pelos consumidores sejam também estimadas. Quando apenas 80% da população é livre para buscar por bancos que cobrem menores tarifas, à taxa de juros mensal de 0,5%, o valor estimado do custo de procura médio incorrido pelos consumidores chega a 1805,80 BRL, sendo a correspondente perda de peso-morto média na ordem de 233,71 BRL por consumidor.

Improved combinations of parametric and nonparametric Regression estimators

This paper provides a systematic and unified treatment of the developments in the area of kernel estimation in econometrics and statistics. Both the estimation and hypothesis testing issues are discussed for the nonparametric and semiparametric regression models. A discussion on the choice of windowwidth is also presented.

A class of improved heteroskedasticity-consistent covariance matrix estimators

The heteroskedasticity-consistent covariance matrix estimator proposed by White (1980), also known as HC0, is commonly used in practical applications and is implemented into a number of statistical software. Cribari–Neto, Ferrari & Cordeiro (2000) have developed a bias-adjustment scheme that delivers bias-corrected White estimators. There are several variants of the original White estimator that also commonly used by practitioners. These include the HC1, HC2 and HC3 estimators, which have proven to have superior small-sample behavior relative to White’s estimator. This paper defines a general bias-correction mechamism that can be applied not only to White’s estimator, but to variants of this estimator as well, such as HC1, HC2 and HC3. Numerical evidence on the usefulness of the proposed corrections is also presented. Overall, the results favor the sequence of improved HC2 estimators.

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.