Márcio Poletti Laurini

Income convergence clubs for Brazilian Municipalities: a non-parametric analysis

This article analyses the evolution of relative per capita income distribution of Brazilian municipalities over the period 1970–1996. Analyses are based on non-parametric methodologies and do not assume probability distributions or functional forms for the data. Two convergence tests have been carried out – a test for sigma convergence based on the bootstrap principle and a beta convergence test using smoothing splines for the growth regressions. The results obtained demonstrate the need to model the dynamics of income for Brazilian municipalities as a process of convergence clubs, using the methodology of transition matrices and stochastic kernels. The results show the formation of two convergence clubs, a low income club formed by the municipalities of the North and Northeast regions, and another high income club formed by the municipalities of the Center-West, Southeast and South regions. The formation of convergence clubs is confirmed by a bootstrap test for multimodality.

Does Ownership Affect the Variability of the Production Process? Evidence from International Courier Services

A firm often must ensure that products or services it produces match customer expectations. We define variability as any deviation in a production process yielding products or services whose attributes differ from the firms stated target specifications. Firms pursuing products marked by low variability are more subject to maladaptation costs if production processes are not adjusted to avoid nonconformities. Furthermore, such adjustments often require idiosyncratic investments (e.g., dedicated information technology systems), thereby creating contractual hazards and potential underinvestment. We hypothesize that ownership of sequential activities in the value chain helps mitigate problems associated with maladaptation as well as suboptimalities in transaction-specific investment, thereby resulting in lower variability. Using data on delivery times from the Japanese international courier and small package services industry, we assess the variability-reducing role of ownership in two complementary ways. The first approach is parametric, allowing us to assess the impact of ownership on the variance associated with delivery time; here we focus on shipments that frequently fail to arrive precisely within the time period initially expected by customers. The second approach is more consistent with the notion of reliability, or the likelihood that shipments will not arrive later than expected: we nonparametrically estimate the distribution of deviations between actual and expected delivery time, and verify how distinct organizational choices change the distribution. Ownership of multiple segments yields a particularly pronounced effect on both variance and reliability. Ownership bestows variability-reducing benefits of ownership, especially when ownership is observed in multiple stages of the value chain.

Original article: Indirect Inference in fractional short-term interest rate diffusions

In this article we discuss the estimation of continuous time interest rate models driven by fractional Brownian motion (fBm) using discretely sampled data. In the presence of a fractional Brownian motion, usual estimation methods for continuous time models are not appropriate since in general fBm is neither a semimartingale nor a Markov process. In this context, we discuss the use of simulation-based Indirect Inference.

A Hybrid Data Cloning Maximum Likelihood Estimator for Stochastic Volatility Models

Abstract: In this article, we analyze a maximum likelihood estimator using Data Cloning for Stochastic Volatility models. This estimator is constructed using a hybrid methodology based on Integrated Nested Laplace Approximations to calculate analytically the auxiliary Bayesian estimators with great accuracy and computational efficiency, without requiring the use of simulation methods such as Markov Chain Monte Carlo. We analyze the performance of this estimator compared to methods based on Monte Carlo simulations (Simulated Maximum Likelihood, MCMC Maximum Likelihood) and approximate maximum likelihood estimators using Laplace Approximations. The results indicate that this data cloning methodology achieves superior results over methods based on MCMC, comparable to results obtained by the Simulated Maximum Likelihood estimator. The methodology is extended to models with leverage effects, continuous time formulations, multifactor and multivariate stochastic volatility.