Comparison between the complete Bayesian method and empirical Bayesian method for ARCH models using Brazilian financial time series

In this work we compared the estimates of the parameters of ARCH models using a complete Bayesian method and an empirical Bayesian method in which we adopted a non-informative prior distribution and informative prior distribution, respectively. We also considered a reparameterization of those models in order to map the space of the parameters into real space. This procedure permits choosing prior normal distributions for the transformed parameters. The posterior summaries were obtained using Monte Carlo Markov chain methods (MCMC). The methodology was evaluated by considering the Telebras series from the Brazilian financial market. The results show that the two methods are able to adjust ARCH models with different numbers of parameters. The empirical Bayesian method provided a more parsimonious model to the data and better adjustment than the complete Bayesian method.

Countercyclical fiscal policy: a review of the literature, empirical evidence and some policy proposals

Includes bibliography

National innovation surveys in Latin America: empirical evidence and policy implications

Includes bibliography

National innovation surveys in latin America: empirical evidence and policy implications

Includes bibliography

Business development service centres in Italy. An empirical analysis of three regional experiences: Emilia Romagna, Lombardia y Veneto

Includes bibliography

The impact of structural reforms on growth in Latin America and the Caribbean: an empirical estimation

Includes bibliography

Capacity-stretching technical change: some empirical and theoretical aspects

Includes bibliography

Bahamas and Barbados: empirical evidence of interest rate pass-through

Includes bibliography

Brazil: an empirical study on fiscal policy transmission

Includes bibliography

Factors affecting the adoption of green supply chain management practices in Brazil: Empirical evidence

The aim of this study is to identify and analyse the factors that affect the adoption of Green Supply Chain Management practices based on empirical evidence from the Brazilian electronics sector. Data are collected in a survey of 100 electronics companies and analysed using statistical analysis of variance and regression methods. The study finds that the size of the company, previous experience with Environmental Management Systems, and the use of hazardous inputs are positively correlated with GSCM practices adoption. Surprisingly, formal pressure from the stronger tier/player in the supply chain is not correlated with the adoption of GSCM practices. Finally, we present some explanations for these findings and suggestions for future research. © 2013 Copyright Taylor and Francis Group, LLC.

A useful empirical bayesian method to analyse industrial data from saturated factorial designs

The use of saturated two-level designs is very popular, especially in industrial applications where the cost of experiments is too high. Standard classical approaches are not appropriate to analyze data from saturated designs, since we could only get the estimates of the main factor effects and we would not have degrees of freedom to estimate the variance of the error. In this paper, we propose the use of empirical Bayesian procedures to get inferences for data obtained from saturated designs. The proposed methodology is illustrated assuming a simulated data set. © 2013 Growing Science Ltd. All rights reserved.

An empirical analysis of technology absorption capacity of the Brazilian industry

Includes bibliography.

International technological dynamics in production sectors: An empirical analysis

A new methodology is proposed for evaluating the economic development opportunities associated with the different industries making up a country’s economic structure. To this end, neo-Schumpeterian concepts are used to reinterpret the tools afforded by the “product space” literature in an attempt to assess the technological pervasiveness and sophistication of different production sectors. The ultimate objective is to develop a description of today’s techno-productive paradigm and the differential role that the various sectors play in it. An analysis of export data from 113 countries and territories for 2005-2009 indicates that the key sectors in the world economy are: industrial machinery, scientific and medical instruments, and pharmaceuticals. The strong performance of sectors based on mature technologies suggests that key sectors originating in different stages in history can survive and overlap one another, much like geological strata, owing to the persistence of older technological systems.