Strategic diagnostics and management decision making: a hybrid knowledge‐based approach

A conceptual model is introduced whereby the focus is placed on environmental scanning, diagnostics and decision‐making on the basis of managerial judgement through the application of tools such as intelligent agents, hybrid intelligent systems, scenario analysis and knowledge‐based systems. The model has a critical stage as an antecedent to the strategic advice that encompasses the issues of strategic fit to purpose (theme, industry, company and strategic business unit‐driven). A number of applications dealing with these four layers are shown, as well as an illustration of hybrid intelligent systems for strategic marketing planning. Copyright © 2007 John Wiley & Sons, Ltd.     

Debt ceiling and fiscal sustainability in Brazil: A quantile autoregression approach

In this paper we investigate fiscal sustainability by using a quantile autoregression (QAR) model. We propose a novel methodology to separate periods of nonstationarity from stationary ones, allowing us to identify various trajectories of public debt that are compatible with fiscal sustainability. We use such trajectories to construct a debt ceiling, that is, the largest value of public debt that does not jeopardize long-run fiscal sustainability. We make an out-of-sample forecast of such a ceiling and show how it could be used by Policy makers interested in keeping the public debt on a sustainable path. We illustrate the applicability of our results using Brazilian data.     

Performance Heterogeneity in Latin America: An Investigation into the Transient Effects of Country of Origin

The heterogeneity of firm performance has been studied from the perspective of factors, including the firm effect, the industry effect, and the country effect. This study emphasizes the importance of country transient effects in light of the volatility present in Latin American countries. Variance decomposition was carried out for the economic and operational performance of five countries in the period from 1998 to 2007. The results show that country effects matter for Latin America, its transient effects increase in periods of higher turbulence, and have a greater effect on a firms' economic performance than on its operational performance.     

Supply chain risk: empirical research in the automotive industry

Supply chain risk management (SCRM) has become a popular topic over the past decade. It is not a surprise that the automotive industry has been a motivating arena for research within this field; however, the few existing empirical studies reveal that SCRM practices within this industry are still in their infancy. Because the identification of risks can be viewed as the trigger for SCRM, attempts to develop a risk profile for this industry that could serve as a guide to start the SCRM process are needed. This research identifies the main risks along the automotive supply chain by investigating their manifestation in three supply chains in Brazil and offers an initial risk profile for the Brazilian automotive industry. Although the importance of SCRM has been recognised by all analysed companies, the research findings underline the lack of preparedness regarding either identifying risk or considering risk-mitigation strategies and risk assessment. In this context, this study identifies the main risk in which a supply chain can be exposed, through the analysis of real-life manifested risks along different supply chains, as a way to help the supply chain start a SCRM process.     

Corruption,South African Multinational Enterprises and Institutions in Africa

We examine the responses of South African multinational enterprises (MNEs) to corruption in African markets in the context of institutional voids. Corruption is a source of uncertainty and additional transactional costs for MNEs and it necessitates a strategic response. The research employs a qualitative study of a sample of MNEs with experience in internationalising into Africa. The results indicate that corruption in African markets is pervasive and closely associated with the institutional voids in these countries. MNEs see themselves as ‘institution takers’ responding to countries’ institutional makeup at the organisational and individual level but fail to fully appreciate their impact on institutions both positively and negatively. Rather MNEs focus on strategic responses at the organisational level to address corruption operationally in the host country. We add to the existing literature by providing a dynamic framework of the complex webs of association between institutions, MNEs and corruption in conditions of economic underdevelopment. The research suggests that MNEs do not need to get caught in a vicious cycle whereby they perpetuate corruption in conditions of underdevelopment and institutional voids but instead can contribute towards a virtuous cycle through which they institutionalise ethical foundations.     

Methodological Procedure for Estimating Brazilian Quarterly GDP Series

This paper presents a methodology for estimating the Brazilian GDP quarterly series in the period between 1960–1996. Firstly, an Engle–Granger’s static equation is estimated using GDP yearly data and GDP-related variables. The estimated coefficients from this regression are then used to obtain a first estimation of the quarterly GDP, with unavoidable measurement errors. The subsequent step is entirely based on benchmarking models estimated within a state space framework and consists in improving the preliminary GDP estimation in order to both eliminate as much as possible the measurement error and that the sum of the quarterly values matches the annual GDP.

Exchange rate movements and monetary policy in Brazil: Econometric and simulation evidence

The literature on monetary economy has aroused growing interest in macroeconomics. Due to computational advancements, models have become increasingly more complex and accurate, allowing for an in-depth analysis of the relationships between real economic variables and nominal variables. Therefore, using a dynamic stochastic general equilibrium (DSGE) model, based on Gali and Monacelli (2005), we propose and estimate a model for the Brazilian economy by employing Bayesian methods so as to assess whether the Central Bank of Brazil takes exchange rate fluctuations into account in the conduct of monetary policy. The most striking result of the present study is that the Central Bank of Brazil does not directly change the interest rate path due to exchange rate movements. A simulation exercise is also used. Our conclusion is that the economy quickly accommodates shocks induced separately on the exchange rate, on the terms of trade, interest rate, and global inflation.     

Profitability and social impacts of reduced impact logging in the Tapajós National Forest, Brazil — A case study

Brazil contains the world's largest tropical rainforests, most located in the Amazon River Basin. Over the last three decades, rapid growth of this region's deforested area has had negative impacts. To minimize these impacts and maintain biodiversity, the Brazilian Government has established several national forests in the Basin. The ITTO Project, a reduced impact logging (RIL) operation, was recently carried out at one of these forests: the Tapajós National Forest, also known as Flona Tapajós. This paper evaluates the Project's profitability and its effect on local residents. The Project, which ran between 1999 and 2003, was coordinated by the Brazilian Institute of Environment and Renewable Natural Resources (IBAMA), with funding for planning and monitoring provided by the United Kingdom's Department of International Development (DFID) working through and approved by the International Tropical Timber Organization (ITTO). Treviso Agropecuária Ltda, a private logging company, carried out timber extraction on the Project site. Our evaluation found the ITTO Project to have been highly profitable for Treviso, even after their compliance with all Brazilian labor and environmental laws. This finding was based on field interviews and the examination of documents from IBAMA and Treviso. Treviso's mean internal rate of return from the Project was calculated to have been 35.79%, considerably higher than that generated by the region's farms and ranches. The ITTO Project positively impacted Project workers, providing employment and exposing them to rainforest management techniques that maximize timber production while minimizing forest destruction. The paper closes by suggesting that more of the direct and indirect benefits of new reduced impact logging projects on Brazilian national forest land need to be channeled to the local population to increase the probability of them act as capable forest custodians.     

The Political Economy of Middle‐Income Traps: Is South Africa in a Long‐Run Growth Trap? The Path to “Bounded Populism”

The current literature on middle‐income traps has been dominated by economists who have relied on economic explanations mainly around stages of development and the structural transformation of economies. But there is an equally vigorous literature from political science which speaks to the political economy of transitions. We look at the dynamics of how economic modernisation triggers structural changes with winners and losers and how this is reflected in the polarisation of the political sphere amongst middle‐income countries. This paper asks the question of whether South Africa is an archetypical example of a country stuck in a trap and how this has affected the policy choices that it has made. South Africa needs to move up the value chain with a viable value proposition, and this requires a very different policy set and human capital plan.     

Treating and Pruning: New approaches to forecasting model selection and combination using prediction intervals

We propose a new way of selecting among model forms in automated exponential smoothing routines, consequently enhancing their predictive power. The procedure, here addressed as treating, operates by selectively subsetting the ensemble of competing models based on information from their prediction intervals. By the same token, we set forth a pruning strategy to improve the accuracy of both point forecasts and prediction intervals in forecast combination methods. The proposed approaches are respectively applied to automated exponential smoothing routines and Bagging algorithms, to demonstrate their potential. An empirical experiment is conducted on a wide range of series from the M-Competitions. The results attest that the proposed approaches are simple, without requiring much additional computational cost, but capable of substantially improving forecasting accuracy for both point forecasts and prediction intervals, outperforming important benchmarks and recently developed forecast combination methods.