Volatility and cross correlation across major stock markets

Several papers have documented the fact that correlations across major stock markets are higher when markets are more volatile—this is done by comparing unconditional correlations over sub-periods or by using conditional correlations that are time varying. In this paper we examine the relation between correlation and variance in a conditional time and state varying framework. We use a switching ARCH (SWARCH) technique that does two things. One, it enables us to model variance as state varying. Two, a bivariate SWARCH model allows us to go from conditional variance to state varying covariances and correlations and hence test for differences in correlations across variance regimes. We find that the correlations between the U.S. and other world markets are on average 2 to 3.5 times higher when the U.S. market is in a high variance state as compared to a low variance regime. We also find that, compared to a GARCH framework, the portfolio choices resulting from our SWARCH model lead to higher Sharpe ratios.     

Slow Growth,Trade Liberalisation and the Mexican Disease: A medium-term macroeconomic model with an application to Mexico

The purpose of this paper is to analyse Mexico's medium-term macroeconomic outlook, from the vantage point of the country's recent development. The analysis is carried out within a formal model for the determination of the rates of employment and inflation under conditions of external and internal balance. In equilibrium, the real wage and the rates of employment and inflation depend inter alia on the level of labour productivity, the ratio of foreign debt to domestic output, and the foreign trade regime. Econometric tests based on Mexican data support the model's basic postulates and, in addition, reveal the presence of structural change, linked to trade reform, in the trade balance and the manufactures' productivity growth equations. The macroeconomic implications of such parameter changes are discussed with the help of the analytical model developed.     

Realism Versus Statistical Efficiency: A Note on Contingent Valuation with Follow-up Queries

This paper shows that we can improve the statistical efficiency of dichotomous choice contingent valuation surveys by asking a second open question (anchored open-ended approach) instead of the traditional double-bounded approach. The former approach is shown to be more efficient than conventional single and double-bounded approaches using a Monte Carlo experiment, even when we allow for strategic behavior from respondents such as protest votes and yea-saying behavior.

The economic and social impact of telecommunications output

Economists, management researchers and sociologists have all examined the impact of the spread of information and communication technologies. There has until now, however, been relatively little interconnectivity and cross-fertilisation of their fi ndings. The following paper attempts to derive a set of integrated “causality frameworks” and tests the resulting hypotheses using data for Spain.     

Emerging Technologies and Ethics: A Race-to-the-Bottom or the Top?

Does national success with an emerging technology require ethical sacrifices? This question is considered through the simultaneous consideration of ethics, investment, and outcomes in the nine jurisdictions that are making the largest investments in nanotechnologies—an important emerging technology. It is found that while ethical environment has no notable effect on pure and applied research, a more positive ethical environment is associated with measures associated with invention and commercialization. In summary, a race-to-the-top supports invention and commercialization of emerging technologies. A critical finding as it suggests that issues such as corruption and regulation could be critical in limiting the extraction of social and economic benefit from emerging technologies (like nanotechnologies).     

Bridging the innovation divide: The Brazilian experience

The purpose of this article is to discuss the Brazilian innovation environment. It addresses the challenges, promises, bottlenecks, and opportunities by examining the country's quest to bridge the innovation divide. In addition, it applies the main dimensions of the “innovation diamond model” to assess how the country is faring in its journey toward competitiveness. The article analyzes Brazil's main innovation policies and agencies. Its companies and policymakers have recently understood that, without a dynamic and rich innovation environment, it will not be able to design long‐term economic development strategies and engage in long‐term sustainable growth. © 2012 Wiley Periodicals, Inc.     

Do disaggregated CPI data improve the accuracy of inflation forecasts?

In this paper, we evaluate the role of using consumer price index (CPI) disaggregated data to improve the accuracy of inflation forecasts. Our forecasting approach is based on extracting the factors from the subcomponents of the CPI at the highest degree of disaggregation. The data set contains 54 macroeconomic series and 243 CPI subcomponents from 1992 to 2009 for Mexico. We find that the factor models that include disaggregated data outperform the benchmark autoregressive model and the factor models containing alternative groups of macroeconomic variables. We provide evidence that using disaggregated price data improves forecasting performance. The forecasts of the factor models that extract the information from the CPI disaggregated data are as accurate as the forecasts from the survey of experts.     

Brazil Post‐Lula: Challenges and Opportunities Facing the Brazilian Business Environment

The Brazilian economy indicates great potential for future economic growth. An increasing and affluent middle class, expanding exports, and foreign reserves are testimonies of Brazil's recent accomplishments. The country, however, still faces a number of challenges that may compromise its sustainable long‐term economic goals and objectives. This article focuses on the Luiz Inacio “Lula” da Silva government's economic performance. The article's main findings show increasing bottlenecks being created as a result of the Lula government's eight years of low economic growth rates, which have penalized the country's competitiveness. The newly elected president, Dilma Rousseff, will inherit a substantial investment deficit in the areas of infrastructure, education, health care, research and development (R&D), and innovation, as well as Brazil's worst public debt/gross domestic product (GDP) ratio in the past 100 years. These conditions will prevent Brazil from growing and developing at faster rates. This article also elaborates on Dilma Rousseff's most recent statements and discusses likely future paths for the Brazilian economy. © 2012 Wiley Periodicals, Inc.     

Double Empirical Bayes Testing

Analysing data from large-scale, multiexperiment studies requires scientists to both analyse each experiment and to assess the results as a whole. In this article, we develop double empirical Bayes testing (DEBT), an empirical Bayes method for analysing multiexperiment studies when many covariates are gathered per experiment. DEBT is a two-stage method: in the first stage, it reports which experiments yielded significant outcomes and in the second stage, it hypothesises which covariates drive the experimental significance. In both of its stages, DEBT builds on the work of Efron, who laid out an elegant empirical Bayes approach to testing. DEBT enhances this framework by learning a series of black box predictive models to boost power and control the false discovery rate. In Stage 1, it uses a deep neural network prior to report which experiments yielded significant outcomes. In Stage 2, it uses an empirical Bayes version of the knockoff filter to select covariates that have significant predictive power of Stage 1 significance. In both simulated and real data, DEBT increases the proportion of discovered significant outcomes and selects more features when signals are weak. In a real study of cancer cell lines, DEBT selects a robust set of biologically plausible genomic drivers of drug sensitivity and resistance in cancer.