Ecologic correlations of selected food groups with disease incidence and mortality in Switzerland.

Background: There is little information regarding the impact of diet on disease incidence and mortality in Switzerland. We assessed ecologic correlations between food availability and disease.Methods: In this ecologic study for the period 1970-2009, food availability was measured using the food balance sheets of the Food and Agriculture Organization of the United Nations. Standardized mortality rates (SMRs) were obtained from the Swiss Federal Office of Statistics. Cancer incidence data were obtained from the World Health Organization Health For All database and the Vaud Cancer Registry. Associations between food availability and mortality/incidence were assessed at lags 0, 5, 10, and 15 years by multivariate regression adjusted for total caloric intake.Results: Alcoholic beverages and fruit availability were positively associated, and fish availability was inversely associated, with SMRs for cardiovascular diseases. Animal products, meat, and animal fats were positively associated with the SMR for ischemic heart disease only. For cancer, the results of analysis using SMRs and incidence rates were contradictory. Alcoholic beverages and fruits were positively associated with SMRs for all cancer but inversely associated with all-cancer incidence rates. Similar findings were obtained for all other foods except vegetables, which were weakly inversely associated with SMRs and incidence rates. Use of a 15-year lag reversed the associations with animal and vegetal products, weakened the association with alcohol and fruits, and strengthened the association with fish.Conclusions: Ecologic associations between food availability and disease vary considerably on the basis of whether mortality or incidence rates are used in the analysis. Great care is thus necessary when interpreting our results.

Forecasting Inflation Using Dynamic Model Averaging

We forecast quarterly US inflation based on the generalized Phillips curve using econometric methods which incorporate dynamic model averaging. These methods not only allow for coe¢ cients to change over time, but also allow for the entire forecasting model to change over time. We nd that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coe¢ cient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period.

Forecasting the European Carbon Market

In an effort to meet its obligations under the Kyoto Protocol, in 2005 the European Union introduced a cap-and-trade scheme where mandated installations are allocated permits to emit CO2. Financial markets have developed that allow companies to trade these carbon permits. For the EU to achieve reductions in CO2 emissions at a minimum cost, it is necessary that companies make appropriate investments and policymakers design optimal policies. In an effort to clarify the workings of the carbon market, several recent papers have attempted to statistically model it. However, the European carbon market (EU ETS) has many institutional features that potentially impact on daily carbon prices (and associated nancial futures). As a consequence, the carbon market has properties that are quite different from conventional financial assets traded in mature markets. In this paper, we use dynamic model averaging (DMA) in order to forecast in this newly-developing market. DMA is a recently-developed statistical method which has three advantages over conventional approaches. First, it allows the coefficients on the predictors in a forecasting model to change over time. Second, it allows for the entire fore- casting model to change over time. Third, it surmounts statistical problems which arise from the large number of potential predictors that can explain carbon prices. Our empirical results indicate that there are both important policy and statistical bene ts with our approach. Statistically, we present strong evidence that there is substantial turbulence and change in the EU ETS market, and that DMA can model these features and forecast accurately compared to conventional approaches. From a policy perspective, we discuss the relative and changing role of different price drivers in the EU ETS. Finally, we document the forecast performance of DMA and discuss how this relates to the efficiency and maturity of this market.

A Comparison Of Forecasting Procedures For Macroeconomic Series: The Contribution Of Structural Break Models

This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeconomic time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. However, we find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance. However, there are also many cases where simple, rolling OLS forecasts perform well.

A Comparison Of Forecasting Procedures For Macroeconomic Series: The Contribution Of Structural Break Models

This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeconomic time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. However, we find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance. However, there are also many cases where simple, rolling OLS forecasts perform well.

Forecasting with Medium and Large Bayesian VARs

This paper is motivated by the recent interest in the use of Bayesian VARs for forecasting, even in cases where the number of dependent variables is large. In such cases, factor methods have been traditionally used but recent work using a particular prior suggests that Bayesian VAR methods can forecast better. In this paper, we consider a range of alternative priors which have been used with small VARs, discuss the issues which arise when they are used with medium and large VARs and examine their forecast performance using a US macroeconomic data set containing 168 variables. We nd that Bayesian VARs do tend to forecast better than factor methods and provide an extensive comparison of the strengths and weaknesses of various approaches. Our empirical results show the importance of using forecast metrics which use the entire predictive density, instead of using only point forecasts.

UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?

Block factor methods offer an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors reflecting different blocks of variables (e.g. a price block, a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for different parsimonious forecasting models to hold at different points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to different forecasting model as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output and inflation using 139 UK monthly time series variables, we find that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods.

Forecasting Inflation Using Dynamic Model Averaging

We forecast quarterly US inflation based on the generalized Phillips curve using econometric methods which incorporate dynamic model averaging. These methods not only allow for coe¢ cients to change over time, but also allow for the entire forecasting model to change over time. We nd that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coe¢ cient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period.

Mortality from cardiovascular and cerebrovascular diseases in Europe and other areas of the world: an update.

OBJECTIVE: To update trends in mortality from coronary heart diseases (CHD) and cerebrovascular diseases (CVD) over the period 1981-2004 in Europe, the USA, Latin America, Japan and other selected areas of the world. METHODS: Age-standardized mortality rates were derived from the World Health Organization database. Joinpoint analysis was used to identify significant changes in trends. RESULTS: In the European Union (27 countries), CHD mortality in men declined from 139/100,000 in 1985-1989 to 93/100,000 in 2000-2004 (-33%). In women, the fall was from 61/100,000 to 44/100,000 (-27%). In this area, a decline by over 30% was also registered in CVD mortality for both sexes. In the Russian Federation and other countries of the former Soviet Union, CHD rates in 2000-2004 were exceedingly high, around 380/100,000 men and 170/100,000 women in Russia, 430 for men and 240 for women in Ukraine, 420 and 200 in Belarus. For CVD, a similar situation was registered, with mortality rates of 226/100,000 for men and 159/100,000 for women in 2004 in the Russian Federation, and more than 24% increase since the late 1980s for men and 15% for women. CHD and CVD mortality continued to decline in most Latin American countries, Australia and other areas considered, including Asia (even if with marked differences). CONCLUSION: Although mortality from CHD and CVD continues to decline in several areas of the world including most countries of Europe and of the America providing data and Australia, unfavourable trends were still observed in the Russian Federation and other countries of the former Soviet Union, whose recent rates remain exceedingly high.

Forecasting US bond default ratings allowing for previous and initial state dependence in an ordered probit model

In this paper we investigate the ability of a number of different ordered probit models to predict ratings based on firm-specific data on business and financial risks. We investigate models based on momentum, drift and ageing and compare them against alternatives that take into account the initial rating of the firm and its previous actual rating. Using data on US bond issuing firms rated by Fitch over the years 2000 to 2007 we compare the performance of these models in predicting the rating in-sample and out-of-sample using root mean squared errors, Diebold-Mariano tests of forecast performance and contingency tables. We conclude that initial and previous states have a substantial influence on rating prediction.

Inequalities in Child Mortality in India: A District-Level Analysis

This paper measures the degree of inequality in child mortality rates across districts in India, using data from the 1981, 1991 and 2001 Indian population censuses. The results show that child mortality is more concentrated in less developed districts in all three census years. Further, between 1981 and 2001, the inequality in child mortality seems to have increased to the advantage of the more developed districts (i.e., there was an increasing concentration of child mortality in less developed districts). However, the inequality in female child mortality rates seems to have declined between 1991 and 2001, even as it increased – albeit at a slower rate than before – for male child mortality rates. In the decomposition analysis, it is found that while a more equitable distribution of medical facilities and safe drinking water across districts did contribute towards reducing inequality in child mortality between 1981 and 1991, different levels of structural change among districts were responsible for a very large part of the inequality in child mortality to the advantage of the more developed districts in all three census years. Other variables which played important roles in increasing inequality included a measure of infrastructure development, female literacy, and a social group status variable. The paper concludes with some brief comments on the policy implications of the findings.

Bayesian forecasting with highly correlated predictors

This paper considers Bayesian variable selection in regressions with a large number of possibly highly correlated macroeconomic predictors. I show that by acknowledging the correlation structure in the predictors can improve forecasts over existing popular Bayesian variable selection algorithms.

Hodgkin's lymphoma mortality in the Americas, 1997-2008: Achievements and persistent inadequacies.

Although therapeutic advancements have made Hodgkin's lymphoma (HL) a largely curable disease, trends in HL mortality have been variable across countries. To provide updated information on HL mortality in the Americas, overall and 20-44 years age-standardized (world population) mortality rates from HL were derived for the 12 Latin American countries providing valid data to the World Health Organization database and with more than two million of inhabitants. For comparative purpose, data for the United States and Canada were also presented. Trends in mortality over the 1997 to 2008 period are based on joinpoint regression analysis. Declines in HL mortality were registered in all Latin American countries except in Venezuela. In most recent years, HL mortality had fallen to about 0.3/100,000 men and 0.2/100,000 women in Argentina, Brazil, Chile, Colombia, Ecuador and Guatemala, that is, to values similar to North America. Despite some declines, rates remained high in Cuba (1/100,000 men and 0.7/100,000 women), Costa Rica and Mexico as well as in Venezuela (between 0.5 and 0.6/100,000 men and between 0.3 and 0.5/100,000 women). In young adults, trends were more favorable in all Latin American countries except Cuba, whose rates remained exceedingly high (0.8/100,000 men and 0.6/100,000 women). Thus, appreciable declines in HL mortality were observed in most Latin America over the last decade, and several major countries reached values comparable to North America. Substantial excess mortality was still observed in Cuba, Costa Rica, Mexico and Venezuela, calling for urgent interventions to improve HL management in these countries.

Social isolation causers mortality by disrupting energy homeostrasis in ants

Social deprivation can have negative effects on the lives of social animals, including humans, yet little is known about the mechanisms by which social withdrawal affects animal health. Here we show that in the carpenter ant Camponotus fellah, socially isolated workers have a greatly reduced life span relative to ants kept in groups of ten individuals. By using a new tracking system, we found that social isolation resulted in important behavioral changes and greatly increased locomotor activity. The higher activity of single ants and their increased propensity to leave the nest to move along the walls suggested that the increased mortality of isolated ants might stem from an imbalance of energy income and expenditure. This view was supported by the finding that while isolated ants ingested the same amount of food as grouped ants, they retained food in the crop, hence preventing its use as an energy source. Moreover, the difference in life span between single and grouped individuals vanished when ants were not fed. This study thus underlines the role of social interactions as key regulators of energy balance, which ultimately affects aging and health in a highly social organism.

Modeling Dependence Structure and Forecasting Portfolio Value-at-Risk with Dynamic Copulas

We study the asymmetric and dynamic dependence between financial assets and demonstrate, from the perspective of risk management, the economic significance of dynamic copula models. First, we construct stock and currency portfolios sorted on different characteristics (ex ante beta, coskewness, cokurtosis and order flows), and find substantial evidence of dynamic evolution between the high beta (respectively, coskewness, cokurtosis and order flow) portfolios and the low beta (coskewness, cokurtosis and order flow) portfolios. Second, using three different dependence measures, we show the presence of asymmetric dependence between these characteristic-sorted portfolios. Third, we use a dynamic copula framework based on Creal et al. (2013) and Patton (2012) to forecast the portfolio Value-at-Risk of long-short (high minus low) equity and FX portfolios. We use several widely used univariate and multivariate VaR models for the purpose of comparison. Backtesting our methodology, we find that the asymmetric dynamic copula models provide more accurate forecasts, in general, and, in particular, perform much better during the recent financial crises, indicating the economic significance of incorporating dynamic and asymmetric dependence in risk management.