We examine the relationship between wealth and health through prominent growth indicators and cognitive ability. Cognitive ability is represented by nutritional status. In this study, the proxy variable for nutritional status is BMI since there is a strong relationship between cognitive ability and nutrition. We use the reduced form equation in the cubic specification of time preference rate to estimate this relationship. We assume that the time preference rate is one of the outputs of cognitive ability. The growth indicators utilized are GDP per capita, schooling, overall and manufacturing productivities, and savings. We estimate our models using the FE, GMM estimators, and long difference OLS and IV estimation through balanced panel data for 47 countries for the 1980-2009 period, which is a representative period of the neo-liberal and globalization economic policy implications. Furthermore, by using the 1980-2009 period, we may eliminate the ripple effects of the 2007-2009 financial crisis. Although there is ample evidence that the association between GDP per capita, overall and manufacturing productivities, and BMI could be cubic, we take the results of the long-difference quadratic specification into consideration and conclude that the relationship between all prominent growth indicators and BMI is inverse U-shaped. In other words, cognitive ability has a significant potential to progress growth and economic development only in a healthy status.

This study aims to investigate the efficient market hypothesis for a number of non-renewable resources over the period 1980Q1-2019Q4. We use two different stationarity tests, one is designed to capture smooth breaks, and the other one is designed to detect abrupt changes in the prices. With the use of the stationarity tests, we aim to overcome the low power issue of the commonly utilized unit root tests with stationary but persistent data. Moreover, given the inference of the existing studies for the importance of structural breaks in the analysis of stochastic properties of non-renewable natural resource prices, we utilize both smooth and instant breaks in our analysis to account for the fact that misspecification of the functional form of the breaks could be as problematic as ignoring the breaks. Our empirical results reveal significant evidence of trend stationarity in almost all prices with structural changes related to market-specific and global economic events, though concerns on economic uncertainties appeared to be effective, especially on precious metals. The only exception is silver, with stationarity being rejected for all specifications considered in the paper, suggesting that shocks to silver are mostly permanent in nature and it is characterized by the efficient market hypothesis.

This paper investigates the hypothesis of unemployment hysteresis for GIPS countries (Greece Ireland, Portugal, and Spain) over the period 1998(4)-2019(4). While most of the existing empirical studies assume constant order of integration for unemployment over the sample period, we consider the possibility that, like many macroeconomic variables, unemployment might display changes in persistence, which might result in potential switches between the natural rate and hysteresis hypotheses. In this respect, we adopt a multiple persistence change methodology. Our empirical results suggest that the structural natural rate (hysteresis) hypothesis is supported for Ireland (Portugal) over the entire sample without any change in persistence of the unemployment rate. For the cases of Greece and Spain, on the other hand, our results propose that unemployment is characterized by multiple changes in persistence with the observed dates for persistence changes coinciding with the Great Recession, the European Sovereign debt crisis, and the deepening of economic and labor market reforms launched to retrain the impact of the crises in those countries.

The electricity price forecasting (EPF) is a challenging task not only because of the uncommon characteristics of electricity but also because of the existence of many potential predictors with changing predictive abilities over time. Particularly, how to account for all available factors and extract as much information as possible is the key to the production of accurate forecasts. To address this long-standing issue in a way that balances complexity and forecasting accuracy while facilitating the traceability of the predictor selection procedure, the method of Bootstrap Aggregation (bagging), which is a variant shrinkage estimation approach for the estimation of large scale models, is proposed in this paper. To forecast day-ahead electricity prices in a multivariate context for six major power markets we construct a large scale pure-price model (in addition to some stochastic models that are commonly applied in the literature) and apply the bagging approach in comparison with the popular Least Absolute Shrinkage and Selection Operator (LASSO) estimation method. Our forecasting study reveals that with its superior forecasting performance and its computationally simple algorithm, the bagging emerges as a strong competitor to the commonly applied LASSO approach for the short-term EPF. Further analysis for the variable selection for the bagging and LASSO approaches suggests that the differentiation in the forecast performances of two approaches might be due to, inter alia, their structural differences in the explanatory variables selection process. Moreover, to account for the intraday hourly dependencies of day-ahead electricity prices, all our models are augmented with latent factors, and a substantial improvement is observed only in the forecasts from models covering a relatively limited number of predictors, while almost no improvement is obtained in the forecasts from the large scale model estimated through LASSO and bagging techniques.