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Increased trapped heat in the atmosphere leads to global warming and economic activity is the primary culprit. This study proposes the nonlinear impact of economic activity on cooling degree days to develop a climate Kuznets curve (CKC). Further, this study explores the moderating role of higher education and renewable energy in diminishing the climate-altering effects of economic activity.

All the selected BRICS economies range from 1992 to 2020. The CKC analysis uses a distribution and outlier robust panel quantile autoregressive distributed lagged model.

Results confirmed a U-shaped CKC, controlling for population density, renewable energy, tertiary education enrollment and innovation. The moderating role of renewable energy and education can be exploited to tackle the progressively expanding climate challenges. Hence, education and renewable energy intervention can help in reducing CKC-based global warming.

This study highlighted the incorporation of climate change mitigating curriculum in education, so that the upcoming economic agents are well equipped to reduce global warming which must be addressed globally.

This study is instrumental in developing the climate change-based economic activity Kuznets curve and assessing the potential of higher education and renewable energy policy intervention.

The purpose of this paper is to assess the responses of different categories of government spending to changes in economic activity. In other words, the authors empirically revisit the validation of the Wagner’s law in a sample of 61 advanced and emerging market economies between 1995 and 2015.

The authors do so via panel data instrumental variables and time-series SUR approaches.

Evidence from panel data analyses show that the Wagner’s law seems more prevalent in advanced economies and when countries are growing above potential. However, such result depends on the government spending category under scrutiny and the functional form used. Country-specific analysis revealed relatively more cases satisfying Wagner’s proposition within the emerging markets sample. The authors also found evidence of counter-cyclicality in several spending items. All in all, the Wagner’s regularity seems more the exception than the norm.

While in the literature on the size of the public sector with respect to a country’s level of economic development has received much attention, the authors make several novel contributions: since some economists criticized Wagner’s law because of ambiguity of the measurement of government expenditure (Musgrave, 1969), instead of looking at aggregate public expenditures, the authors go much more granular into the different functions of government (to this end, the authors use the Classification of Functions of the Government nomenclature). The authors check the validity of the Law via an instrumental variable approach in a panel setting; after that, the authors take into account the phase of the business cycle using a new filtering technique to compute potential GDP (output gap); then, the authors cross-check the baseline results by considering alternative functional form specifications of the Law; and finally, the authors look at individual countries one at the time via SUR analysis.

Congress passed the Stop Trading on Congressional Knowledge (STOCK) Act of 2012, vesting the Securities and Exchange Commission with the clear legal authority to prosecute members of Congress (politicians) if they engage in insider trading. This paper aims to investigate whether members of Congress are informed traders even before they get elected to Congress, and thus helps assess whether the STOCK Act was a necessary piece of legislation.

This study compares the performance of politicians’ portfolios before and after they are elected to Congress using data from the 2004-2010 period. The authors use an event-study method to construct transactions-based calendar-time portfolios and use standard asset pricing models including capital asset pricing model (CAPM) to determine whether these portfolios earn abnormal returns (i.e. outperform the market).

The authors find weak and inconsistent evidence of abnormal returns in politicians’ portfolios that precede their election. They also find that it takes two consecutive terms in Congress for members to start making informed trades that earn themselves abnormal returns. However, these abnormal returns only accrue to those who serve on powerful committees.

The results in this paper provide support for the STOCK Act of 2012 by showing that members of Congress become informed traders while they serve in Congress. However, these results do not imply any wrongdoing for members of Congress, because the paper uses the pre-STOCK Act data (2004-2010 period).

This study is the first academic work that compares politicians’ portfolios before and after they get elected.

This paper aims to investigate the profile of the wind speed of a Cameroonian city for the very first time, as there is a growing trend for new wind energy installations in the West region of Cameroon. Two well-known artificial neural networks, namely, multi-layer perceptron (MLP) and nonlinear autoregressive network with exogenous inputs (NARX), were used to model the wind speed profile of the city of Bapouh in the West-region of Cameroon.

In this work, the profile of the wind speed of a Cameroonian city was investigated for the very first time since there is a growing trend for new wind energy installations in the West region of Cameroon. Two well-known artificial neural networks namely multi-layer perceptron (MLP) and nonlinear autoregressive network with exogenous inputs (NARX) were used to model the wind speed profile of the city of Bapouh in the West-region of Cameroon. The meteorological data were collected every 10 min, at a height of 50 m from the NASA website over a period of two months from December 1, 2016 to January 31, 2017. The performance of the model was evaluated using some well-known statistical tools, such as root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The input variables of the model were the mean wind speed, wind direction, maximum pressure, maximum temperature, time and relative humidity. The maximum wind speed was used as the output of the network. For optimal prediction, the influence of meteorological variables was investigated. The hyperbolic tangent sigmoid (Tansig) and linear (Purelin) were used as activation functions, and it was shown that the combination of wind direction, maximum pressure, maximum relative humidity and time as input variables is the best combination.

Maximum pressure, maximum relative humidity and time as input variables is the best combination. The correlation between MLP and NARX was computed. It was found that the MLP has the highest correlation when compared to NARX.

Two well-known artificial neural networks namely multi-layer perceptron (MLP) and nonlinear autoregressive network with exogenous inputs (NARX) were used to model the wind speed profile.

Weak-form rationality of fixed-event forecasts implies that forecast revisions should not be correlated. However, significant positive correlations between consecutive forecast revisions were found in most USDA forecasts for U.S. corn, soybeans, wheat, and cotton. This study developed a statistical procedure for correction of this inefficiency which takes into account the issue of outliers, the impact of forecast size and direction, and the stability of revision inefficiency. Findings suggest that the adjustment procedure has the highest potential for improving accuracy in corn, wheat, and cotton production forecasts.

In this second installment, the author addresses some of the problems associated with empirically validating contingent‐claim models for valuing risky debt. The article uses a simple contingent claims risky debt valuation model to fit term structures of credit spreads derived from data for U.S. corporate bonds. An essential component to fitting this model is the use of expected default frequency; the estimate of the firms' expected default probability over a specific time horizon. The author discusses the statistical and econometric procedures used in fitting the term structure of credit spreads and estimating model parameters. These include iteratively reweighted non‐linear least squares are used to dampen the impact of outliers and ensure convergence in each cross‐sectional estimation from 1992 to 1999.

Sustainable operations management is characterized by environmental, social and operational goals. The implementation of routines to protect and direct the effective use of human capital is proposed to potentially improve all three dimensions. However, functional managers with overlapping responsibilities at the plant-level might implement human capital routines based on their individual functional schemas. The purpose of this paper is to investigate whether functional managers have conflicting perceptions of human capital routines, due to narrow perceptions benefiting their own functional domain, and thus generate trade-offs.

A combination of matched survey and archival data from 198 manufacturing plants is used to explore the degree to which functional managers have conflicting perceptions of human capital routines and the effects of these perceptions on sustainability outcomes.

The results indicate that on average functional managers have conflicting perceptions that generate trade-offs between sustainability dimensions. However, when functional managers had a shared perception better outcomes on all sustainability dimensions are shown. Thus, human capital routines can be a powerful tool for sustainability only if senior management can promote a shared schema across functional managers.

Differently than most previous studies assuming shared sustainability goals within an organization, this study considers a multiplicity of functional actors with potentially varying perceptions about sustainability goals and links these to organizational routine implementation and outcomes. Additionally, the dynamic and subjective nature of organizational routines, such as human capital routines, is proposed to explain contradictory impacts in a multi-objective setting such as sustainable operations management.

This paper seeks to propose a univariate robust control chart for location and the necessary table of factors for computing the control limits and the central line as an alternative to the Shewhart X¯ control chart.

The proposed method is based on two robust estimators, namely, the sample median, MD, to estimate the process mean, μ, and the median absolute deviation from the sample median, MAD, to estimate the process standard deviation, σ. A numerical example was given and a simulation study was conducted in order to illustrate the performance of the proposed method and compare it with that of the traditional Shewhart X¯ control chart.

The proposed robust X¯_MDMAD control chart gives better performance than the traditional Shewhart X¯ control chart if the underlying distribution of chance causes is non‐normal. It has good properties for heavy‐tailed distribution functions and moderate sample sizes and it compares favorably with the traditional Shewhart X¯ control chart.

The most common statistical process control (SPC) tool is the traditional Shewhart X¯ control chart. The chart is used to monitor the process mean based on the assumption that the underlying distribution of the quality characteristic is normal and there is no major contamination due to outliers. The sample mean, X¯, and the sample standard deviation, S, are the most efficient location and scale estimators for the normal distribution often used to construct the X¯ control chart, but the sample mean, X¯, and the sample standard deviation, S, might not be the best choices when one or both assumptions are not met. Therefore, the need for alternatives to the X¯ control chart comes into play. The literature shows that the sample median, MD, and the median absolute deviation from the sample median, MAD, are indeed more resistant to departures from normality and the presence of outliers.

This study is concerned with the separate output effects of female and male education, as well as output effects of the educational gender gap. Several recent empirical studies have examined the gender effects of education on economic growth or on output level using the much exploited, familiar cross-country data. This paper aims to undertake a similar study of the gender effects of education on economic growth using a panel data across the provinces of Turkey for the period 1975-2000.

The theoretical basis of the estimating equations is the neoclassical growth model augmented to include separate female and male education capital and health capital variables. The methodology the authors use includes robust regression on pooled panel data controlling for regional and time effects. The results are found to be robust to a number of sensitivity analyses, such as elimination of outlier observations, controls for simultaneity and measurement errors, controls for omitted variables by including regional dummy variables, steady-state versus growth equations and different samples of developed and less-developed provinces of Turkey.

The main findings indicate that female education positively and significantly affects the steady-state level of labor productivity, while the effect of male education is in general either positive or insignificant. Separate examination of the effect of educational gender gap was to reduce output.

As evident in the literature, there is controversy surrounding the gender effects of education on growth. This paper provides new evidence on this issue from the perspective of a single country rather than a cross-country viewpoint.

The purpose of this paper is to propose a new linear-nonlinear data preprocessing-based hybrid model to achieve a more accurate result at a lower cost for wind power forecasting. For this purpose, a decomposed based series-parallel hybrid model (PKF-ARIMA-FMLP) is proposed which can model linear/nonlinear and certain/uncertain patterns in underlying data simultaneously.

To design the proposed model at first, underlying data are divided into two categories of linear and nonlinear patterns by the proposed Kalman filter (PKF) technique. Then, the linear patterns are modeled by the linear-fuzzy nonlinear series (LLFN) hybrid models to detect linearity/nonlinearity and certainty/uncertainty in underlying data simultaneously. This step is also repeated for nonlinear decomposed patterns. Therefore, the nonlinear patterns are modeled by the linear-fuzzy nonlinear series (NLFN) hybrid models. Finally, the weight of each component (e.g. KF, LLFN and NLFN) is calculated by the least square algorithm, and then the results are combined in a parallel structure. Then the linear and nonlinear patterns are modeled with the lowest cost and the highest accuracy.

The effectiveness and predictive capability of the proposed model are examined and compared with its components, based models, single models, series component combination based hybrid models, parallel component combination based hybrid models and decomposed-based single model. Numerical results show that the proposed linear-nonlinear data preprocessing-based hybrid models have been able to improve the performance of single, hybrid and single decomposed based prediction methods by approximately 66.29%, 52.10% and 38.13% for predicting wind power time series in the test data, respectively.

The combination of single linear and nonlinear models has expanded due to the theory of the existence of linear and nonlinear patterns simultaneously in real-world data. The main idea of the linear and nonlinear hybridization method is to combine the benefits of these models to identify the linear and nonlinear patterns in the data in series, parallel or series-parallel based models by reducing the limitations of the single model that leads to higher accuracy, more comprehensiveness and less risky predictions. Although the literature shows that the combination of linear and nonlinear models can improve the prediction results by detecting most of the linear and nonlinear patterns in underlying data, the investigation of linear and nonlinear patterns before entering linear and nonlinear models can improve the performance, which in no paper this separation of patterns into two classes of linear and nonlinear is considered. So by this new data preprocessing based method, the modeling error can be reduced and higher accuracy can be achieved at a lower cost.