Cement production and CO2 emission cycles in the USA: evidence from MS-ARDL and MS-VARDL causality methods with century-long data.

The cement industry is among the top three polluters among all industries and the examination of the nonlinear and cointegration dynamics between cement production and CO2 emissions has not been explored. Focusing on this research gap, the study employs a novel Markov-switching autoregressive distributed lag (MS-ARDL) model and its generalization to vector error correction, the MS-VARDL model, for regime-dependent causality testing. The new method allows the determination of nonlinear long-run and short-run relations, regime duration, and cement-induced-CO2 emission cycles in the USA for a historically long dataset covering 1900-2021. Empirical findings point to nonlinearity in all series and nonlinear cointegration between cement production and cement-induced CO2 emissions. The phases of regimes coincide closely with NBER's official economic cycles for the USA. The second regime, characterized by expansions, lasts twice as long relative to the first, the contractionary regime, which contains severe economic recessions, as well as economic crises, the 1929 Great Depression, the 1973 Oil Crisis, the 2009 Great Recession, and the COVID-19 Shutdown and Wars, including WWI and II. In both regimes, the adverse effects of cement production on CO2 emissions cannot be rejected with varying degrees both in the long and the short run. Markov regime-switching vector autoregressive distributed lag (MS-VARDL) causality tests confirm unidirectional causality from cement production to CO2 emissions in both regimes. The traditional Granger causality test produces an over-acceptance of causality in a discussed set of cases. Industry-level policy recommendations include investments to help with the shift to green kiln technologies and energy efficiency. National-level policies on renewable energy and carbon capture are also vital considering the energy consumption of cement production.

Chaos Structure and Contagion Behavior between COVID-19, and the Returns of Prices of Precious Metals and Oil: MS-GARCH-MLP Copula.

This study was developed for two purposes. The first one is detection of the existence of chaotic structure and uncertainty behaviour of the total number of people infected with the COVID-19 outbreak, and the returns of precious metals and oil by using the Lyapunov exponent, Kolmogrov and Shannon entropy tests. The additional aim was to analyze the co-movement and contagion behavior among COVID-19 infected persons, returns of precious metals and oil for the period of between December 30, 2019 and October 26, 2020 by MSGARCH-copula and MSGARCH-multi-layer perceptron methods. Confirmation of the results was provided by using the Diebold-Mariano (DM) tests and Wilcoxon signed rank (WS) tests. Accordingly, the empirical findings of this paper are as follows: The presence of chaotic structure and uncertainty behavior in the variables were determined by Lyapunov exponent, Kolmogorov and Shannon entropy tests. Additionally, co-movement and contagion behavior among the analyzed variables was established by the MS-GARCH-MLP copula method. Following, in the context of the results of confirmation, it has been determined that MSGARCH-MLPst has the best prediction performance under all criteria compared to MSGARCHst, or GARCHst. As a further result, COVID-19 had considerable effects on the returns of precious metals and oil prices, and there was co-movement and contagion behavior between COVID-19 and the returns of precious metals and oil.

Renewable energy and current account balance nexus.

Renewable energy is in the center for providing a sustainable energy future. Renewables are the key for overcoming the energy problems. This paper investigated the dynamic and causal relationship among renewable energy production, current account balance, energy imports, renewable energy consumption, and economic growth in the 1976-2019 period for G20 countries through panel Fourier bootstrapping ARDL model. According to empirical results, the evidence of cointegration between the selected variables was determined. Results indicate that economic growth, energy imports, energy consumption, and current account balance provide the important impacts on renewable energy generations. Our results determined that there is a unidirectional causality from energy imports, renewable energy consumption, and current deficit to renewable energy production. As countries decrease their dependence on the imports of energy, this may both increase the quality of environment through production of more renewable energy and reduce current account imbalances.

Refugees, governance, and sustainable environment: PQARDL method.

Although many variables that have adverse impacts on the sustainable environment are investigated from many aspects, some variables are missing. In this study, it will be simultaneously focused on the relation between refugees, governance, sustainable environment, economic growth, energy consumption, and supplementary explanatory variables, HDI, the trade deficit, and financial development, for Bangladesh, Ethiopia, Jordan, Lebanon, Pakistan, Sudan, and Uganda using the panel quantile autoregressive distributed lag (PQARDL) and causality methods for the 1996-2019 period. Long-run coefficients found by PQARDL method showed the evidence of the long-run relationship between the sustainable environment, refugee population, governance, economic growth, energy consumption, and explanatory variables. Both traditional and Dumitrescu and Hurlin (2012) causality tests determined the evidence of a unidirectional causality from political and economic governance to greenhouse gas (GHG) emissions and deforestation, as well as a unidirectional causality from refugees to GHG emissions and deforestation.

Terror and Voting Behavior of Turkey in 1986-2020.

This study investigated the existence of chaotic structure in voting behavior by considering non-economic and macroeconomic factors in Turkey during the period of 03.1986-01.2020. The chaotic structure among the analyzed variables was characterized by Lyapunov exponents that explore the chaotic dynamics of the series. Following, the effects of inflation, unemployment, economic growth and terror on party votes were analyzed by Fourier regression model. Then, the causality among the macroeconomic variables, terror and party groups was analyzed by the Granger causality method. According to our results, there is unidirectional causality from terror to all four party groups. In the context of macroeconomic variables, there is the evidence of bidirectional causality between conservative parties and inflation; unidirectional causality from inflation to center-right and center-left parties. There is no causality between nationalist parties and inflation. Furthermore, center-right and center-left parties have the evidence of no causality with unemployment while there is unidirectional causality from unemployment to conservative and nationalist parties. There is unidirectional causality from economic growth to conservative parties and bidirectional causality between center-right parties and economic growth. However, the center-left and nationalist parties are not the sources of Granger causality of economic growth, and there is no inverse Granger causality relationship between these variables. Therefore, it can be concluded that between the periods 03.1986-01.2020, there was no concern for economic growth in left-wing and nationalist-based parties in Turkey.

The impacts of governance on environmental pollution in some countries of Middle East and sub-Saharan Africa: the evidence from panel quantile regression and causality.

Governance is one of the basic determinants of pollution levels through property rights, the effective judicial system, etc. it is accepted as that bad governance because of inefficient regulatory structures, government bureaucracy, weak law enforcement, etc. support environmental pollution. In this context, in some countries of the Middle East and sub-Saharan Africa, it will be studied the impacts of governance on environmental pollution over the period of 1996-2018 by the panel quantile and Granger causality methods. The countries were selected by considering two different measurements, EPI (2020) index and governance index (2020). According to EPI (2020), these countries have low scores in terms of environmental quality, and in the governance index (2020), they have bad governance scores. In this study, in which panel quantile regression model is used, control variables are included in the model to prevent omitted-variable bias. The results of the analysis determined that the effect of governance on carbon emissions is positive, as well as that the effects of independent variables on CO2 emission are heterogeneous across quantities. Panel quantile regression revealed the evidence of the relation among the environmental pollution, two parameters of governance, FDI, financial development, human development index, and trade openness used as the explanatory variable and determined that government has the greatest positive effect on CO2 emission. On the other hand, by using traditional Granger causality and Dumitrescu-Hurlin causality methods, it was found the evidence of causality among governance and environmental pollution in the context of two parameters of governance. Accordingly, it was determined the evidence of unidirectional causality relation from political governance to environmental pollution and besides from economic governance to environmental pollution. And it was determined the evidence of unidirectional causality from FDI and the other explanatory variables to environmental pollution.

Chaotic Dynamics on Air Quality and Human Health: Evidence from China, India, and Turkey.

The chaotic structure of air pollution, human health, the evidence of cointegration and causality between the air pollution, transportation, tourism, construction, and manufacturing industries and the evidence of cointegration and causality between the air pollution, transportation, tourism, construction, and manufacturing industries and mortality rate were explored for China, India, and Turkey in the period of 1975-2018 by using four different methods. Firstly, Lyapunov and Shannon tests were applied to determine chaotic dynamics. The maximum Lyapunov test, for the selected variables, found the evidence of chaotic dynamics. Secondly, Fourier ADF and NL tests were applied. Fourier unit root test determined stationary of the variables. Thirdly, bootstrapping autoregressive distributed lag with Fourier transformation (FBARDL) was used to determine the evidence of cointegration between the variables with two different models. FBARDL test determined that the manufacturing, tourism, transport, and construction industries, air pollution, and mortality rate have evidence of cointegration in different two models. Lastly, the causality test with Fourier transformation was used to determine the direction of causality between the variables. Granger causality test determined that there is evidence of one-way causality running from transportation, tourism, construction, and manufacturing industries to air pollution and mortality rates. Accordingly, the results of this paper suggest air pollution and human health have chaotic behaviors. Air pollution has a complex, multi-variable, and multi-coupling system. Air pollutants influencing factors and air pollution itself have adverse effects on human health.

The relationship between cement production, mortality rate, air quality, and economic growth for China, India, Brazil, Turkey, and the USA: MScBVAR and MScBGC analysis.

The related literature reveal that the papers on environmental pollution do not sufficiently analyse the cement production which is an important determinant of air pollution and health problems by using econometric methods. To fill this gap, this paper aims to examine the relationship between cement production, air pollution, mortality rate, and economic growth by employing MS Bayesian Vector Autoregressive (MScBVAR) and Markov Switching Bayesian Granger causality (MScBGC) approaches from 1960 to 2017 for China, Brazil, India, Turkey and the USA. MSIA(2)-BVAR(1) model for China, MSIAH(2)-BVAR(3) models for India, MSIAH(3)-BVAR(2) for Brazil, and MSIAH(3)-BVAR(1) for Turkey, and MSIAH(2)-BVAR(2) for the USA were selected. The MScBGC results revealed that the cement production is granger cause of mortality rate, air pollution, and economic growth in all regimes for China, India, Brazil, Turkey, and the USA.

Chaotic Structure of Oil Prices, Inflation and Unemployment.

This article investigates the chaotic relationship among inflation rate, unemployment rate and oil prices over the period of January, 1974-October, 2018 in the USA. This study complements the previous studies on this subject. However, it differs from the existing literature in examination of inflation-unemployment trade-off by a neural network model in which oil prices are considered as an exogenous variable, and in analyzing the chaotic causality relation among the variables by Hristu-Varsakelis and Kyrtsou and Bai nonlinear causality tests. The results first suggested by a class of neural network model, which was the multi-layer perceptron (MLP), pointed out an important relation among the analyzed variables. Accordingly, oil price changes have substantial effect on unemployment and inflation. Following, the empirical findings of Hristu-Varsakelis and Kyrtsou and Bai nonlinear causality tests show that there is a unidirectional chaotic relation from oil price to inflation, from oil price to unemployment and from inflation to unemployment.

Markov-switching vector autoregressive neural networks and sensitivity analysis of environment, economic growth and petrol prices.

The paper aims at evaluating the nonlinear and complex relations between CO2 emissions, economic development, and petrol prices to obtain new insights regarding the shape of the environmental Kuznets curve (EKC) in the USA and in the UK in addition to introducing a newly proposed nonlinear approach. Within this respect, the paper has three purposes: the first one is to combine the multilayer perceptron neural networks (MLP) with Markov-switching vector autoregressive (MS-VAR) type nonlinear models to obtain the MS-VAR-MLP model. The second is to utilize one of the largest datasets in the literature covering the 1871-2016 period, a long span of data starting from the late eighteenth century. Since the emission, economic development, and petrol price relation is subject to nonlinearity and trajectory changes due to many historical events, the development of the MS-VAR-MLP model is a necessity to contribute to the ongoing debate regarding the shape of the EKC curve and the stability of the relation. The third purpose is to develop the MS-VAR-MLP-based regime-dependent sensitivity analysis, which eases the visual interpretation of the nonlinear causal relationships, which are allowed to have asymmetric interactions in different phases of the expansionary and recessionary periods of the business cycles. Our results provide clear deviations from the findings in the literature: (i) the shape of the EKC curve cannot be assumed to be stable and is subject to regime dependency, nonlinearity, and magnitude dependency; (ii) the forecast results suggest that incorporation of regime switching and neural networks provide significant improvement over the MS-VAR counterpart; and (iii) for both USA and UK and for the 1871-2016 period, the positive impacts of economic growth on emissions cannot be rejected for the majority of the phases of the business cycles; however, the magnitude of this effect is at various degrees. In addition, the incorporation of petrol price provides significant findings considering its effects on emission and economic growth rates. The analysis suggest clear deviations from the expected shape of the EKC curve and puts forth the necessity to utilize more complex empirical methodologies to evaluate the EKC since the emissions-economic development relation is more complex than it was assumed. Following these findings, several policy recommendations are provided. Lastly, the proposed MS-VAR-MLP methodology is compared with the MS-VAR model and various advantages and disadvantages are enumerated.

Impact of military on biofuels consumption and GHG emissions: the evidence from G7 countries.

It was aimed to test the relation among the greenhouse gases emissions, economic growth, biofuels consumption, and militarization in G7 countries during the 1985-2015 period by Pedroni 1995 and panel Johansen tests and two long-run estimators-dynamic OLS and fully modified OLS. Long-run estimators found that economic growth and militarization have statistically significant positive impact on CO2 emission of G7 countries. Furthermore, the panel causality tests were applied: Dumitrescu and Hurlin (Econ Model 29(4):1450-1460, 2012) and panel Granger causality. These tests determined the causal relationship between the variables. The results of this paper implied that economic growth and biofuels consumption depend on militarization, and economic growth and militarization are granger causes of the greenhouse gases emissions.

Economic growth and CO2 emissions: an investigation with smooth transition autoregressive distributed lag models for the 1800-2014 period in the USA.

The study aims to combine the autoregressive distributed lag (ARDL) cointegration framework with smooth transition autoregressive (STAR)-type nonlinear econometric models for causal inference. Further, the proposed STAR distributed lag (STARDL) models offer new insights in terms of modeling nonlinearity in the long- and short-run relations between analyzed variables. The STARDL method allows modeling and testing nonlinearity in the short-run and long-run parameters or both in the short- and long-run relations. To this aim, the relation between CO2 emissions and economic growth rates in the USA is investigated for the 1800-2014 period, which is one of the largest data sets available. The proposed hybrid models are the logistic, exponential, and second-order logistic smooth transition autoregressive distributed lag (LSTARDL, ESTARDL, and LSTAR2DL) models combine the STAR framework with nonlinear ARDL-type cointegration to augment the linear ARDL approach with smooth transitional nonlinearity. The proposed models provide a new approach to the relevant econometrics and environmental economics literature. Our results indicated the presence of asymmetric long-run and short-run relations between the analyzed variables that are from the GDP towards CO2 emissions. By the use of newly proposed STARDL models, the results are in favor of important differences in terms of the response of CO2 emissions in regimes 1 and 2 for the estimated LSTAR2DL and LSTARDL models.

Correction to: Economic growth and CO2 emissions: an investigation with smooth transition autoregressive distributed lag models for the 1800-2014 period in the USA.

The original publication of this paper contains a mistake.

The causal link among militarization, economic growth, CO2 emission, and energy consumption.

This paper examines the long-run and the causal relationship among CO2 emissions, militarization, economic growth, and energy consumption for USA for the period 1960-2013. Using the bound test approach to cointegration, a short-run as well as a long-run relationship among the variables with a positive and a statistically significant relationship between CO2 emissions and militarization was found. To determine the causal link, MWALD and Rao's F tests were applied. According to Rao's F tests, the evidence of a unidirectional causality running from militarization to CO2 emissions, from energy consumption to CO2 emissions, and from militarization to energy consumption all without a feedback was found. Further, the results determined that 26% of the forecast-error variance of CO2 emissions was explained by the forecast error variance of militarization and 60% by energy consumption.

Modeling Markov switching ARMA-GARCH neural networks models and an application to forecasting stock returns.

The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the proposed Markov-switching MS-ARMA-FIGARCH, APGARCH, and FIAPGARCH processes are further augmented with MLP, Recurrent NN, and Hybrid NN type neural networks. The MS-ARMA-GARCH family and MS-ARMA-GARCH-NN family are utilized for modeling the daily stock returns in an emerging market, the Istanbul Stock Index (ISE100). Forecast accuracy is evaluated in terms of MAE, MSE, and RMSE error criteria and Diebold-Mariano equal forecast accuracy tests. The results suggest that the fractionally integrated and asymmetric power counterparts of Gray's MS-GARCH model provided promising results, while the best results are obtained for their neural network based counterparts. Further, among the models analyzed, the models based on the Hybrid-MLP and Recurrent-NN, the MS-ARMA-FIAPGARCH-HybridMLP, and MS-ARMA-FIAPGARCH-RNN provided the best forecast performances over the baseline single regime GARCH models and further, over the Gray's MS-GARCH model. Therefore, the models are promising for various economic applications.

An empirical analysis of the effects of consanguineous marriages on economic development.

In this study, development experiences toward economic development are investigated to provide an alternative analysis of economic development, human capital, and genetic inheritance in the light of consanguineous marriages. The countries analyzed in the study are discussed in accordance with consanguineous marriage practices and classified by their per capita gross domestic product (GDP) growth. A broad range of countries are analyzed in the study. Arab countries that experienced high rates of growth in their gross national income during the twentieth century but failed to fulfill adequate development measures as reflected in the growth in national income, countries undergoing transition from tight government regulation to free market democracy, and African nations that have experienced complications in the process of development show important differences in the process of economic development. It is shown that the countries that have reached high average development within the context of per capita GDP have overcome problems integral to consanguineous marriage.