Search

This study examined agenda-setting relationships across the media, politics, and the public, while accounting for contextual boundaries from 1978 to 2018 in the United States. Our findings indicate that (1) for the overall model, the media and the public influence the prominence of economic concerns on the political agenda, but the political agenda has no agenda-setting power; (2) under a Republican president, the prominence of economic topics in the media and political agendas influence each other, the media and the public agendas influence each other, and the public influences the political agenda; and (3) under a Democrat president, attention paid toward economic topics in the media and political agendas influence each other, as do the political and public agendas. Of particular note for agenda-setting theorists is our finding that the media significantly sets the agenda for the public under a Republican presidential condition and not under other conditions. Additionally, these relationships differ longitudinally; for example, the media responds to economic changes quicker and for more prolonged periods under a Republican than a Democrat. This study advances the agenda-setting theory by presenting novel findings about agenda-setters in action and by demonstrating the unique complexities of modeling longitudinal nonlinear relationships. [ABSTRACT FROM AUTHOR]

In psychology, the use of portable technology and wearable devices to ease participant burden in data collection is on the rise. This creates increased interest in collecting real-time or near real-time data from individuals within their natural environments. As a result, vast amounts of observational time series data are generated. Often, motivation for collecting this data hinges on understanding within-person processes that underlie psychological phenomena. Motivated by the body of Dr. Peter Molenaar's life work calling for analytical approaches that consider potential heterogeneity and non-ergodicity, the focus of this paper is on using idiographic analyses to generate population inferences for within-person processes. Meta-analysis techniques using one-stage and two-stage random effects meta-analysis as implemented in single-case experimental designs are presented. The case for preferring a two-stage approach for meta-analysis of single-subject observational time series data is made and demonstrated using an empirical example. This provides a novel implementation of the methodology as prior implementations focus on applications to short time series with experimental designs. Inspired by Dr. Molenaar's work, we describe how an approach, two-stage random effects meta-analysis (2SRE-MA), aligns with recent calls to consider idiographic approaches when making population-level inferences regarding within-person processes. [ABSTRACT FROM AUTHOR]

Estimating the long-run variance (LRV) is crucial for several econometric issues. Constructing reliable heteroskedasticity autocorrelation consistent (HAC) variance-covariance matrices and implementing efficient generalized method of moments (GMM) estimation procedures require a consistent LRV estimate. A good VARHAC estimator (HAC matrix with the spectral density at frequency zero constructed using a VAR spectral estimation) requires accurately estimating the sum of autoregressive (AR) coefficients; however, a criterion that minimizes the innovation variance does not necessarily yield the best spectral estimate. This article implements an optimal VARHAC estimator using an alternative information criterion, considering the bias in the sum of the parameters for the AR estimator of the spectral density at frequency zero. [ABSTRACT FROM AUTHOR]

Purpose: Competitively multipolar international system demands bilateral and multilateral partnerships. Joint innovation signals close partnerships. Regional proximity of Central and Eastern Europe (CEE) to the European Union (EU) defines its research and development objectives. These objectives are additionally subjected to the USA's geopolitical strategy in this geographical area. Hence, CEE's limited resources require limited resources make international innovation cooperation. This paper aims to analyse whether and how CEE countries make international innovation cooperation decisions. Design/methodology/approach: This paper used Organisation for Economic Co-operation and Development (OECD) database of total patent applications filed to the patent cooperation treaty (PCT) with co-applicants from abroad, where co-patents with at least one foreign inventor present a measure of international innovation partnership. A vector autoregression analysis and impulse response function were used to analyse international innovation partnership choices of eight CEE OECD countries for the period 1990–2018. Findings: Innovation with the EU is of collaborative nature, commonly displaying complementary properties with the rest of the examined innovation partners, while co-patenting with the Russia and China act as substitutes or complements. Co-patenting with Russia is the most versatile, displaying both properties of collaboration and competition. Some countries exhibit complementarity in co-patenting activities with multiple partners. The significance levels of these relationships vary, indicating varying degrees of impact. Overall, these findings highlight the complex dynamics of co-patenting activities and the influence of different partners on countries' collaborative innovation strategies. Research limitations/implications: In addition to significant relationships, insignificant relationships as well as those that could bring about greater synergy are flagged in the paper. Those relationships portray possible direction into which national funds could be channelled to incite cooperation between different sectors and countries, especially as innovation partnerships are not always successful and require a long time period to materialise. Originality/value: By examining bilateral innovation partnerships, this study provides an insight into the strategic political and economic spheres of influence in the CEE region. [ABSTRACT FROM AUTHOR]

The research discusses management strategies for addressing production risks in the polyculture cultivation of milkfish and vaname shrimp in Bulukumba Regency. Effective management is necessary to minimize these risks and optimize profits for business operators. Production risk management is carried out using Value at Risk (VaR) analysis, based on observations and meetings with business operators and fisheries services. This approach helps identify and address sources of production risk in the polyculture of milkfish and vaname shrimp. The findings show that both the probability and impact of risk sources are higher for vaname shrimp than for milkfish. Feed is the largest source of risk for milkfish, while disease poses the greatest risk for vaname shrimp. Conversely, feed is a minor risk factor for vaname shrimp, and disease is a smaller concern for milkfish. Risk mitigation strategies include: (1) carefully monitoring the dosage of saponin poison used to eradicate pests; (2) ensuring the correct dosage and application of urea fertilizer, TSP, and feed; (3) controlling water quality; and (4) selecting high-quality seeds. [ABSTRACT FROM AUTHOR]

This study analyzes the response of economic fundamentals to a misalignment shock of the real effective exchange rate in Korea. The estimation results of the equilibrium exchange rate determination model and time series model show that there is no significant difference in the direction of the deviation from equilibrium and that the won is significantly undervalued during the period before 1988, or during the currency and global financial crises. The cumulative impulse response analysis of the VAR model over the full period shows that an upward shock to the deviation from the equilibrium exchange rate reduces the GDP gap and inflation rate, while the effect on the call rate is not statistically significant. Furthermore, an upward misalignment shock initially worsens the goods and services balance, but the deficit in the goods and services balance shrinks significantly over time. In rolling regressions analysis, the entire sample is divided into two periods to estimate the impulse response function from the first period, and then the same procedure is repeated by moving the sample forward one by one. The cumulative impulse response results show that, as is the case for the full period, a positive exchange rate misalignment shock initially reduces the GDP gap, inflation, and worsens the goods and services balance, but the impact of this upward shock on these variables becomes increasingly weaker in the more recent sample. It also shows that the negative impact of upward shocks on the current account is smoothed out more recently during periods of undervaluation than during periods of overvaluation. [ABSTRACT FROM AUTHOR]

The term "Dutch disease" is commonly used to refer to economic problems that arise in resource-rich countries, but there are other factors that can lead to the outcomes of Dutch disease. In particular, foreign capital inflows, by focusing on profitable sectors, can increase the competitive advantage in certain sectors of the economy, but can also bring about problems such as the appreciation of the local currency and changes in the balance of foreign trade. In clearer terms, foreign capital inflows can also lead to Dutch Disease. The Turkish economy experienced a period of high foreign capital inflows as a result of local policies and the global economic context after the 2001 economic crisis. This study investigates econometrically whether there is a hypothesis of Dutch Disease arising from foreign capital inflows in the Turkish economy. The analyses using the VAR method cover the period from 2003:1 to 2020:4. In this context, an econometric model that takes foreign trade channels into account has been created. The results of the foreign trade model show that foreign capital inflows resulted in a depreciation of the exchange rate, leading to a decrease in exports and an increase in imports, thus causing a contraction in tradable sectors. In conclusion, empirical findings regarding the validity of the Dutch Disease hypothesis for the relevant period have been obtained. [ABSTRACT FROM AUTHOR]

The carbon market was established to reduce carbon dioxide emissions. The traditional fossil energy market, new energy market, and carbon market have interrelated effects such as substitution, demand, and production inhibition, which can potentially lead to risk transmission. This study examines the nonlinear volatility correlation between China's carbon market, China's new energy market, and the international crude oil futures market. Seven submarkets within these three markets are selected for analysis. By measuring volatility risk through the conditional heteroscedasticity of returns, the analysis of nonlinear Granger causality networks reveals that, from a nonlinear perspective, risk primarily spills over through the paths of "International crude oil futures market → China's carbon market" and "International crude oil futures market → China's new energy market → China's carbon market." China's carbon market serves as a recipient of risk, with minimal spillover effects. Therefore, further optimization is needed for the framework of China's carbon market to enhance its asset allocation function and promote its spillover influence. Investors in China's carbon market should consider both linear and nonlinear risks from China's new energy market and the international crude oil futures market, and take appropriate measures to facilitate the sustainable growth of Chinese enterprises. [ABSTRACT FROM AUTHOR]

This paper examines the factors influencing Kosovo's economic growth from 2009 to 2022, specifically investigating the relationship between export, capital formation, consumption, and economic growth using co-integration analysis and the Vector Autoregressive Model (VAR). The findings indicate that exports of goods and services, as well as household consumption, negatively affect economic growth. Conversely, gross capital formation positively impacts economic growth. The study underscores the complexity of economic growth, highlighting the varied significance of different determinants in different contexts. Key findings reveal that while export and gross capital formation are significant contributors to economic growth, household consumption shows an insignificant relationship to GDP. This research contributes to the ongoing debate on the critical factors influencing economic growth, providing empirical evidence from the context of Kosovo and enhancing our understanding of these dynamics, thus offering new insights for policymakers. [ABSTRACT FROM AUTHOR]

The half-normal distribution is composited with the Pareto model to obtain a uni-parametric distribution with a heavy right tail, called the composite half-normal-Pareto distribution. This new distribution is useful for modeling positive data with atypical observations. We study the properties and the behavior of the right tail of this new distribution. We estimate the parameter using a method based on percentiles and the maximum likelihood method and assess the performance of the maximum likelihood estimator using Monte Carlo. We report three applications, one with simulated data and the others with income and expenditure data, in which the new distribution presents better performance than the Pareto distribution. [ABSTRACT FROM AUTHOR]

This study investigates the impact of inflation rate fluctuations on economic growth in China, with a particular focus on potential non-linear characteristics. The global economic impact of the COVID-19 pandemic notably heightens the study's relevance. The research that the unidirectional causal relationship from inflation to economic growth in China first strengthens and then weakens over time. Furthermore, there is an inflation rate threshold effect on economic growth, identified at 2%. Below this threshold, inflation positively influences economic growth, whereas above it, the impact turns negative. This finding underscores the importance of balancing economic growth with inflation control in the formulation of monetary policy. [ABSTRACT FROM AUTHOR]

Should the South African Reserve Bank (SARB) lower the inflation target (IT) band? Does lowering the IT band impact the relationship between GDP growth and inflation? This paper explores these questions considering the SARB Governor, Lesetja Kganyago statements that there is a need to lower the IT band from 3–6% to a point target of 3%. We estimate the VAR model to determine whether the passthrough of positive GDP growth shocks to inflation is nonlinear in South Africa. The inflation effects are delineated into bands (i) above 6% (ii) between 4.5% and 6% (iii) between 3% and 4.5% (iv) between 0% and 3% and (v) when there are no IT bands. Evidence reveals that the passthrough is elevated when inflation exceeds 6% and is lower when inflation is within the (i) 3 to 4.5% and (ii) 0 to 3% IT bands. The passthrough from positive GDP growth shocks is more than halved when inflation is less than 3%. The policy implication is that lowering the IT band from 3 to 6% to 0 to 3% will reduce the passthrough of GDP growth shocks to inflation. It allows expansionary monetary to have more real effects as prices are more rigid in the low inflation environment. [ABSTRACT FROM AUTHOR]

AbstractBackground: Relations among psychological variables are assumed to be complex and to vary over time. Personalized networks can model multivariate complex interactions. The development of time-varying networks allows to model the variation of parameters over time. Objectives: We aimed to determine the value of time-varying networks for clinical practice. Methods: We applied time-varying mixed graphical models (TV-MGM) and time-varying vector autoregressive models (TV-VAR) to intensive longitudinal data of nine participants with depressive symptoms (n = 6) or anxiety (n = 3). Results: Most of the participants showed temporal changes in network topology within the assessment period of 30 days. Time-varying networks of participants with small, medium, and large time variability in edge parameters clearly show the different temporal evolvements of dynamic interactions between variables. The case example indicates clinical utility but also limitations to the application of time-varying networks in clinical practice. Conclusion: Time-varying network models provide a data-driven and exploratory approach that could complement current diagnostic standards by reflecting interacting, often mutually reinforcing processes of mental health problems and by accounting for variation over time. They can be used to generate hypotheses for further confirmatory and clinical testing. [ABSTRACT FROM AUTHOR]

The media, speculators, investors, and governments throughout the world have all become increasingly interested in cryptocurrencies in recent years. The price swings of cryptocurrencies are notoriously unstable and have a high level of volatility. This study focused on modeling that volatility of cryptocurrencies, the purpose of this study is to identify the most suitable or appropriate innovation distribution and different GARCH Models to model the returns of the most popular cryptocurrencies. The majority of our work was focused on the top ten cryptocurrencies, but we also extended our analysis to 377 cryptocurrencies. To describe the time dependent volatility of the cryptos, we utilize eleven different GARCH models, including the sGARCH, iGARCH, GJRGARCH, eGARCH, tGARCH, AVGARCH, CSGARCH, ALLGARCH, NGARCH, APARCH, and NAGARCH. For the research period of September 14, 2014 to November 10, 2022, the daily closing prices of cryptocurrencies are collected. The underlying innovation(error) distribution are assumed to be from one of the following eight distributions of Normal, Student's t, Generalized Error, Skew Normal, Skew Student's t, Skew Generalized error, Normal Inverse Gaussian and Generalized Hyperbolic Distribution. Each GARCH-type model was fitted with this eight innovations. [ABSTRACT FROM AUTHOR]

The implementation of Video Assistant Referees (VARs) in 2018 has had a significant impact on the multi-billion-dollar soccer industry. As the most popular and watched sport globally, soccer's financial stakes are high, with clubs, leagues, broadcasters, sponsors, and fans heavily invested in the game. The ongoing debate surrounding the VAR system brings to light the intricate balance between preserving the authenticity of football (soccer) and harnessing technology to improve accuracy. It is crucial to strike the right equilibrium in order to uphold football's metaphorical power and sustain the timeless joy it has brought to fans throughout generations. In this context, Immanuel Kant's philosophy can offer valuable insights into the utilization of VARs in soccer. According to Kantian ethics, using VARs can be justified if it serves to enhance fairness and accuracy, aligning with the moral duties of referees. Nevertheless, it is important to consider the potential dehumanizing effects and the necessity of preserving the value of human judgment in the game. Therefore, this paper aims to explore in-depth the intricate dynamics that arise when technology is integrated into traditional practices, emphasizing the significance of critical reflection on the implications of such advancements. [ABSTRACT FROM AUTHOR]

This work analyses the asymmetric response of conventional and shale oil rig counts to WTI oil price returns. Our analysis shows that the rig count time series exhibited a structural change after the oil glut of 2014. All series are non-stationary in each sub-period but not cointegrated. Therefore, after controlling for possible confounding factors, a vector auto regressive (VAR) model is set up. Our specification accounts for the possible role of oil production and distinguishes between positive and negative oil price changes. It is shown that shale and conventional rig counts reacted differently in each subperiod to signed changes in oil price. Subsequently, by evaluating the response of rig counts to oil price shocks, their intensity and duration over time, we observe that the shale oil rig count reacts more intensively to positive than to negative oil price changes. On the contrary, the conventional rig count exhibits a modest reaction only to positive price changes. Finally, we robustify our findings by focusing on the data of the Permian basin, on the one hand, and the Anadarko, Bakken, Eagle Ford and Niobrara, on the other hand, which are characterized by different patterns in the number of Drilled but not Completed wells. [ABSTRACT FROM AUTHOR]

Oil palm has been identified as one of the key agricultural products that can diversify Nigeria's economy. However, its production and export have been hampered by capital constraints. Consequently, the Agricultural Credit Guarantee Scheme Fund (ACGSF) was introduced to encourage lending to farmers and agro-processors to enhance agricultural productivity. Therefore, this study aims to determine the effectiveness of ACGSF allocations to the oil palm production subsector. Time series data on oil palm production, as well as data on ACGSF allocations to oil palm and cash crop production, prime lending rate and commercial banks' loans to agriculture were obtained from FAOSTAT and the Central Bank of Nigeria. Vector autoregression, Granger causality and impulse response functions were used to estimate the short-run relationship. The results reveal that ACGSF allocations to the oil palm subsector and commercial banks' loans to agriculture have negative and positive effects, respectively, on oil palm production. There was unidirectional causality from ACGSF and commercial banks to agriculture to oil palm production. The findings suggest expansion and close monitoring of the ACGSF scheme, and the intensification and esterification of oil palm production. The study shows that estimating the effect of ACGSF on overall agricultural productivity does not provide deep insight into commodity-specific credit packages. [ABSTRACT FROM AUTHOR]

Based on the daily closing price data of the internet finance index from 2012 to 2021, the two-zone MS-GARCH(1,1) model is firstly used to describe the fluctuation process of the internet finance index, and the optimal model MS(2)-EGARCH(1,1)-st is selected through analysis. The results show that the return rate of the internet finance index has two clearly divided states: the mild fluctuation state is more persistent than the shape fluctuation state, and the shape fluctuation state has asymmetric effects. Secondly, the combined model of MS-EGARCH model and quantile regression (QR) model are used to measure the risk of internet finance return series, and the success rate is calculated by Kupiec backtracking test method. The results show that the success rate of value at risk (VaR) obtained by QR-MS(2)-EGARCH(1,1)-st is higher. [ABSTRACT FROM AUTHOR]

The impact of shadow banking on financial stability remains a controversial issue today due to the size and complexity of these activities and the inadequate regulatory frameworks for systemic risks. In this context, shadow banking has become the focus of financial regulators due to its potential effects on financial stability. Therefore, this study was conducted to understand the impact of shadow banking on financial stability. In this study, the relationship between shadow banking and financial instability is examined using the VAR method for the example of the United States, covering the period 2000-2020. In order to create an accurate model, firstly unit root tests were performed, followed by autocorrelation and heteroscedasticity tests. The findings were found to be significant and Johansen cointegration test was applied. In the cointegration results, it was seen that the series were cointegrated, that is, they moved together in the long run. Finally, a Granger causality test was conducted between shadow banking and financial instability, and according to the empirical findings, it was concluded that there was a causality from shadow banking to financial instability for the period in question. [ABSTRACT FROM AUTHOR]

The objective of this research was to analyze the relationship between economic policy uncertainty and the Brazilian stock market. The research adopted a quantitative, descriptive and econometric approach. The operationalization of the study involved collecting data from the Economic Policy Uncertainty Index and Ibovespa between January 2006 and December 2022. Data analysis was carried out using autoregressive vector models. The results showed a bidirectional relationship between the variables. An increase in uncertainty resulted in a reduction in market performance, while an increase in market performance led to a decrease in uncertainty levels. [ABSTRACT FROM AUTHOR]

Inflation and economic growth are two crucial indicators for any country in the world. In light of the importance of these two economic indicators, the forecast of economic growth and inflation has become a significant topic that national governments have traditionally prioritized. This study aims to apply popular machine learning algorithms such as KNN and MLP to build models for predicting economic growth and inflation. We also provide a comparison of the predictive accuracy between these machine learning algorithms and traditional forecasting models such as VAR and LASSO. Specifically, we employ techniques such as VAR, LASSO, KNN, and multi-layer perceptron (MLP) to construct forecasting models for Vietnam’s economic growth and inflation using data collected from 1996 to 2021. The accuracy of the models is assessed using three indices: RMSE, MAE, and MSE. The empirical results show that according to all three indicators, RMSE, MAE, and MSE, the forecasting models of economic growth and inflation by the MLP model are the most accurate. Based on the results, we have concluded that the MLP model is a valuable tool for future forecasting because it can describe the nonlinear relationships between variables in the model and visually map them. [ABSTRACT FROM AUTHOR]

This article considers a first order continuous time vector autoregression with random coefficients. We discuss some difficulties that arise when the exact discrete analogue is used for estimating the continuous time parameters and provide an estimation method based on an approximate discrete model. Some expressions for the estimator of the drift parameter matrix, for its approximated bias and for the covariance matrix of the parameter estimates are derived. The finite sample performance of the proposed method is studied by a Monte Carlo experiment. We also illustrate the advantages of our model in an application on the expectations theory of the term structure of interest rates. Results show that the performance of the proposed methodology is good, and allowing for time variation on coefficients results in large reductions in the root mean square error of the parameter estimates. [ABSTRACT FROM AUTHOR]

Purpose: Oil price shocks greatly impact the global economy, but the effects vary among countries. While higher oil prices benefit oil-exporting countries, they harm the economic performance of oil-importing nations, and vice versa for lower oil prices. However, economic relations, such as trade, can mitigate the impacts of oil price shocks on both groups. In this paper, the authors aim at estimating the effects of oil price shocks on the major net oil-exporting and net oil-importing countries while accounting for international trade. Design/methodology/approach: The authors derive a reduced form of a macro model and set up a Panel VAR model to estimate the direct and indirect impacts of oil price shocks on economic growth. The sample includes data on macroeconomic variables from 30 oil-exporting and oil-importing countries that comprise more than 73 percent of the world's economy. The authors construct the spillover variables using bilateral trade matrix. To control for institutional and structural variations across the countries, they are divided into four groups of developed and developing oil-exporting and oil-importing countries. Findings: The results reveal that all oil-exporting countries have significantly benefited from oil price shocks, although trade has dampened the effect. The positive growth effect has been more pronounced in oil-exporting developing countries. The impact of oil price shocks on oil-importing countries has been negative with a one-year delay, but not statistically significant, and trade has only had a small effect. The effect has been more substantial in oil-importing developing countries. Research limitations/implications: One of the limitations of this study is the focus on trade as the main spillover channel. Given the data availability, other channels such as foreign investment and financial markets can also be included in future studies. Practical implications: Removing trade restrictions would help both oil-exporting and oil-importing countries to mitigate the negative impacts of the oil price shocks. However, the asymmetric oil-macroeconomy relationship across oil-exporting and oil-importing countries puts oil-exporting countries in a more vulnerable position as they cannot rely on trade with oil-importing countries to reduce the negative impacts of lower oil prices on their growth. Therefore, it is crucial for oil-exporting countries to reassess their oil-dependent development plans and invest their oil revenues in non-oil sectors to diversity their economies and prepare for a future with reduced dependence on oil. Social implications: The recent technological advances, structural changes, and increasing energy efficiency suggest that major oil-importing countries will become less dependent on oil in near future. As a result, oil-exporting countries will also need to undergo structural changes in order to sustain their income level. These significant changes will have important social implications, particularly in the labor market, during the transition, for which preparation will be necessary. Originality/value: While the literature on the total impact of oil price shocks on either oil-exporting or oil-importing countries is rich, studies on their spillover impacts are limited. Recent research has shown that trade and migration can affect the impact of oil price shock on the economy in federated countries such as Canada. However, the trade effect on oil price shocks in the international level, where countries are subject to different regulations/restrictions and institutional variations, remains scarce. By considering the trade relationship between different groups of oil-exporting and oil-importing countries, the authors aim to contribute to the literature of the global impacts of oil price shocks on the world economy. [ABSTRACT FROM AUTHOR]

In recent years, some authors have warned of the increasingly widespread use of risk management techniques by financial institutions, arguing that this can cause the market to become more unstable. To analyse these claims, we present a model based on evolutionary game theory of a financial market, where part of the investors use the VaR technique to manage their risk. We study the evolution of this market through simulation, and we confirm that the use of risk management models can induce instability regimes in the market, characterised by sudden changes in the asset price and sharp increases in the volatility. [ABSTRACT FROM AUTHOR]

Purpose: In this paper, the authors investigate whether coronavirus disease 2019 (COVID-19) impacts household finances, like household debt repayments in the UK. Design/methodology/approach: This paper employs a vector autoregressive (VAR) model that nests neural networks and uses Mixed Data Sampling (MIDAS) techniques. The authors use data information related to COVID-19, financial markets and household finances. Findings: The authors' results show that household debt repayments' response to the first principal component of COVID-19 shocks is negative, albeit of low magnitude. However, when the authors employ specific COVID-19-related data like vaccines and tests the responses are positive, insinuating the underlying dynamic complexities. Overall, confirmed deaths and hospitalisations negatively affect household debt repayments. The authors also report low persistence in household debt repayments. Generalised impulse response functions (IRFs) confirm the main results. As draconian measures, the lockdowns are eased and the COVID-19 shocks are diminishing, and household financial data converge to the levels prior to the pandemic albeit with some lags. Originality/value: To the best of the authors' knowledge, this is the first study that examines the impact of the pandemic on household debt repayments. The authors' findings show that policy response in the future should prioritise innovation of new vaccines and testing. [ABSTRACT FROM AUTHOR]

Purpose: The literature mostly investigates the business cycle transmission of the United Kingdom (UK) and France as a part of a wider group (e.g. European Exchange Rate Mechanism or G7), despite their historical links and regional significance. Thus, herein paper aims to analyse the inter-dependence of these economies and how a shock from one of them affects the other for the data since 1978 to 2019. Design/methodology/approach: In this paper, first, preliminary statistics were calculated in order to describe the historical relationship between these countries. The econometric part estimates the vector auto-regression model (VAR) to assess the inter-dependence of the economies. VAR model allows further to inspect the impulse response functions that shows the shock dynamics from one country to another. In order to verify if a shock from one of the economies is important to another, the study uses granger causality test. Findings: The study establishes a strong link between these countries. A business cycle is transmitted significantly between the economies of France and UK, with a single standard deviation shock from France resulting in a long term effect of 0.4% change in gross domestic product (GDP) of UK and 1% vice versa. Additionally changes in GDP of both of the countries significantly Granger-cause change to GDP of the corresponding economy. Originality/value: This is the first empirical study investigating the business cycle transmission between France and UK and providing a quantitative assessment of their inter-dependence. [ABSTRACT FROM AUTHOR]

Using Vector autoregression (VAR), variance decomposition and Pearson correlation techniques, this study investigates the impact of digital finance on banks' credit allocation in Nigeria between 2009Q1 and 2019Q4. The study specifically aims to examine the impact of digital finance (proxied with value of Automated Teller Machine (ATM), Point of Sales (POS) and web payment (WEBPAY) transactions) on total sectorial credit allocation of deposit money banks in Nigeria. This study finds no long-run relationship between digital finance and credit allocation of deposit of money banks in Nigeria. Furthermore, digital finance and banks' credit allocation were found to have strong and positive correlation in Nigeria. It is also unveiled that, unlike the value of POS transactions which have negative and non-significant impact, the value of ATM and WEBPAY transactions have positive but non-significant impacts on banks' credit allocation in Nigeria. It can therefore be concluded that, though, a strong and positive correlation exists between digital finance and banks' credit allocation but the former (digital finance) does not exert significant impact on the latter (credit allocation function of deposit money banks) in Nigeria in the period of study. There is therefore the need for the current pace of digital finance adoption in Nigeria be intensified in order for it to achieve the desirable impact on banks' credit allocation function in the country. [ABSTRACT FROM AUTHOR]

• Mental Health Concerns (MHC) index is constructed using Google Trends data. • We study if economic uncertainty raises concerns about mental health in the U.S. • A positive shock to uncertainty is shown to raise MHC index. This paper investigates if heightened economic uncertainty raises concerns about mental health in the U.S. We first quantify such concerns by constructing a composite Mental Health Concerns index, using time series of the intensity of Google search queries related to mental disorders and distress. This index i) rises significantly during the three recessionary episodes and ii) comoves negatively with survey responses that reflect views on current consumer sentiment or on future economic conditions. We find that the concerns regarding mental health substantially increase after an unexpected hike in economic uncertainty; uncertainty not only channels through its negative impacts on economic activity, but also directly affects the level of concerns. Our findings suggest that an uncertainty shock can have a far-reaching impact on overall welfare of economic agents by leaving them more concerned about mental health. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic, which emerged in December 2019 in China for the first time, affected the whole world in a short while. The pandemic spread to all countries not only damaged human health but also led to the losses in the global economy as the restrictions placed on struggling with the outbreak have resulted in shrinking the world economy. In the meantime, different sectors have been affected differently by the economic situation in line with their own characteristics. In this context, the aim of the present study was to examine the effects of the Covid-19 pandemic crisis on the Food, Service, Technology, Wholesale-Retail Trade, Electricity, and Transportation sectors in the Borsa Istanbul (BIST) indice using time series analysis. In this direction, the impulse variable in the analysis consisted of the number of daily active cases for the period 17.03.2020-31.05.2022, and the response variables consisted of the BIST sector indices. To analyze the relationship between the variables, the ADF and Zivot-Andrews unit root tests were used and it was determined that the variables were stationary at the level. In order to determine the causality relationship between the variables, we ran the Granger Causality test based on the Vector Autoregressive (VAR) model. The findings revealed that there is a unidirectional Granger Causality relationship from the number of active cases to the Food, Service, Technology, Wholesale-Retail Trade, Electricity, and Transportation sectors, and also that these sectors respond differently to the change in the number of active cases. [ABSTRACT FROM AUTHOR]

Distortion risk measures play an essential role in the fields of finance and risk management. In this paper, we present a new distortion risk measure with mixed methods. We then investigate the optimal reinsurance problem under the new risk measure and the closed-form solutions of optimal reinsurance policies are obtained. As special cases of the new distortion risk measure, VaR and GlueVaR are considered in the application of risk management. [ABSTRACT FROM AUTHOR]

Mining operations provide the coal required to satisfy more than 36% of the electricity demand worldwide. Coal mining releases methane gas which constitutes a significant risk for the safety of coal miners working underground. Therefore, early warning of rising methane gas concentrations is critical to preventing accidents and loss of life. The prediction of methane concentration is complicated by its dependence on many factors and the presence of stochastic fluctuations. This paper introduces a new forecasting approach for methane gas emissions in underground coal mines. The proposed approach employs univariate and multivariate time series forecasting methods. Multivariate methods incorporate barometric pressure as a predictor of gas concentration. The data used herein were collected from the Atmospheric Monitoring Systems of three active underground coal mines in the eastern USA. The performance of three time series methods is compared: the univariate autoregressive integrated moving average (ARIMA), the multivariate vector autoregressive (VAR), and ARIMA with exogenous inputs (ARIMAX). The optimal model per method (ARIMA, VAR, ARIMAX) is selected based on the Akaike Information Criterion. The forecasting performance is assessed using cross-validation to determine the best overall model. It is concluded that all three methods can, in most cases, satisfactorily predict methane gas concentrations in underground coal mines. [ABSTRACT FROM AUTHOR]

The banking sector is one of the leading sectors in Turkey as well as in the rest of the world, and its existence is important in terms of economy. The banks in the system, on the one hand, carry out their intermediary activities, on the other hand, they operate as a credit creation authority. While performing these activities, banks try to create various sources of income. The most important source of income is the interest item. However, as a result of the changes and transformations experienced in recent years, non-interest income items have also become a powerful source of income. This is important for the profitability of banks as well as the functioning of the credit transfer mechanism. In this study, it has been investigated to what extent interest incomes and non-interest incomes affect the total loans extended by banks. Thus, it has been tried to determine which income item is more effective on bank loans and in which direction it interacts with the help of the VAR model within the scope of Granger causality analysis. As a result, while a mutual and positive causality relationship was determined between interest incomes and loans, it was found that noninterest income was also effective together with interest income. [ABSTRACT FROM AUTHOR]

Background: Self-rated health among old adults (SHOA) indicates individuals' subjective assessments and evaluations of their overall health based on objective physical circumstances. The purpose of this study was to analyze the current state and influencing factors of the subjective perception-based self-rated health (SH) by qualifying selected older adults with similar objective physical conditions, as well as to explore the equality and changing trends of SHOA based on influencing factors. Methods: This study designed a cross-sectional study, conducted in three provinces in east, central and west China, and included 1,153 older adults (> = 60 years) with intact physical condition (IPC). The current state of SHOA and its influencing factors were analyzed using mean comparisons and Logistic regression (LR) models. The equality level and trend of SHOA's effect on health literacy, health habits, and access to health care were determined using the Lorenz curve, Gini coefficient, and Vector Autoregression (VAR) model. Results: The mean SHOA with IPC was 74.37 ± 13.22. Findings from LR modeling indicated that SHOA with IPC was mainly influenced by age and communication methods (P < 0.05). It was also observed that the total Gini coefficient of the allocation of SHOA with IPC based on communication methods was equal to 0.0188, and the VAR results showed that the total effect of change in SHOA on health literacy among older adults was negative and its duration of the effect exceeded 50. Conclusions: The SHOA with IPC was shown to be better and was primarily influenced by age and communication methods. The observed effect of SHOA on health literacy was negative and lasting. To improve SHOA with IPC even further, policymakers could consider promoting the use of modern and convenient communication methods (such as smartphones) through training and purchasing subsidies, as well as focusing on increasing sustained attention and promoting health literacy and behavior among older adults with improved SH. [ABSTRACT FROM AUTHOR]

We develop statistical tools for time series analysis of large multivariate datasets, when a few core series are of principal interest and there are many potential auxiliary predictive variables. The methodology, based on Vector Autoregressions (VAR), handles the case where unrestricted fitting is precluded by a large number of series and a huge parameter space. In particular, we adopt a forecast error criterion and use Granger-causality tests in a sequential manner to build a VAR model that targets the main variables. This approach affects variable reduction (or equivalently, sparsity restrictions) in a computationally fast way that remains feasible for large dimensions. The search for the best model results in a VAR, fitted with a selection of supporting series, that has the best possible forecast performance with respect to the core variables. We apply the statistical methodology to model real Gross Domestic Product and the national Unemployment Rate, two time series widely monitored by economists and policy-makers, based on a large set of Quarterly Workforce Indicators comprising various major sectors of the economy and different measures of labor market conditions. [ABSTRACT FROM AUTHOR]

Although the 2008 global financial crisis has radically changed monetary policies, it is seen that the Taylor Rule still has a wide place in practice. The aim of this study is to examine the reaction function of the Central Bank of the Republic of Turkey with the Extended Taylor Rule model for two sub-periods. In this way, it is determined how sensitive the monetary policy in Turkey has changed in the post-crisis period to which macroeconomic variables. For this purpose, monetary policy for the period 2011:04-2019:07 was modeled with the extended Taylor Rule and estimated with the linear Vector Autoregressive (VAR) method. In the analysis, it is divided into two sub-periods according to the CBRT presidents. The period between 2011:04-2016:03 represents Erdem Basci, and the period between 2016:04-2019:07 represents the Murat Cetinkaya period. The data used in the econometric analysis are the CBRT weighted average funding cost, inflation rate, industrial production index and USD/TL exchange rate. According to the impulse response analysis, the main findings are that the CBRT reacts increasingly to a shock in the exchange rate in both periods and does not react to a shock in the industrial production index. The severity of the shocks is different in the two periods. In the Cetinkaya period, the interest rate reacted more severely to the fluctuations in the exchange rate compared to the Basci period. Variance decomposition also supports this result. In the Cetinkaya period, the share of the exchange rate in the variance of the interest rate is higher. As a result, the reaction function reacts differently to different variables in both sub-periods. [ABSTRACT FROM AUTHOR]

From 1996 to 2016, Brazil faced great fluctuations in both its exchange and interest rates and simultaneously a huge growth of its agriculture has taken place. Examining the literature that deals with the relationships among these three variables (interest rate, exchange rate and agriculture GDP) we verified that monetary and exchange shocks can affect directly and/or indirectly (through relative prices) the agriculture GDP. However, these effects can be coincident or contrary between them. What actually happened in Brazil from 1996 to 2016? This article aimed to access the direct and indirect effects of monetary and exchange rate shocks on Brazil´s agriculture GDP taking into consideration years ranging from 1996 thru 2016. Using quarterly data for the 1996-2016 period, autoregressive vector models (VAR and VEC) were estimated. The exchange rate and monetary shocks were analyzed considering three different interest rate types, namely: the current Brazil´s Federal Funds Rate (Nominal SELIC rate), real interest rate of agricultural loans, and ex-ante real interest rate. We also analyzed the effect of the exchange rate separately on prices paid and on prices received by the agriculture (both make up the relative prices of agriculture) using an ARDL (Distributed Lag Self-Regressive Model) model with monthly data. Our econometric results revealed that both direct and indirect effects of monetary shocks were negative on the agriculture growth while the direct and indirect effects of exchange rate shocks were mutually contradictory themselves on the Brazil´s agriculture GDP. Because of the technical changes that have taken place during the 2000s and the 2010s, which turned some of the most representative crops more dependent on imported inputs, and the hedging behavior that the firms have adopted during the first two decades of the 21st century, which protects them from exchange oscillations, the agricultural sector lost sensitivity to the exchange rate variations in some extent. Regarding policy implications, it could be said that monetary policy can be efficient in the promotion of the agricultural sector, and, in fact, Brazilian government has been doing such through the rural credit subsidized interest rate. Exchange policy, on the other hand, proved to be inefficient in favoring the agriculture, or, in other words, the government can freely exercise exchange policy without regards to harm the agricultural sector. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to gain insights useful to explain the loan puzzle: the unexpected increase of loans to firms in case of a monetary tightening. To this end, the authors develop the analysis using several loan categories distinguished by lender, scope and borrower. This approach helps to unveil significant differences on how those categories respond to the same shock and allow to evaluate possible alternative explanations for such differences. Design/methodology/approach: The paper is empirical. The analysis is based on a large vector auto-regression, estimated using Bayesian techniques and has as object the US economy. Findings: The findings support a supply-side explanation of the loan puzzle, i.e. banks reshuffle their portfolio in favor of short-term business loans after a monetary tightening. Moreover, the authors achieve the following results. First, the analysis shows that loans to small firms increase as well, but less than what observed with large firms: small firms stay between large firms and households. Second, considering advances and other loans allows to conclude that finance companies behave very much as banks. Third, some limited evidence suggests that not just industrial and commercial loans to firms might increase but also more long-term loans, such as mortgages. Originality/value: The authors develop an analysis, based on state-of-the-art Bayesian techniques, that reveals the differential response of well-distinguished loan categories to several shocks; monetary and real shocks in the first place. After showing their heterogenous response, the authors discuss it in detail, with specific reference to supply and demand factors of credit intrinsic to the transmission mechanism. With respect to previous contributions, the authors consider a plurality of loan categories functional to understand the reason behind each specific response. This allows to conclude in favor of supply factors as an explanation of the unexpected increase of loans to corporate firms in case of a monetary shock. [ABSTRACT FROM AUTHOR]

Energy stocks are potentially a hedge against inflation and have a number of advantages over other forms of energy investing. This motivates us to study on portfolio management of energy stocks. We compare the performance of proposed GARCH-EVT-vine copula models under three different dimensions with other competing models using energy stocks from the U.S. market. In our proposed model, we use static C- and D-vine copulas. We compare the accuracy and efficiency of different models in forecasting portfolio VaR and CVaR. We also examine whether the proposed models yield greater economic and statistical performances than the competing models in a tactical asset allocation framework. Our findings indicate that the proposed models perform best overall. In fact, the relatively better performance of the proposed model is even more prominent when the portfolio size increases. Further, the comparative analysis between GARCHEVT- static vine and GARCH-EVT-dynamic vine copula models produces mixed results. [ABSTRACT FROM AUTHOR]

It is important to understand the features of an integrated renewable energy power system, especially for deregulated systems. The greatest obstacle to assimilating renewable energy generators with the existing electrical system is their unpredictability. Because wind energy is inconsistent, incorporating it into an established power system necessitates more planning. The effects of wind farm (WF) incorporation with fuel cells and a unified power flow controller (UPFC) on electric losses, voltage profile, generating price, and the economics of the system in a deregulated power market are examined in this paper. An impact analysis of integrating wind farms into controlled and uncontrolled situations is conducted. At two randomly selected locations in India, the real-time statistics of the actual wind speed (AWS) and forecasted wind speed (FWS) were merged for this study. The surplus charge rate and deficit charge rate are intended to evaluate the imbalance cost which is arising from the difference between anticipated and true wind speeds to determine the economics of the system. Customers are always trying to find electricity that is reliable, inexpensive, and efficient due to the reconfiguration of the power system. As a consequence, the security limitations of the system may be surpassed or might function beyond the safety limit, which is undesirable. In the last section, heuristic algorithms, such as sequential quadratic programming (SQP), artificial bee colony algorithms (ABC), and moth-flame optimization algorithms (MFO), are employed to analyze economic risk. In the real-time energy market, it also covers how the fuel cells and UPFC are utilized to rectify the WF integration's deviation. Economic risk evaluation approaches include value-at-risk (VaR) and conditional value-at-risk (CVaR). A modified IEEE 30-bus test system is used throughout the whole project. [ABSTRACT FROM AUTHOR]

The aim of this study is to econometrically investigate the determinants of inflation in the Turkish economy. In the study that analyses the period of 2006-2021, the effect of exchange rate, interest rate, real money supply, budget deficit and current account deficit on the inflation rate was estimated under three different time series techniques: Stepwise regression model, Vector Autoregressive (VAR) model and Autoregressive Distributed Lag (ARDL) model. According to the findings, the exchange rate has a positive effect on the inflation rate in both the short and long-run. The change in real money supply affects negatively inflation in both periods. Interest rate has a positive effect on the inflation rate. However, this effect is marginally low. Although the budget deficit affects inflation in the short-run, it is not a determinant on inflation in the long-run. In the study, strong findings that the current account deficit is determinant on inflation were not obtained. In terms of the variables discussed in the period of 2006-2021, the most statistically effective factor on inflation in the long run in the Turkish economy is the changes in the exchange rate. [ABSTRACT FROM AUTHOR]

We consider a multiplicative decomposition of the financial returns to improve the density forecasts of financial returns. The multiplicative decomposition is based on the identity that financial return is the product of its absolute value and its sign. Advantages of modeling the two components are discussed. To reduce the effect of the estimation error due to the multiplicative decomposition in estimation of the density forecast model, we impose a moment constraint that the conditional mean forecast is set to match with the sample mean. Imposing such a moment constraint operates a shrinkage and tilts the density forecast of the decomposition model to produce the improved maximum entropy density forecast. An empirical application to forecasting density of the daily stock returns demonstrates the benefits of using the decomposition and imposing the moment constraint to obtain the improved density forecast. We evaluate the density forecast by comparing the logarithmic score (LS), the quantile score (QS), and the continuous ranked probability score (CRPS). We contribute to the literature on the density forecast and the decomposition models by showing that the density forecast of the decomposition model can be improved by imposing a sensible constraint in the maximum entropy framework. [ABSTRACT FROM AUTHOR]

Airports, as integral components of the global aviation industry, experience dynamic changes in air passenger traffic load, which are also related to the trends of local urban development and airspace restrictions of nearby airports. Using time-series data from 2006 to 2019, this study comprehensively applied the autoregressive distributed lag and vector auto-regression mode approaches to identify causal relationships between the urban development factors, including GDP, population, tourism industry, industrial structure, etc., of Tianjin city (China); the Tianjin airport passenger flow; and the Beijing Capital airport's airspace restriction factor, namely, airport aircraft sorties. The results show that the growth of Tianjin city's GDP, primary industry and disposable income per capita was accompanied by a long-term decline in the passenger flow at the Tianjin airport. In addition, increased aircraft sorties of Tianjin airport, as well as the growth of primary, secondary and tertiary industries in Tianjin city, led to a short-term decline in passenger flow at the Tianjin airport. In general, there is variability in the long- and short-term impacts of urban economic structure on airport passenger flow, and this variability applies to other airports. The increased aircraft sorties at the Beijing Capital airport had a short-term positive impact on passenger flow at the Tianjin airport but resulted in a long-term decline of the latter's aircraft sorties. This phenomenon indicates that there is interaction between airports and that this influence varies depending on the competition and cooperation mechanisms between airports. The findings of this study are considered instrumental in guiding the competitive and cooperative strategies of nearby airports and predicting the coupled trends of the airport and urban development. [ABSTRACT FROM AUTHOR]

In actuarial statistics, distributions with heavy tails are of great interest to actuaries, as they represent a better description of risk exposure through a type of indicator with a certain probability. These risk indicators are used to determine companies' exposure to a particular risk. In this paper, we present a distribution with heavy right tail, studying its properties and the behaviour of the tail. We estimate the parameters using the maximum likelihood method and evaluate the performance of these estimators using Monte Carlo. We analyse one set of simulated data and another set of real data, showing that the distribution studied can be used to model income data. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to evaluate the health and economic impact of the varicella vaccination program on varicella disease in the United States (US), 1996-2020.Methods: Analysis was conducted using the Centers for Disease Control and Prevention or published annual population-based varicella incidence, and varicella-associated hospitalization, outpatient visit, and mortality rates in the US population aged 0-49 years during 1996-2020 (range, 199.5-214.2 million persons) compared to before vaccination (1990-1994). Disease costs were estimated using the societal perspective. Vaccination program costs included costs of vaccine, administration, postvaccination adverse events, and travel and work time lost to obtain vaccination. All costs were adjusted to 2020 US dollars using a 3% annual discount rate. The main outcome measures were the number of varicella-associated cases, hospitalizations, hospitalization days, and premature deaths prevented; life-years saved; and net societal savings from the US varicella vaccination program.Results: Among US persons aged 0-49 years, during 1996-2020, it is estimated that more than 91 million varicella cases, 238 000 hospitalizations, 1.1 million hospitalization days, and almost 2000 deaths were prevented and 118 000 life-years were saved by the varicella vaccination program, at net societal savings of $23.4 billion.Conclusions: Varicella vaccination has resulted in substantial disease prevention and societal savings for the US over 25 years of program implementation. [ABSTRACT FROM AUTHOR]

Renewable energy forecasting is a key for efficient resource use in terms of power generation and safe grid control. In this study, we investigated a short-term statistical forecasting model with 1 to 3 h horizons using photovoltaic operation data from 215 power plants throughout South Korea. A vector autoregression (VAR) model-based regional photovoltaic power forecasting system is proposed for seven clusters of power plants in South Korea. This method showed better predictability than the autoregressive integrated moving average (ARIMA) model. The normalized root-mean-square errors of hourly photovoltaic generation predictions obtained from VAR (ARIMA) were 8.5–10.9% (9.8–13.0%) and 18.5–22.8% (21.3–26.3%) for 1 h and 3 h horizon, respectively, at 215 power plants. The coefficient of determination, R2 was higher for VAR, at 4–5%, than ARIMA. The VAR model had greater accuracy than ARIMA. This will be useful for economical and efficient grid management. [ABSTRACT FROM AUTHOR]

The problem of reliability minimization is considered. One of the available approaches to solving this problem, namely bPOE, is analyzed. The advantages and disadvantages of this approach are determined. It is noted that the results of minimizing the probability of failures in the classical setting and minimizing the bPOE may differ. A new method of reliability optimization in the classical formulation of the problem is proposed. A comparative analysis of the results of reliability minimization using the bPOE and the results obtained by the proposed method is carried out. [ABSTRACT FROM AUTHOR]

This paper aimed to analyze the relationships between the variables price, consumption and supply of electricity in rural Brazil and the different public pricing policies applied to the sector. To this end, an autoregressive time series model (VAR) and Granger causality are used, with data from the rural electricity sector, from 1996 to 2020, distributed quarterly. Among the contributions, it is observed that the policy of universalization of access to electrification can be considered a success regarding the number of consumer units reached, however, there is a need for a review regarding the pricing policies applied, since there is a bidirectional relationship, in which Production causes Consumption and Consumption causes Production, but Price does not cause Production, but causes Consumption. This is in line with the results of the VAR model which show that an increase in the price of energy in the Rural Zone would reduce its consumption, negatively impacting productivity, since energy is an important input for production and income in the agricultural sector. [ABSTRACT FROM AUTHOR]

A new non‐parametric estimator of the cross‐spectrum of a bivariate stationary time series is proposed, which is non‐quadratic in the observations. This estimator is an extension of the Capon‐estimator of the spectrum of a univariate time series. The proposed estimator is designed so as to cope with the leakage effect induced by strong peaks of the marginal spectra by utilizing adaptive windowing. We study the asymptotic bias and covariance structure of the proposed estimator and prove a central limit theorem for its distribution. We also obtain a result of independent importance for the consistency rate of the cross‐covariance matrix of the two series. The performance of the estimator in comparison to more traditional ones is demonstrated in a simulation study under a model exhibiting extreme characteristics, such as strong peaks in its marginal spectra. Finally, the estimator is used to judge the fit of a VAR(p) model in a real data example. [ABSTRACT FROM AUTHOR]

Although NFTs (non-fungible tokens) and cryptocurrencies are active on the same market, their prices are not so closely related over time. The objective of this paper is to identify the relationship between the two types of assets (NFTs and the cryptocurrencies Ethereum, Crypto Coin, and Bitcoin), using data for the period between September 2020 until February 2022. The conclusions of the study are useful for cryptocurrency and NFT issuers, but also for investors on the financial market who are reconfiguring their portfolios with increasing frequency, and use these new assets for speculative or hedging purposes based on blockchain technology. The results highlighted relationships between NFTs and Ethereum, between Ethereum and Crypto Coin, and between Bitcoin and Ethereum, Ethereum being a bridge between all four. Therefore, NFTs present a relationship with Ethereum, the NFTs price had a causal effect on the price of Ethereum. [ABSTRACT FROM AUTHOR]