In this study, impact of inflation （WPI--Wholesale Price Index）, exchange rate, and interest rate on the production of red meat in Turkey was examined using the vector autoregressive （VAR） model. The model consist...In this study, impact of inflation （WPI--Wholesale Price Index）, exchange rate, and interest rate on the production of red meat in Turkey was examined using the vector autoregressive （VAR） model. The model consisting of variables of dollar exchange rate, inflation rate, interest rate, beef, buffalo meat, mutton, and goat meat production amounts has been estimated for the period from 1981 to 2014. It has been detected that there is a tie among the dollar exchange rate, inflation rate, interest rate, and the amount of red meat production in Turkey. In order to determine the direction of this relation, Granger causality test was conducted. A one-way causal relation has been observed between： the goat meat production and dollar exchange rate; the buffalo meat production and the mutton production; and the beef production and the mutton production. To interpret VAR model, the impulse response function and variance decomposition analysis was used. As a result of variance decomposition, it has been detected that explanatory power of changes in the variance of dollar exchange rate, inflation rate, and interest rate in goat meat production amount is more than explanatory power of changes in the variances of mutton, beef, and buffalo meat variables.展开更多
In this paper, vector autoregressive （VAR） models have been recognized for the selected indicators of Dhaka stock exchange （DSE）. Bangladesh uses the micro economic variables, such as stock trade, invested stock c...In this paper, vector autoregressive （VAR） models have been recognized for the selected indicators of Dhaka stock exchange （DSE）. Bangladesh uses the micro economic variables, such as stock trade, invested stock capital, stock volume, current market value, and DSE general indexes which have the direct impact on DSE prices. The data were collected for the period from June 2004 to July 2013 as the basis on daily scale. But to get the maximum explorative information and reduction of volatility, the data have been transformed to the monthly scale. The outliers and extreme values of the study variables are detected through box and whisker plot. To detect the unit root property of the study variables, various unit root tests have been applied. The forecast performance of the different VAR models is compared to have the minimum residual. Moreover, the dynamics of this financial market is analyzed through Granger causality and impulse response analysis.展开更多
A forecasting method of oil well production based on multivariate time series(MTS)and vector autoregressive(VAR)machine learning model for waterflooding reservoir is proposed,and an example application is carried out....A forecasting method of oil well production based on multivariate time series(MTS)and vector autoregressive(VAR)machine learning model for waterflooding reservoir is proposed,and an example application is carried out.This method first uses MTS analysis to optimize injection and production data on the basis of well pattern analysis.The oil production of different production wells and water injection of injection wells in the well group are regarded as mutually related time series.Then a VAR model is established to mine the linear relationship from MTS data and forecast the oil well production by model fitting.The analysis of history production data of waterflooding reservoirs shows that,compared with history matching results of numerical reservoir simulation,the production forecasting results from the machine learning model are more accurate,and uncertainty analysis can improve the safety of forecasting results.Furthermore,impulse response analysis can evaluate the oil production contribution of the injection well,which can provide theoretical guidance for adjustment of waterflooding development plan.展开更多
The paper uses a global vector autoregressive model to examine provincial output spillover effects in China. We find that there are effective output spillovers from Guangdong, Liaoning and Zhejiang to other provinces ...The paper uses a global vector autoregressive model to examine provincial output spillover effects in China. We find that there are effective output spillovers from Guangdong, Liaoning and Zhejiang to other provinces in China, but trivial effects from Shanghai, Shandong, Sichuan and Xinfiang, and negative effects from Beijing. Foreign direct investment （FDI） in Guangdong and Liaoning is the main channel for creating provincial output spillovers, compared with domestic investment and exports. However, FDl spillovers tend to decrease, with spillovers from exports and domestic investment rising over time, so that the spillover effects in Guangdong and Liaoning are non-persistent and highly volatile. Other channels of output spillover, such as domestic investment, should be enhanced. Impacts of shock from government expenditure on GDP vary significantly across time and provinces; inland and western provinces are most negatively affected. The heterogeneous spillover structure shows that regional policies might achieve better results than nationwide policies in reducing regional disparity.展开更多
Detection and clarification of cause-effect relationships among variables is an important problem in time series analysis. Traditional causality inference methods have a salient limitation that the model must be linea...Detection and clarification of cause-effect relationships among variables is an important problem in time series analysis. Traditional causality inference methods have a salient limitation that the model must be linear and with Gaussian noise. Although additive model regression can effectively infer the nonlinear causal relationships of additive nonlinear time series, it suffers from the limitation that contemporaneous causal relationships of variables must be linear and not always valid to test conditional independence relations. This paper provides a nonparametric method that employs both mutual information and conditional mutual information to identify causal structure of a class of nonlinear time series models, which extends the additive nonlinear times series to nonlinear structural vector autoregressive models. An algorithm is developed to learn the contemporaneous and the lagged causal relationships of variables. Simulations demonstrate the effectiveness of the nroosed method.展开更多
This note considers parameter estimation for panel vector autoregressive models with intercorrelation. Conditional least squares estimators are derived and the asymptotic normality is established. A simulation is carr...This note considers parameter estimation for panel vector autoregressive models with intercorrelation. Conditional least squares estimators are derived and the asymptotic normality is established. A simulation is carried out for illustration.展开更多
Forecasting solar irradiance is a critical task in the renewable energy sector, as it provides essential information regarding the potential energy production from solar panels. This study aims to utilize the Vector A...Forecasting solar irradiance is a critical task in the renewable energy sector, as it provides essential information regarding the potential energy production from solar panels. This study aims to utilize the Vector Autoregression (VAR) model to forecast solar irradiance levels and weather characteristics in the San Francisco Bay Area. The results demonstrate a correlation between predicted and actual solar irradiance, indicating the effectiveness of the VAR model for this task. However, the model may not be sufficient for this region due to the requirement of additional weather features to reduce disparities between predictions and actual observations. Additionally, the current lag order in the model is relatively low, limiting its ability to capture all relevant information from past observations. As a result, the model’s forecasting capability is limited to short-term horizons, with a maximum horizon of four hours.展开更多
This study investigates the determinants that drive the volatility of the credit default swaps(CDS)of BRICIT(Brazil,Russia,India,China,Indonesia,and Turkey)nations as a proxy measure for sovereign risk.On the existenc...This study investigates the determinants that drive the volatility of the credit default swaps(CDS)of BRICIT(Brazil,Russia,India,China,Indonesia,and Turkey)nations as a proxy measure for sovereign risk.On the existence of cointegration,an unrestricted error correction model integrated with the autoregressive distributed lag(ARDL)model is applied to measure the short-run and long-run dynamics empirically.The study utilizes the Bayesian global vector autoregression methodology for cross-border spillover estimation.The study also suggests a strategy for policymakers for quadrant categorization to mitigate risk arising from cross-border spillover.The result of ARDL indicates that the global macroeconomic variables affect the BRICIT CDS more than domestic macroeconomic determinants,with Indian CDS being the most sensitive to Fed tapering.Notably,China’s CDS is the most sensitive to shocks,with the CDS volatility primarily driven by China’s geopolitical risk.Russian CDS is more sensitive to real effective exchange rates due to severe ruble depreciation than crude oil,despite Russia being a major oil exporter.The quadrant categorization indicates that the Indonesian stock market index is most interconnected with BRICIT CDS,while the Turkish long-term interest rates send the highest intensity spillover across BRICIT nations.展开更多
It is of real and direct significance for China to cope with oil price fluctuations and ensure oil security. This paper aims to quantitatively analyze the specific contribution ratios of the complex factors influencin...It is of real and direct significance for China to cope with oil price fluctuations and ensure oil security. This paper aims to quantitatively analyze the specific contribution ratios of the complex factors influencing international crude oil prices and to establish crude oil price models to forecast long-term international crude oil prices. Six explanatory influential variables, namely Dow Jones Indexes, the Organization for Economic Cooperation and Development oil stocks, US rotary rig count, US dollar index, total open interest, which is the total number of outstanding contracts that are held by market participants at the end of each day, and geopolitical instability are specified, and the samples, from January 1990 to August 2017, are divided into six sub-periods. Moreover, the co-integration relationship among variables shows that the contribution ratios of all the variables influencing Brent crude oil prices are in accordance with the corresponding qualitative analysis. Furthermore, from September 2017 to December 2022 outside of the sample, the Vector Autoregressive forecasts show that annually averaged Brent crude oil prices for 2017-2022 would be $53.0, $61.3, $74.4, $90.0, $105.5, and $120.7 per barrel, respectively. The Vector Error Correction forecasts show that annual average Brent crude oil prices for 2017-2022 would be $53.0, $56.5, $58.5, $60.7, $63.0 and $65.4 per barrel, respectively.展开更多
We introduce a novel approach to multifractal data in order to achieve transcended modeling and forecasting performances by extracting time series out of local Hurst exponent calculations at a specified scale.First,th...We introduce a novel approach to multifractal data in order to achieve transcended modeling and forecasting performances by extracting time series out of local Hurst exponent calculations at a specified scale.First,the long range and co-movement dependencies of the time series are scrutinized on time-frequency space using multiple wavelet coherence analysis.Then,the multifractal behaviors of the series are verified by multifractal de-trended fluctuation analysis and its local Hurst exponents are calculated.Additionally,root mean squares of residuals at the specified scale are procured from an intermediate step during local Hurst exponent calculations.These internally calculated series have been used to estimate the process with vector autoregressive fractionally integrated moving average(VARFIMA)model and forecasted accordingly.In our study,the daily prices of gold,silver and platinum are used for assessment.The results have shown that all metals do behave in phase movement on long term periods and possess multifractal features.Furthermore,the intermediate time series obtained during local Hurst exponent calculations still appertain the co-movement as well as multifractal characteristics of the raw data and may be successfully re-scaled,modeled and forecasted by using VARFIMA model.Conclusively,VARFIMA model have notably surpassed its univariate counterpart(ARFIMA)in all efficacious trials while re-emphasizing the importance of comovement procurement in modeling.Our study’s novelty lies in using a multifractal de-trended fluctuation analysis,along with multiple wavelet coherence analysis,for forecasting purposes to an extent not seen before.The results will be of particular significance to finance researchers and practitioners.展开更多
Based on the monitoring and discovery service 4 （MDS4） model, a monitoring model for a data grid which supports reliable storage and intrusion tolerance is designed. The load characteristics and indicators of comput...Based on the monitoring and discovery service 4 （MDS4） model, a monitoring model for a data grid which supports reliable storage and intrusion tolerance is designed. The load characteristics and indicators of computing resources in the monitoring model are analyzed. Then, a time-series autoregressive prediction model is devised. And an autoregressive support vector regression（ ARSVR） monitoring method is put forward to predict the node load of the data grid. Finally, a model for historical observations sequences is set up using the autoregressive （AR） model and the model order is determined. The support vector regression（SVR） model is trained using historical data and the regression function is obtained. Simulation results show that the ARSVR method can effectively predict the node load.展开更多
Data from the World Federation of Exchanges show that Brazil＇s Sao Paulo stock exchange is one of the largest worldwide in terms of market value. Thus, the objective of this study is to obtain univariate and bivariat...Data from the World Federation of Exchanges show that Brazil＇s Sao Paulo stock exchange is one of the largest worldwide in terms of market value. Thus, the objective of this study is to obtain univariate and bivariate forecasting models based on intraday data from the futures and spot markets of the BOVESPA index. The interest is to verify if there exist arbitrage opportunities in Brazilian financial market. To this end, three econometric forecasting models were built： ARFIMA, vector autoregressive （VAR）, and vector error correction （VEC）. Furthermore, it presents the results of a Granger causality test for the aforementioned series. This type of study shows that it is important to identify arbitrage opportunities in financial markets and, in particular, in the application of these models on data of this nature. In terms of the forecasts made with these models, VEC showed better results. The causality test shows that futures BOVESPA index Granger causes spot BOVESPA index. This result may indicate arbitrage opportunities in Brazil.展开更多
Vegetation is an important ecosystem on earth. It influences the earth system in many ways. Any influences on this fragile variable should be investigated, especially in a changing climate. Humans can have a positive ...Vegetation is an important ecosystem on earth. It influences the earth system in many ways. Any influences on this fragile variable should be investigated, especially in a changing climate. Humans can have a positive or a negative influence on plants. This paper investigates the possible impact of tourism development and economic growth on vegetation health using cointegration and causality for Aruba. The proposed framework contributes to a better understanding on the use of remote sensing of vegetation response to tourism development and economic growth. Thereby, provide opportunities for improving the overall strategy for achieving sustainable development on a small island state. The calculations showed that there were relationships between the tourism demand and economic growth on the vegetation health on Aruba for the western part of the island. On the other hand, for the central part of the island, no relationships were found.展开更多
The Brazilian aquaculture industry has shown a strong production growth in recent years. Regarding consumption, analysts believe in a potential expansion of domestic demand for fish in Brazil due to current low per ca...The Brazilian aquaculture industry has shown a strong production growth in recent years. Regarding consumption, analysts believe in a potential expansion of domestic demand for fish in Brazil due to current low per capita consumption and a growing deficit of trade balance. This paper intends to investigate whether there is domestic demand to absorb the increased supply provided by the growth of aquaculture production in Brazil. The investigation consisted in analyze the relationship between the domestic consumption, the population income and the fish price, and analyzed the behavior of this consumption due to the increase of production, using annual time series from 1995 to 2009. Econometric methods of time series showed that could not be said that there will be balance in the fish market.展开更多
Stock exchange market responses to macroeconomic fluctuations show deviations between countries in terms of direction, magnitude and duration due to the idiosyncratic characteristics of the countries. The paper empiri...Stock exchange market responses to macroeconomic fluctuations show deviations between countries in terms of direction, magnitude and duration due to the idiosyncratic characteristics of the countries. The paper empirically searches for the identification of these variations for CEECs, namely Czech Republic, Hungary, Poland, Slovak Republic and also Turkey for the period of December, 1999 to December, 2009. The empirical analyses demonstrate that for each CEEC, stock exchange market responds positively to industrial production and to appreciation of local currency. Czech Republic and Hungary display negative and the rest display positive response to M1, whereas the response of stock market to CB policy rate shows mixed results for each country. Besides, foreign exchange market returns are found to be the variable with the highest significance in explaining the stock exchange market returns. These findings point out to arbitrage opportunities for investors and give insight to Monetary Policy Authorities about the Monetary Transmission Mechanisms of the countries.展开更多
Finance is one important factor to promote economic development. Meanwhile, it also has a dubious effect on income inequality in accordance with the prior literatures. In order to promote economic development, most o...Finance is one important factor to promote economic development. Meanwhile, it also has a dubious effect on income inequality in accordance with the prior literatures. In order to promote economic development, most of China’s governments provide many policies to boost financial development. However, these policies should also be evaluated with its impact on the income inequality. As one important province in China, Henan also wants to have a rapid economic development with policies on financial development. Therefore, this paper uses the vector autoregressive model to detect the impact of financial development on income inequality between the urban and the rural, and the results suggest one positive impulse on financial development would cause income inequality to be increased immediately, but to be decreased after the fourth period. Thus, Henan’s policies on financial development would achieve the goal to promote economic development without the detrimental effect on income inequality.展开更多
Fluctuations of the world oil prices affect economic performance. Outside the impact on the sector of energy production, the rising oil price has consequences on inflationary pressures and a deteriorating fiscal posit...Fluctuations of the world oil prices affect economic performance. Outside the impact on the sector of energy production, the rising oil price has consequences on inflationary pressures and a deteriorating fiscal position of Burkina Faso. In this context, studying the impact of rising oil prices on the economy, especially the cost of living of its population has a great interest because although many studies have attempted to link 〈〈oil prices〉~ and 〈〈cost of living~, very few have focused on the specific case of Burkina Faso. This allows us to make our contribution to this construction literature. This contribution will consist to highlight the relation between changes in oil prices and the cost of living in Burkina Faso. Also to be reached, we will find the best indicator to reflect the cost of living in Burkina Faso, identify the suitable econometric model for estimating the correlation and verify the existence of the relation between oil prices and the cost of living. For a better approach to this study, we used a VAR （Vector Auto-Regressive） model. Also, we will use documentary research that will make an assessment on the existing in terms of theoretical debates around the theme descriptive statistics that will help to introduce and describe the variables used in the study, and econometric analysis will analyze and estimate the parameters of our objective function using Eviews.展开更多
Investigating how COVID-19 has influenced Liquefied Natural Gas(LNG)is significant for benefits evaluation for shipping companies and safety management for sustainable LNG shipping in case of accidents.This paper prop...Investigating how COVID-19 has influenced Liquefied Natural Gas(LNG)is significant for benefits evaluation for shipping companies and safety management for sustainable LNG shipping in case of accidents.This paper proposes a quantitative method to model the impact of COVID-19 on global LNG shipping efficiency based on the spatiotemporal characteristics of behavior mining for LNG ships.The time cost for LNG carriers serving inside LNG terminals is calculated based on the status of LNG carriers specifically based on arrival and departure times.Then,the time series analysis method is employed to extract the statistical characteristics of the COVID-19 severity index and time cost for LNG carriers inside LNG terminals.Finally,the impact of COVID-19 on global LNG shipping is explored through the Vector Autoregressive Model(VAR)combined with the sliding window.The results demonstrate that the COVID-19 pandemic has a certain influence on the service time for LNG carriers with time lags worldwide.The impact is spatial heterogeneity on a large scale or small scale across global,countries,and trading terminals.It can be used for decision-making in energy safety and LNG intelligent shipping management under unexpected global public health events in the future.The results provide support for intelligent decision-making for safety management under unexpected public health events,such as reducing the seafarer’s explosion to risk events and taking efficient actions to ensure the shipping flow to avoid the energy supply shortage.展开更多
文摘In this study, impact of inflation （WPI--Wholesale Price Index）, exchange rate, and interest rate on the production of red meat in Turkey was examined using the vector autoregressive （VAR） model. The model consisting of variables of dollar exchange rate, inflation rate, interest rate, beef, buffalo meat, mutton, and goat meat production amounts has been estimated for the period from 1981 to 2014. It has been detected that there is a tie among the dollar exchange rate, inflation rate, interest rate, and the amount of red meat production in Turkey. In order to determine the direction of this relation, Granger causality test was conducted. A one-way causal relation has been observed between： the goat meat production and dollar exchange rate; the buffalo meat production and the mutton production; and the beef production and the mutton production. To interpret VAR model, the impulse response function and variance decomposition analysis was used. As a result of variance decomposition, it has been detected that explanatory power of changes in the variance of dollar exchange rate, inflation rate, and interest rate in goat meat production amount is more than explanatory power of changes in the variances of mutton, beef, and buffalo meat variables.
文摘In this paper, vector autoregressive （VAR） models have been recognized for the selected indicators of Dhaka stock exchange （DSE）. Bangladesh uses the micro economic variables, such as stock trade, invested stock capital, stock volume, current market value, and DSE general indexes which have the direct impact on DSE prices. The data were collected for the period from June 2004 to July 2013 as the basis on daily scale. But to get the maximum explorative information and reduction of volatility, the data have been transformed to the monthly scale. The outliers and extreme values of the study variables are detected through box and whisker plot. To detect the unit root property of the study variables, various unit root tests have been applied. The forecast performance of the different VAR models is compared to have the minimum residual. Moreover, the dynamics of this financial market is analyzed through Granger causality and impulse response analysis.
基金Huo Yingdong Education Foundation Young Teachers Fund for Higher Education Institutions(171043)Sichuan Outstanding Young Science and Technology Talent Project(2019JDJQ0036)。
文摘A forecasting method of oil well production based on multivariate time series(MTS)and vector autoregressive(VAR)machine learning model for waterflooding reservoir is proposed,and an example application is carried out.This method first uses MTS analysis to optimize injection and production data on the basis of well pattern analysis.The oil production of different production wells and water injection of injection wells in the well group are regarded as mutually related time series.Then a VAR model is established to mine the linear relationship from MTS data and forecast the oil well production by model fitting.The analysis of history production data of waterflooding reservoirs shows that,compared with history matching results of numerical reservoir simulation,the production forecasting results from the machine learning model are more accurate,and uncertainty analysis can improve the safety of forecasting results.Furthermore,impulse response analysis can evaluate the oil production contribution of the injection well,which can provide theoretical guidance for adjustment of waterflooding development plan.
文摘The paper uses a global vector autoregressive model to examine provincial output spillover effects in China. We find that there are effective output spillovers from Guangdong, Liaoning and Zhejiang to other provinces in China, but trivial effects from Shanghai, Shandong, Sichuan and Xinfiang, and negative effects from Beijing. Foreign direct investment （FDI） in Guangdong and Liaoning is the main channel for creating provincial output spillovers, compared with domestic investment and exports. However, FDl spillovers tend to decrease, with spillovers from exports and domestic investment rising over time, so that the spillover effects in Guangdong and Liaoning are non-persistent and highly volatile. Other channels of output spillover, such as domestic investment, should be enhanced. Impacts of shock from government expenditure on GDP vary significantly across time and provinces; inland and western provinces are most negatively affected. The heterogeneous spillover structure shows that regional policies might achieve better results than nationwide policies in reducing regional disparity.
基金supported by the National Natural Science Foundation of China under Grant Nos.60972150 and 10926197
文摘Detection and clarification of cause-effect relationships among variables is an important problem in time series analysis. Traditional causality inference methods have a salient limitation that the model must be linear and with Gaussian noise. Although additive model regression can effectively infer the nonlinear causal relationships of additive nonlinear time series, it suffers from the limitation that contemporaneous causal relationships of variables must be linear and not always valid to test conditional independence relations. This paper provides a nonparametric method that employs both mutual information and conditional mutual information to identify causal structure of a class of nonlinear time series models, which extends the additive nonlinear times series to nonlinear structural vector autoregressive models. An algorithm is developed to learn the contemporaneous and the lagged causal relationships of variables. Simulations demonstrate the effectiveness of the nroosed method.
基金supported by a grant from the Research Grants Council of Hong KongAlso the work of the first author was supported in part by project 07JJD790154Youth Talent Foundation of Zhejiang Gongshang University.
文摘This note considers parameter estimation for panel vector autoregressive models with intercorrelation. Conditional least squares estimators are derived and the asymptotic normality is established. A simulation is carried out for illustration.
文摘Forecasting solar irradiance is a critical task in the renewable energy sector, as it provides essential information regarding the potential energy production from solar panels. This study aims to utilize the Vector Autoregression (VAR) model to forecast solar irradiance levels and weather characteristics in the San Francisco Bay Area. The results demonstrate a correlation between predicted and actual solar irradiance, indicating the effectiveness of the VAR model for this task. However, the model may not be sufficient for this region due to the requirement of additional weather features to reduce disparities between predictions and actual observations. Additionally, the current lag order in the model is relatively low, limiting its ability to capture all relevant information from past observations. As a result, the model’s forecasting capability is limited to short-term horizons, with a maximum horizon of four hours.
文摘This study investigates the determinants that drive the volatility of the credit default swaps(CDS)of BRICIT(Brazil,Russia,India,China,Indonesia,and Turkey)nations as a proxy measure for sovereign risk.On the existence of cointegration,an unrestricted error correction model integrated with the autoregressive distributed lag(ARDL)model is applied to measure the short-run and long-run dynamics empirically.The study utilizes the Bayesian global vector autoregression methodology for cross-border spillover estimation.The study also suggests a strategy for policymakers for quadrant categorization to mitigate risk arising from cross-border spillover.The result of ARDL indicates that the global macroeconomic variables affect the BRICIT CDS more than domestic macroeconomic determinants,with Indian CDS being the most sensitive to Fed tapering.Notably,China’s CDS is the most sensitive to shocks,with the CDS volatility primarily driven by China’s geopolitical risk.Russian CDS is more sensitive to real effective exchange rates due to severe ruble depreciation than crude oil,despite Russia being a major oil exporter.The quadrant categorization indicates that the Indonesian stock market index is most interconnected with BRICIT CDS,while the Turkish long-term interest rates send the highest intensity spillover across BRICIT nations.
基金supported by the National Science Foundation of China(NSFC No.41271551/71201157)the National Key Research and Development Program(2016YFA0602700)
文摘It is of real and direct significance for China to cope with oil price fluctuations and ensure oil security. This paper aims to quantitatively analyze the specific contribution ratios of the complex factors influencing international crude oil prices and to establish crude oil price models to forecast long-term international crude oil prices. Six explanatory influential variables, namely Dow Jones Indexes, the Organization for Economic Cooperation and Development oil stocks, US rotary rig count, US dollar index, total open interest, which is the total number of outstanding contracts that are held by market participants at the end of each day, and geopolitical instability are specified, and the samples, from January 1990 to August 2017, are divided into six sub-periods. Moreover, the co-integration relationship among variables shows that the contribution ratios of all the variables influencing Brent crude oil prices are in accordance with the corresponding qualitative analysis. Furthermore, from September 2017 to December 2022 outside of the sample, the Vector Autoregressive forecasts show that annually averaged Brent crude oil prices for 2017-2022 would be $53.0, $61.3, $74.4, $90.0, $105.5, and $120.7 per barrel, respectively. The Vector Error Correction forecasts show that annual average Brent crude oil prices for 2017-2022 would be $53.0, $56.5, $58.5, $60.7, $63.0 and $65.4 per barrel, respectively.
文摘We introduce a novel approach to multifractal data in order to achieve transcended modeling and forecasting performances by extracting time series out of local Hurst exponent calculations at a specified scale.First,the long range and co-movement dependencies of the time series are scrutinized on time-frequency space using multiple wavelet coherence analysis.Then,the multifractal behaviors of the series are verified by multifractal de-trended fluctuation analysis and its local Hurst exponents are calculated.Additionally,root mean squares of residuals at the specified scale are procured from an intermediate step during local Hurst exponent calculations.These internally calculated series have been used to estimate the process with vector autoregressive fractionally integrated moving average(VARFIMA)model and forecasted accordingly.In our study,the daily prices of gold,silver and platinum are used for assessment.The results have shown that all metals do behave in phase movement on long term periods and possess multifractal features.Furthermore,the intermediate time series obtained during local Hurst exponent calculations still appertain the co-movement as well as multifractal characteristics of the raw data and may be successfully re-scaled,modeled and forecasted by using VARFIMA model.Conclusively,VARFIMA model have notably surpassed its univariate counterpart(ARFIMA)in all efficacious trials while re-emphasizing the importance of comovement procurement in modeling.Our study’s novelty lies in using a multifractal de-trended fluctuation analysis,along with multiple wavelet coherence analysis,for forecasting purposes to an extent not seen before.The results will be of particular significance to finance researchers and practitioners.
基金The National High Technology Research and Development Program of China (863 Program) (No2007AA01Z404)
文摘Based on the monitoring and discovery service 4 （MDS4） model, a monitoring model for a data grid which supports reliable storage and intrusion tolerance is designed. The load characteristics and indicators of computing resources in the monitoring model are analyzed. Then, a time-series autoregressive prediction model is devised. And an autoregressive support vector regression（ ARSVR） monitoring method is put forward to predict the node load of the data grid. Finally, a model for historical observations sequences is set up using the autoregressive （AR） model and the model order is determined. The support vector regression（SVR） model is trained using historical data and the regression function is obtained. Simulation results show that the ARSVR method can effectively predict the node load.
文摘Data from the World Federation of Exchanges show that Brazil＇s Sao Paulo stock exchange is one of the largest worldwide in terms of market value. Thus, the objective of this study is to obtain univariate and bivariate forecasting models based on intraday data from the futures and spot markets of the BOVESPA index. The interest is to verify if there exist arbitrage opportunities in Brazilian financial market. To this end, three econometric forecasting models were built： ARFIMA, vector autoregressive （VAR）, and vector error correction （VEC）. Furthermore, it presents the results of a Granger causality test for the aforementioned series. This type of study shows that it is important to identify arbitrage opportunities in financial markets and, in particular, in the application of these models on data of this nature. In terms of the forecasts made with these models, VEC showed better results. The causality test shows that futures BOVESPA index Granger causes spot BOVESPA index. This result may indicate arbitrage opportunities in Brazil.
文摘Vegetation is an important ecosystem on earth. It influences the earth system in many ways. Any influences on this fragile variable should be investigated, especially in a changing climate. Humans can have a positive or a negative influence on plants. This paper investigates the possible impact of tourism development and economic growth on vegetation health using cointegration and causality for Aruba. The proposed framework contributes to a better understanding on the use of remote sensing of vegetation response to tourism development and economic growth. Thereby, provide opportunities for improving the overall strategy for achieving sustainable development on a small island state. The calculations showed that there were relationships between the tourism demand and economic growth on the vegetation health on Aruba for the western part of the island. On the other hand, for the central part of the island, no relationships were found.
文摘The Brazilian aquaculture industry has shown a strong production growth in recent years. Regarding consumption, analysts believe in a potential expansion of domestic demand for fish in Brazil due to current low per capita consumption and a growing deficit of trade balance. This paper intends to investigate whether there is domestic demand to absorb the increased supply provided by the growth of aquaculture production in Brazil. The investigation consisted in analyze the relationship between the domestic consumption, the population income and the fish price, and analyzed the behavior of this consumption due to the increase of production, using annual time series from 1995 to 2009. Econometric methods of time series showed that could not be said that there will be balance in the fish market.
文摘Stock exchange market responses to macroeconomic fluctuations show deviations between countries in terms of direction, magnitude and duration due to the idiosyncratic characteristics of the countries. The paper empirically searches for the identification of these variations for CEECs, namely Czech Republic, Hungary, Poland, Slovak Republic and also Turkey for the period of December, 1999 to December, 2009. The empirical analyses demonstrate that for each CEEC, stock exchange market responds positively to industrial production and to appreciation of local currency. Czech Republic and Hungary display negative and the rest display positive response to M1, whereas the response of stock market to CB policy rate shows mixed results for each country. Besides, foreign exchange market returns are found to be the variable with the highest significance in explaining the stock exchange market returns. These findings point out to arbitrage opportunities for investors and give insight to Monetary Policy Authorities about the Monetary Transmission Mechanisms of the countries.
文摘Finance is one important factor to promote economic development. Meanwhile, it also has a dubious effect on income inequality in accordance with the prior literatures. In order to promote economic development, most of China’s governments provide many policies to boost financial development. However, these policies should also be evaluated with its impact on the income inequality. As one important province in China, Henan also wants to have a rapid economic development with policies on financial development. Therefore, this paper uses the vector autoregressive model to detect the impact of financial development on income inequality between the urban and the rural, and the results suggest one positive impulse on financial development would cause income inequality to be increased immediately, but to be decreased after the fourth period. Thus, Henan’s policies on financial development would achieve the goal to promote economic development without the detrimental effect on income inequality.
文摘Fluctuations of the world oil prices affect economic performance. Outside the impact on the sector of energy production, the rising oil price has consequences on inflationary pressures and a deteriorating fiscal position of Burkina Faso. In this context, studying the impact of rising oil prices on the economy, especially the cost of living of its population has a great interest because although many studies have attempted to link 〈〈oil prices〉~ and 〈〈cost of living~, very few have focused on the specific case of Burkina Faso. This allows us to make our contribution to this construction literature. This contribution will consist to highlight the relation between changes in oil prices and the cost of living in Burkina Faso. Also to be reached, we will find the best indicator to reflect the cost of living in Burkina Faso, identify the suitable econometric model for estimating the correlation and verify the existence of the relation between oil prices and the cost of living. For a better approach to this study, we used a VAR （Vector Auto-Regressive） model. Also, we will use documentary research that will make an assessment on the existing in terms of theoretical debates around the theme descriptive statistics that will help to introduce and describe the variables used in the study, and econometric analysis will analyze and estimate the parameters of our objective function using Eviews.
基金National Natural Science Foundation of China(No.42101429 and No.42371415)Young Elite Scientists Sponsorship Program by China Association for Science and Technology(CAST)(No.YESS20220491)+2 种基金Project of Education Department of Hainan Province(No.Hnjg2024-284)Open Fund of State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University(No.21S04)National Key Research and Development Program of China(No.2022YFC3302703).
文摘Investigating how COVID-19 has influenced Liquefied Natural Gas(LNG)is significant for benefits evaluation for shipping companies and safety management for sustainable LNG shipping in case of accidents.This paper proposes a quantitative method to model the impact of COVID-19 on global LNG shipping efficiency based on the spatiotemporal characteristics of behavior mining for LNG ships.The time cost for LNG carriers serving inside LNG terminals is calculated based on the status of LNG carriers specifically based on arrival and departure times.Then,the time series analysis method is employed to extract the statistical characteristics of the COVID-19 severity index and time cost for LNG carriers inside LNG terminals.Finally,the impact of COVID-19 on global LNG shipping is explored through the Vector Autoregressive Model(VAR)combined with the sliding window.The results demonstrate that the COVID-19 pandemic has a certain influence on the service time for LNG carriers with time lags worldwide.The impact is spatial heterogeneity on a large scale or small scale across global,countries,and trading terminals.It can be used for decision-making in energy safety and LNG intelligent shipping management under unexpected global public health events in the future.The results provide support for intelligent decision-making for safety management under unexpected public health events,such as reducing the seafarer’s explosion to risk events and taking efficient actions to ensure the shipping flow to avoid the energy supply shortage.
