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
China＇s astonishing economic growth implies a necessity to understand its inflation. The present paper employs threshold nonrecursive structural vector autoregression analysis to explore the asymmetric effects of mac...China＇s astonishing economic growth implies a necessity to understand its inflation. The present paper employs threshold nonrecursive structural vector autoregression analysis to explore the asymmetric effects of macro-variables on inflation in low and high inflation regimes. The empirical evidence demonstrates, first, that the reactions of inflation to various shocks are inflation-regime-dependent and asymmetric. Second, monetary policy influences China ＂s high inflation and adjusting the domestic interest rate in China may be an effective way to control inflation in a high inflation regime, but not in a low inflation regime. In a high inflation regime, a high inflation rate may cause the macro-policy authorities to increase the domestic interest rate, in an attempt to stabilize high inflation. Third, contrary to expectations, the world oil price is not a strong cost-push factor in a low inflation regime. Oil price increases may increase inflation in a high inflation regime, but there is no such obvious effect in a low inflation regime. Finally, China ＂s nominal effective exchange rate influences inflation in both low and high inflation regimes. A nominal effeetive exchange rate appreciation might be effective in controlling domestic inflation in both regimes.展开更多
In the parameter tracking of time-varying systems, the ordinary method is weighted least squares with the rectangular window or the exponential window. In this paper we propose a new kind of sliding window called the ...In the parameter tracking of time-varying systems, the ordinary method is weighted least squares with the rectangular window or the exponential window. In this paper we propose a new kind of sliding window called the multiple exponential window, and then use it to fit time-varying Gaussian vector autoregressive models. The asymptotic bias and covariance of the estimator of the parameter for time-invariant models are also derived. Simulation results show that the multiple exponential windows have better parameter tracking effect than rectangular windows and exponential ones.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
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
We analyze the connectedness between major cryptocurrencies and nonfungible tokens(NFTs)for different quantiles employing a time-varying parameter vector autoregression approach.We find that lower and upper quantile s...We analyze the connectedness between major cryptocurrencies and nonfungible tokens(NFTs)for different quantiles employing a time-varying parameter vector autoregression approach.We find that lower and upper quantile spillovers are higher than those at the median,meaning that connectedness augments at extremes.For normal,bearish,and bullish markets,Bitcoin Cash,Bitcoin,Ethereum,and Litecoin consistently remain net transmitters,while NFTs receive innovations.However,spillover topology at both extremes becomes simpler—from cryptocurrencies to NFTs.We find no markets useful for mitigating BTC risks,whereas BTC is capable of reducing the risk of other digital assets,which is a valuable insight for market players and investors.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
As the biggest iron and steel producer in the world and one of the highest CO2 emission sectors, China’s iron and steel industry is undergoing a low-carbon transition accompanied by remarkable technological progress ...As the biggest iron and steel producer in the world and one of the highest CO2 emission sectors, China’s iron and steel industry is undergoing a low-carbon transition accompanied by remarkable technological progress and investment adjustment, in response to the macroeconomic climate and policy intervention. Many drivers of the CO2 emissions of the iron and steel industry have been explored, but the relationships between CO2 abatement,investment and technological expenditure, and their connections with the economic growth and governmental policies in China, have not been conjointly and empirically examined. We proposed a concise conceptual model and an econometric model to investigate this crucial question. The results of regression, Granger causality test and impulse response analysis indicated that technological expenditure can significantly reduce CO2 emissions, and that investment expansion showed a negative impact on CO2 emission reduction. It was also argued with empirical evidence that a good economic situation favored CO2 abatement in China’s iron and steel industry, while achieving CO2 emission reduction in this industrial sector did not necessarily threaten economic growth.This shed light on the dispute over balancing emission cutting and economic growth.Regarding the policy aspects, the year 2000 was found to be an important turning point for policy evolution and the development of the iron and steel industry in China. The subsequent command and control policies had a significant, positive effect on CO2 abatement.展开更多
Based on the commodity property and finance property of gold in the international gold futures market,the influence factors of international gold futures price volatility are analyzed from the perspectives of supply a...Based on the commodity property and finance property of gold in the international gold futures market,the influence factors of international gold futures price volatility are analyzed from the perspectives of supply and demand factors,financial factors and speculation factors.The structural vector autoregression(SVAR)model is applied to investigating the direction and strength of the effects of influence factors on the international gold futures prices and the variance decomposition approach(VDA)is used to compare the contributions of these factors.The results show that the supply and demand factors still play a fundamental role in the international gold futures price volatility and the role of“China’s gold demand”is exaggerated.The financial factors and speculation factors have significant impacts on the international gold futures price volatility,which reflects that the financial property of gold becomes increasingly important.Governments and investors should pay close attention to the financial property of gold futures.展开更多
The Natural Forest Protection Program(NFPP)is one of the key ecological forestry programs in China.It not only facilitates the improvement of forest ecological quality in NFPP areas,but also plays a significant role i...The Natural Forest Protection Program(NFPP)is one of the key ecological forestry programs in China.It not only facilitates the improvement of forest ecological quality in NFPP areas,but also plays a significant role in increasing the carbon storage of forest ecosystems.The program covers 17 provinces,autonomous regions,and municipalities with correspondingly diverse forest resources and environments,ecological features,engineering measures and forest management regimes,all of which affect regional carbon storage.In this study,volume of timber harvest,tending area,pest-infested forest,firedamaged forest,reforestation,and average annual precipitation,and temperature were evaluated as factors that influence carbon storage.We developed a vector autoregression model for these seven indicators and we studied the dominant factors of carbon storage in the areas covered by NFPP.Timber harvest was the dominant factorinfluencing carbon storage in the Yellow and Yangtze River basins.Reforestation contributed most to carbon storage in the state-owned forest region in Xinjiang.In state-owned forest regions of Heilongjiang and Jilin Provinces,the dominant factors were forest fires and forest cultivation,respectively.For the enhancement of carbon sequestration capacity,a longer rotation period and a smaller timber harvest are recommended for the Yellow and Yangtze River basins.Trees should be planted in stateowned forests in Xinjiang.Forest fires should be prevented in state-owned forests in Heilongjiang,and greater forest tending efforts should be made in the state-owned forests in Jilin.展开更多
The United States,Russia and China are militarily and economically among the most powerful countries in the post-Cold War period,and the interactions between the three powers heavily influence the international system...The United States,Russia and China are militarily and economically among the most powerful countries in the post-Cold War period,and the interactions between the three powers heavily influence the international system.However,different conclusions about this question are generally made by researchers through qualitative analysis,and it is necessary to objectively and quantitatively investigate their interactions.Monthly-aggregated event data from the Global Data on Events,Location and Tone(GDELT)to measure cooperative and conflictual interactions between the three powers,and the complementary cumulative distribution function(CCDF)and the vector autoregression(VAR)method are utilized to investigate their interactions in two periods:January,1991 to September,2001,and October,2001 to December,2016.The results of frequencies and strengths analysis showed that:the frequencies and strengths of USA-China interactions slightly exceeded those of USA-Russia interactions and became the dominant interactions in the second period.Although that cooperation prevailed in the three dyads in two periods,the conflictual interactions between the USA and Russia tended to be more intense in the second period,mainly related to the strategic contradiction between the USA and Russia,especially in Georgia,Ukraine and Syria.The results of CCDF indicated that similar probabilities in the cooperative behaviors between the three dyads,but the differences in the probabilities of conflictual behaviors in the USA-Russia dyad showed complicated characteristic,and those between Russia and China indicated that Russia had been consistently giving China a hard time in both periods when dealing with conflict.The USA was always an essential factor in affecting the interactions between Russia and China in both periods,but China’s behavior only played a limited role in influencing the interactions between the USA-Russia dyad.Our study provides quantitative insight into the direct cooperative and conflictual interactions between the three dyads since the end of the Cold War and helps to understand their interactions better.展开更多
Background:This paper examines the role of monetary and fiscal factors in interest rate variations in Sri Lanka under its deregulated regime of interest rates.In addition the paper also examines the role of monetary f...Background:This paper examines the role of monetary and fiscal factors in interest rate variations in Sri Lanka under its deregulated regime of interest rates.In addition the paper also examines the role of monetary factors in the variation of interest rates,using a quarterly dataset for the post-global recession period,when the exchange rate is determined by market forces.Results:Empirical analysis uses a dataset of nominal interest rates,money growth,income growth,changes in nominal exchange rate,and budget deficit.From the methodological point of view the paper involves vector autoregression model and Wald tests of Granger causality,followed by impulse response analysis while stationarity and the order of integration of the selected variables are confirmed involving the augmented Dickey-Fuller and the Phillips-Perron unit-root tests.Results:The paper confirms that both monetary and fiscal factors have significant effects on the variations of interest rates.Money growth triggers an increase in interest rates,which supports the Fisher equation view,while income growth has a negative impact.Budget deficit causes a rise in interest rates,but the role of the exchange rate is found to be almost insignificant,probably due to including exchange rate series that cover both the pegged and market-based regimes of exchange rates.The second part of the analysis,using a quarterly dataset for the post-global recession period,further establishes the positive impact of M2 money growth and income growth on interest rates.In this case,exchange rate depreciation causes an increase in interest rates.Conclusions:The significant role of monetary and fiscal factors in interest rate variations implies it would be possible to manage interest rates through a judiciary management of monetary and fiscal policies.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
The purpose of this paper is to investigate the relationships among the variables, and how interest rate, unemployment, stock market, and consumer confidence affect housing market index （HM1） in the U.S.. We constru...The purpose of this paper is to investigate the relationships among the variables, and how interest rate, unemployment, stock market, and consumer confidence affect housing market index （HM1） in the U.S.. We construct vector autoregression （VAR） model with variables such as unemployment rate （UMR）, consumer confidence index （CCI）, the Dow Jones industrial index （DJI）, and interest rate, etc., to forecast the HMI. Our model and analysis show that U.S. HMI very sensitive to unemployment and interest rates. Every 1% moves in unemployment and interest rates will result in HMI to move in the opposite direction by 11.7% and 11.4% respectively. However, changes in CCI and stock mark index have only minor impacts on HMI--0.49% and 0.3%, changes for 1% fluctuation in CCI and DJI. Our research also shows that relationships among these variables associated with housing market are very stable in the long run.展开更多
In time series literature, many authors have found out that multicollinearity and autocorrelation usually afflict time series data. In this paper, we compare the performances of classical VAR and Sims-Zha Bayesian VAR...In time series literature, many authors have found out that multicollinearity and autocorrelation usually afflict time series data. In this paper, we compare the performances of classical VAR and Sims-Zha Bayesian VAR models with quadratic decay on bivariate time series data jointly influenced by collinearity and autocorrelation. We simulate bivariate time series data for different collinearity levels (﹣0.99, ﹣0.95, ﹣0.9, ﹣0.85, ﹣0.8, 0.8, 0.85, 0.9, 0.95, 0.99) and autocorrelation levels (﹣0.99, ﹣0.95, ﹣0.9, ﹣0.85, ﹣0.8, 0.8, 0.85, 0.9, 0.95, 0.99) for time series length of 8, 16, 32, 64, 128, 256 respectively. The results from 10,000 simulations reveal that the models performance varies with the collinearity and autocorrelation levels, and with the time series lengths. In addition, the results reveal that the BVAR4 model is a viable model for forecasting. Therefore, we recommend that the levels of collinearity and autocorrelation, and the time series length should be considered in using an appropriate model for forecasting.展开更多
The paper aims to analyze the monetary transmission model between the monetary policy and the labor market variable of unemployment.The results of the data show that,the external shocks have an important impact especi...The paper aims to analyze the monetary transmission model between the monetary policy and the labor market variable of unemployment.The results of the data show that,the external shocks have an important impact especially on the Romanian interest rates but also on the domestic production;however,the impact is not significant on unemployment,which proves the resilience of the domestic labor market.The central bank policy rate has a stabilizing effect on the unemployment rate in case of an increase in the euro area policy rate.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
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China＇s astonishing economic growth implies a necessity to understand its inflation. The present paper employs threshold nonrecursive structural vector autoregression analysis to explore the asymmetric effects of macro-variables on inflation in low and high inflation regimes. The empirical evidence demonstrates, first, that the reactions of inflation to various shocks are inflation-regime-dependent and asymmetric. Second, monetary policy influences China ＂s high inflation and adjusting the domestic interest rate in China may be an effective way to control inflation in a high inflation regime, but not in a low inflation regime. In a high inflation regime, a high inflation rate may cause the macro-policy authorities to increase the domestic interest rate, in an attempt to stabilize high inflation. Third, contrary to expectations, the world oil price is not a strong cost-push factor in a low inflation regime. Oil price increases may increase inflation in a high inflation regime, but there is no such obvious effect in a low inflation regime. Finally, China ＂s nominal effective exchange rate influences inflation in both low and high inflation regimes. A nominal effeetive exchange rate appreciation might be effective in controlling domestic inflation in both regimes.
文摘In the parameter tracking of time-varying systems, the ordinary method is weighted least squares with the rectangular window or the exponential window. In this paper we propose a new kind of sliding window called the multiple exponential window, and then use it to fit time-varying Gaussian vector autoregressive models. The asymptotic bias and covariance of the estimator of the parameter for time-invariant models are also derived. Simulation results show that the multiple exponential windows have better parameter tracking effect than rectangular windows and exponential ones.
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supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2022S1A5A2A01038422)partly funded by the University of Economics Ho Chi Minh City,Vietnam.
文摘We analyze the connectedness between major cryptocurrencies and nonfungible tokens(NFTs)for different quantiles employing a time-varying parameter vector autoregression approach.We find that lower and upper quantile spillovers are higher than those at the median,meaning that connectedness augments at extremes.For normal,bearish,and bullish markets,Bitcoin Cash,Bitcoin,Ethereum,and Litecoin consistently remain net transmitters,while NFTs receive innovations.However,spillover topology at both extremes becomes simpler—from cryptocurrencies to NFTs.We find no markets useful for mitigating BTC risks,whereas BTC is capable of reducing the risk of other digital assets,which is a valuable insight for market players and investors.
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.
基金supported by the National Natural Science Foundation of China (No. 41071352)the National Social Science Foundation of China (No. 13BJY030)the National Science and Technology Support Program (No. 2012BAC03B01)
文摘As the biggest iron and steel producer in the world and one of the highest CO2 emission sectors, China’s iron and steel industry is undergoing a low-carbon transition accompanied by remarkable technological progress and investment adjustment, in response to the macroeconomic climate and policy intervention. Many drivers of the CO2 emissions of the iron and steel industry have been explored, but the relationships between CO2 abatement,investment and technological expenditure, and their connections with the economic growth and governmental policies in China, have not been conjointly and empirically examined. We proposed a concise conceptual model and an econometric model to investigate this crucial question. The results of regression, Granger causality test and impulse response analysis indicated that technological expenditure can significantly reduce CO2 emissions, and that investment expansion showed a negative impact on CO2 emission reduction. It was also argued with empirical evidence that a good economic situation favored CO2 abatement in China’s iron and steel industry, while achieving CO2 emission reduction in this industrial sector did not necessarily threaten economic growth.This shed light on the dispute over balancing emission cutting and economic growth.Regarding the policy aspects, the year 2000 was found to be an important turning point for policy evolution and the development of the iron and steel industry in China. The subsequent command and control policies had a significant, positive effect on CO2 abatement.
基金Projects(71874210,71633006,71501193) supported by the National Natural Science Foundation of China
文摘Based on the commodity property and finance property of gold in the international gold futures market,the influence factors of international gold futures price volatility are analyzed from the perspectives of supply and demand factors,financial factors and speculation factors.The structural vector autoregression(SVAR)model is applied to investigating the direction and strength of the effects of influence factors on the international gold futures prices and the variance decomposition approach(VDA)is used to compare the contributions of these factors.The results show that the supply and demand factors still play a fundamental role in the international gold futures price volatility and the role of“China’s gold demand”is exaggerated.The financial factors and speculation factors have significant impacts on the international gold futures price volatility,which reflects that the financial property of gold becomes increasingly important.Governments and investors should pay close attention to the financial property of gold futures.
基金funded by Special Research Project of Institute of Applied Ecology,CAS(No.Y5YZX151YD)Key Laboratory of Forest Ecology and Management,Institute of Applied Ecology,CAS(No.LFEM2016-05)
文摘The Natural Forest Protection Program(NFPP)is one of the key ecological forestry programs in China.It not only facilitates the improvement of forest ecological quality in NFPP areas,but also plays a significant role in increasing the carbon storage of forest ecosystems.The program covers 17 provinces,autonomous regions,and municipalities with correspondingly diverse forest resources and environments,ecological features,engineering measures and forest management regimes,all of which affect regional carbon storage.In this study,volume of timber harvest,tending area,pest-infested forest,firedamaged forest,reforestation,and average annual precipitation,and temperature were evaluated as factors that influence carbon storage.We developed a vector autoregression model for these seven indicators and we studied the dominant factors of carbon storage in the areas covered by NFPP.Timber harvest was the dominant factorinfluencing carbon storage in the Yellow and Yangtze River basins.Reforestation contributed most to carbon storage in the state-owned forest region in Xinjiang.In state-owned forest regions of Heilongjiang and Jilin Provinces,the dominant factors were forest fires and forest cultivation,respectively.For the enhancement of carbon sequestration capacity,a longer rotation period and a smaller timber harvest are recommended for the Yellow and Yangtze River basins.Trees should be planted in stateowned forests in Xinjiang.Forest fires should be prevented in state-owned forests in Heilongjiang,and greater forest tending efforts should be made in the state-owned forests in Jilin.
基金The Second Tibetan Plateau Scientific Expedition and Research(STEP)program,No.2019QZKK0608Talent Start Project of Beijing Normal University。
文摘The United States,Russia and China are militarily and economically among the most powerful countries in the post-Cold War period,and the interactions between the three powers heavily influence the international system.However,different conclusions about this question are generally made by researchers through qualitative analysis,and it is necessary to objectively and quantitatively investigate their interactions.Monthly-aggregated event data from the Global Data on Events,Location and Tone(GDELT)to measure cooperative and conflictual interactions between the three powers,and the complementary cumulative distribution function(CCDF)and the vector autoregression(VAR)method are utilized to investigate their interactions in two periods:January,1991 to September,2001,and October,2001 to December,2016.The results of frequencies and strengths analysis showed that:the frequencies and strengths of USA-China interactions slightly exceeded those of USA-Russia interactions and became the dominant interactions in the second period.Although that cooperation prevailed in the three dyads in two periods,the conflictual interactions between the USA and Russia tended to be more intense in the second period,mainly related to the strategic contradiction between the USA and Russia,especially in Georgia,Ukraine and Syria.The results of CCDF indicated that similar probabilities in the cooperative behaviors between the three dyads,but the differences in the probabilities of conflictual behaviors in the USA-Russia dyad showed complicated characteristic,and those between Russia and China indicated that Russia had been consistently giving China a hard time in both periods when dealing with conflict.The USA was always an essential factor in affecting the interactions between Russia and China in both periods,but China’s behavior only played a limited role in influencing the interactions between the USA-Russia dyad.Our study provides quantitative insight into the direct cooperative and conflictual interactions between the three dyads since the end of the Cold War and helps to understand their interactions better.
文摘Background:This paper examines the role of monetary and fiscal factors in interest rate variations in Sri Lanka under its deregulated regime of interest rates.In addition the paper also examines the role of monetary factors in the variation of interest rates,using a quarterly dataset for the post-global recession period,when the exchange rate is determined by market forces.Results:Empirical analysis uses a dataset of nominal interest rates,money growth,income growth,changes in nominal exchange rate,and budget deficit.From the methodological point of view the paper involves vector autoregression model and Wald tests of Granger causality,followed by impulse response analysis while stationarity and the order of integration of the selected variables are confirmed involving the augmented Dickey-Fuller and the Phillips-Perron unit-root tests.Results:The paper confirms that both monetary and fiscal factors have significant effects on the variations of interest rates.Money growth triggers an increase in interest rates,which supports the Fisher equation view,while income growth has a negative impact.Budget deficit causes a rise in interest rates,but the role of the exchange rate is found to be almost insignificant,probably due to including exchange rate series that cover both the pegged and market-based regimes of exchange rates.The second part of the analysis,using a quarterly dataset for the post-global recession period,further establishes the positive impact of M2 money growth and income growth on interest rates.In this case,exchange rate depreciation causes an increase in interest rates.Conclusions:The significant role of monetary and fiscal factors in interest rate variations implies it would be possible to manage interest rates through a judiciary management of monetary and fiscal policies.
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The purpose of this paper is to investigate the relationships among the variables, and how interest rate, unemployment, stock market, and consumer confidence affect housing market index （HM1） in the U.S.. We construct vector autoregression （VAR） model with variables such as unemployment rate （UMR）, consumer confidence index （CCI）, the Dow Jones industrial index （DJI）, and interest rate, etc., to forecast the HMI. Our model and analysis show that U.S. HMI very sensitive to unemployment and interest rates. Every 1% moves in unemployment and interest rates will result in HMI to move in the opposite direction by 11.7% and 11.4% respectively. However, changes in CCI and stock mark index have only minor impacts on HMI--0.49% and 0.3%, changes for 1% fluctuation in CCI and DJI. Our research also shows that relationships among these variables associated with housing market are very stable in the long run.
文摘In time series literature, many authors have found out that multicollinearity and autocorrelation usually afflict time series data. In this paper, we compare the performances of classical VAR and Sims-Zha Bayesian VAR models with quadratic decay on bivariate time series data jointly influenced by collinearity and autocorrelation. We simulate bivariate time series data for different collinearity levels (﹣0.99, ﹣0.95, ﹣0.9, ﹣0.85, ﹣0.8, 0.8, 0.85, 0.9, 0.95, 0.99) and autocorrelation levels (﹣0.99, ﹣0.95, ﹣0.9, ﹣0.85, ﹣0.8, 0.8, 0.85, 0.9, 0.95, 0.99) for time series length of 8, 16, 32, 64, 128, 256 respectively. The results from 10,000 simulations reveal that the models performance varies with the collinearity and autocorrelation levels, and with the time series lengths. In addition, the results reveal that the BVAR4 model is a viable model for forecasting. Therefore, we recommend that the levels of collinearity and autocorrelation, and the time series length should be considered in using an appropriate model for forecasting.
文摘The paper aims to analyze the monetary transmission model between the monetary policy and the labor market variable of unemployment.The results of the data show that,the external shocks have an important impact especially on the Romanian interest rates but also on the domestic production;however,the impact is not significant on unemployment,which proves the resilience of the domestic labor market.The central bank policy rate has a stabilizing effect on the unemployment rate in case of an increase in the euro area policy rate.
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.
