Utilizing the Vector Autoregression Model (VAR) for Short-Term Solar Irradiance Forecasting

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.

Vector Autoregressive （VAR） Modeling and Projection of DSE

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.

Impact of Inflation, Dollar Exchange Rate and Interest Rate on Red Meat Production in Turkey： Vector Autoregressive （VAR） Analysis

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.

Influence factors of international gold futures price volatility

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.

A statistical analysis of spatiotemporal variations and determinant factors of forest carbon storage under China's Natural Forest Protection Program

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.

LEARNING CAUSAL GRAPHS OF NONLINEAR STRUCTURAL VECTOR AUTOREGRESSIVE MODEL USING INFORMATION THEORY CRITERIA

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.

The Connection of Vegetation with Tourism Development and Economic Growth： A Case Study for Aruba

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.

The Impact of Macroeconomic Fluctuations on Stock Exchange Markets： A Comparative Analysis on CEECs

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.

Synergic Relationship between the Grain for Green Program and the Agricultural Eco-economic System in Ansai County based on the VAR model

Understanding the synergic relationship between the Grain for Green Program(GGP)and the agricultural eco-economic system is important for designing an optimized agricultural eco-economic system and developing a highly efficient structure of an agricultural industry chain and a resource chain.This study used Ansai County time series data from 1995 to 2014,applied vector autoregressive(VAR)models and used tools such as Granger causality,impulse response analysis and variance decomposition,to explore the synergy between the GGP and the agricultural eco-economic system.The results revealed a synergic and reciprocal relationship between the GGP and the agroeconomic system.The contribution of the GGP to the agroecosystem reached 34%,which was significantly higher than either its largest contribution to the agroeconomic system(20.8%)or its peak contribution to the agrosocial system(26.7%).The agroeconomic system had the most prominent influence on the GGP,with a year-round stable contribution of up to 55.3%.These results were consistent with reality.However,the impact of the GGP on the agricultural eco-economic system was weaker than the effect of the agricultural eco-economic system on the GGP.The lag of variable stationarity after the shock was relatively short,indicating that optimal coupling had not formed between the GGP and the agricultural eco-economic system.On the basis of enhancing the ecological functions,we should construct the agricultural industry-resource chain such that it focuses on promoting the effective utilization of resources in the region.In addition,the development of a carbon sink industry can be used to manifest the ecological values of ecological functions.

The cooperative and conflictual interactions between the United States,Russia,and China:A quantitative analysis of event data

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.