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.

What Causes China's High Inflation? A Threshold Structural Vector Autoregression Analysis

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.

Recursive Least Squares Estimator with Multiple Exponential Windows in Vector Autoregression

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.

Production performance forecasting method based on multivariate time series and vector autoregressive machine learning model for waterflooding reservoirs

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.

数字经济发展对制造业转型升级的影响效应——基于安徽省2006-2020年16个地级市面板数据的实证分析

安徽省制造业发展多年仍存在着大而不强不优的问题,制造业亟需转型升级,数字经济的发展为安徽省制造业转型升级提供了可能.本文分析了安徽省数字经济发展现状,对各地级市进行了数字经济梯队划分,在此基础上,以2006-2020年安徽省16个地级市面板数据为样本建立Cobb-Douglas生产函数和面板向量自回归模型(Panel Vector AutoRegression, PVAR),实证分析了数字经济三个维度对安徽省制造业转型升级的影响和具体作用过程,从政策和数字经济角度提出了相关对策和建议.

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.

Monetary and fiscal factors in nominal interest rate variations in Sri Lanka under a deregulated regime

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.

On the Performances of Classical VAR and Sims-Zha Bayesian VAR Models in the Presence of Collinearity and Autocorrelated Error Terms

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.

Monetary Policy and Unemployment:The Case of Romania

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.

Economics,fundamentals,technology,finance,speculation and geopolitics of crude oil prices:an econometric analysis and forecast based on data from 1990 to 2017

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.

Modeling and forecasting time series of precious metals:a new approach to multifractal data

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.

A Note on Parameter Estimations of Panel Vector Autoregressive Models with Intercorrelation

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.

Provincial Output Spillovers in China:Global Vector Autoregressive Approach

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.

Application of data science in the prediction of solar energy for the Amazon basin:a study case

The need for renewable energy sources has challenged most countries to comply with environmental protection actions and to handle climate change.Solar energy figures as a natural option,despite its intermittence.Brazil has a green energy matrix with significant expansion of solar form in recent years.To preserve the Amazon basin,the use of solar energy can help communities and cities improve their living standards without new hydroelectric units or even to burn biomass,avoiding harsh environmental consequences.The novelty of this work is using data science with machine-learning tools to predict the solar incidence(W.h/m^(2))in four cities in Amazonas state(north-west Brazil),using data from NASA satellites within the period of 2013-22.Decision-tree-based models and vector autoregressive(time-series)models were used with three time aggregations:day,week and month.The predictor model can aid in the economic assessment of solar energy in the Amazon basin and the use of satellite data was encouraged by the lack of data from ground stations.The mean absolute error was selected as the output indicator,with the lowest values obtained close to 0.20,from the adaptive boosting and light gradient boosting algorithms,in the same order of magnitude of similar references.

Low-carbon transition of iron and steel industry in China:Carbon intensity, economic growth and policy intervention

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.

Impact of the Global Financial Crisis on China: Empirical Evidence and Policy Implications

This paper applies a structural vector autoregression analysis to quantify the impact of the global financial crisis on China. It is found that the impact is indeed sizeable： a 1-percent decline in economic growth in the USA, the EU and Japan is likely to lead to a0. 73-percent decline in growth in China. The article discusses whether the current measures of fiscal stimulus are adequate to offset the sharp decline in external demand Although there is little doubt that the massive fiscal stimulus will largely offset the significant shortfalls in external demand, the current growth pattern in China will be increasingly unsustainable in the long term. China ＂s reform cycles suggest that external shocks are often opportunities for structural reforms. Therefore, the crisis could also be a catulyst for rebalancing China ＇s economic structure so as to return the economy to a sustainable path.

中美俄合作与冲突互动——基于事件数据的定量分析

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.

Mix of Fiscal and Monetary Policy Rules and Inflation Dynamics in China

The present paper examines the role of the mix of fiscal and monetary policy rules in determining inflation dynamics using fiscal and monetary policy reaction func.tions and Markov-switching vector autoregression methods based on quarterly data in the period 1992-2007. Our results show that fiseal and monetary policies in China can be adequately described using some simple rules, and that significant regime shifts took plaee around 1998. Fiscal policy tended to be active and countereyclical in the pre-1998 period, then switched to be passive and more eountercyclical, whereas monetary policy was characterized as passive and procyclical in the pre-1998 period, and switched to be active and countercyclical afterwards. The mix of fiscal and monetary policy rules can explain inflation dynamics better than the monetary policy rule alone. Therefore, price stability requires not only appropriate monetary policy but also appropriate fiseal policy.

Optimal Monetary Policy in China

Using the structural vector autoregression model, we estimate the current responses of monetary policy to contemporaneous shocks from macroeconomic variables. Our findings indicate that the People＇s Bank of China responded to inflation and output changes, but did not react to asset price fluctuations during the period from January 1997 to March 2010. The optimal monetary responses to exogenous shocks are also examined It is revealed that using asset prices to formulate monetary policy would not help to improve monetary authorities ＇performance in lowering the volatilities of output growth and inflation while keeping output growth and inflation in their safety zones. The effectiveness of monetary policy and fiscal policy in reacting to external shocks is also discussed.

Autonomy and Effectiveness of Chinese Monetary Policy under the De Facto Fixed Exchange Rate System

This paper uses monthly data to examine the autonomy and effectiveness of monetary policy in China under the de facto fixed exchange rate arrangement in place from 1998 to 2005. The results obtained from Granger causality tests in a vector autoregression framework indicate that： （i） China actually conducted independent monetary policy during the fixed exchange rate period; and （ii） market-oriented policy measures are impotent in influencing real output and prices. The framework of the investigation into the autonomy of monetary policy adapts to the Chinese economic condition that primary loan and deposit rates are set by the central bank. Based on the empirical results, the present paper provides alternative strategies to improve the effectiveness of monetary policy in China, including developing the financial system and solidifying microeconomic fundamentals instead of forcing the adaptation of a more flexible exchange rate regime.