ERN: Other Econometrics: Applied Econometric Modeling in Agriculture最新文献

{"title":"Parametric and Non-Parametric Cost Efficiency Benchmarking of Water Utilities in Russia","authors":"I. Dolmatov, V. Dvorkin, Igor V. Maskaev","doi":"10.2139/ssrn.2698129","DOIUrl":"https://doi.org/10.2139/ssrn.2698129","url":null,"abstract":"The activities of the water companies in Russia are regulated in order to overcome market failures caused by regional monopolization, information asymmetries and the need to find a balance between the interests of consumers and company objectives for its normal functioning and development. In the Russian Federation, the regulator uses the outdated and inefficient cost method, which deprives the company an incentive to reduce their own costs. However, Russian regulator is in active discussion about the transition to modern long-term management regulation practices in order to increase companies’ efficiency, which in the framework of the regulation defines the future of the company's profits. Russian regulator should take more active steps to encourage regulated companies to increase efficiency and productivity. Solution is to move to using benchmarking, which allows to identify sources of companies’ inefficiency to assess the validity of the established tariffs. This study presents the first attempt to implement benchmarking methods used by the world's leading regulators to determine the cost efficiency of companies and improve their potential. The authors tested a parametric (COLS) and non-parametric (DEA) methods to assess the performance of companies with different technical and economic characteristics more accurately. The study makes a number of recommendations for the specification of the model, assessing its sensitivity to the changes in samples. The authors concluded that the model based on COLS is of high quality and resistance to changing of sample while assessing the technical efficiency. However, a similar statement for DEA models is unfair, since the inclusion in the analysis of either too large or too small companies does not lead to plausible results. On the other hand, DEA allowed to assess not only the technical efficiency of companies but also the allocative one. In general, the authors have shown that the potential for increasing the efficiency of Russian water supply companies is large enough, and the regulator is necessary to accelerate the transition to incentive regulation in order to increase efficiency in the sector.","PeriodicalId":403142,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Agriculture","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115201655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling and Predicting Photovoltaic Power Generation in the EEX Market","authors":"Almut E. D. Veraart, Hanna Zdanowicz","doi":"10.2139/ssrn.2691906","DOIUrl":"https://doi.org/10.2139/ssrn.2691906","url":null,"abstract":"The importance of solar energy has been growing in recent years. This raises the need for efficient modelling and forecasting methods. The existing methods are predominantly based on weather predictions or forecast solar radiation, which is not easy to convert into production forecast. Instead we propose to directly model the photovoltaic power production in the EEX market in Germany by time series methods. To this end we test an autoregressive moving average (ARMA) model combined with three types of generalised autoregressive conditional heteroscedastic (GARCH) models for the univariate case of solar production aggregated over the whole country, and an vector autoregressive (VAR) model for the multivariate case of individual regions divided among four transmission system operators (TSOs). We compare the output from the models with forecasts provided by the producers. The study reveals that our models work very well compared to rather complex models used by the TSOs. In addition, our stochastic models provide valuable insight into the market and can be used as a building block for risk management purposes in energy markets.","PeriodicalId":403142,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Agriculture","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116806893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Does Technical Analysis Beat the Market? – Evidence from High Frequency Trading in Gold and Silver","authors":"Andrew Urquhart, J. Batten, B. Lucey, Frank McGroarty, M. Peat","doi":"10.2139/ssrn.2652637","DOIUrl":"https://doi.org/10.2139/ssrn.2652637","url":null,"abstract":"Previous research has identified that investors place more emphasis on technical analysis than fundamental analysis, however the research has largely been confined to daily data and stock market indices. This paper studies whether intraday technical trading rules produce significant payoffs in the gold and silver market using three popular moving average rules. We find that using the standard parameters previously used in the literature, technical trading rules offer are not profitable. However after utilising a universe of parameters, we find a number of parameter combinations offer significant profits in the gold market, but there remains no significant payoff in the silver market. Our results show that parameters that use longer histories are more successful than the traditional parameters chosen in the literature. Intraday technical trading rules can be profitable in the gold market but offer no significant profit in the silver market.","PeriodicalId":403142,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Agriculture","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115975353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the Dynamics of Indian GDP, Crude Oil Production and Imports","authors":"A. Tiwari","doi":"10.1111/opec.12047","DOIUrl":"https://doi.org/10.1111/opec.12047","url":null,"abstract":"This study investigates the static and dynamic causal relationship among income, crude oil production and imports for the Indian economy. The static-short-run Granger causality shows that income and crude oil imports Granger-cause domestic crude oil production. This implies that gross domestic product (GDP) and oil imports contain important information in predicting the production of crude oil not the vice versa. The error correction value −0.415 implies that disequilibrium in GDP will get corrected in the long run by the speed of adjustment of 41.5 per cent in a year. The dynamic analysis reveals that the most exogenous variable is crude oil production as it is mostly dependent on itself, and relatively less is accounted by other two variables; GDP is a relatively less exogenous variable, and crude oil exports fairly good proportion of forecast error. Further, we found that the most endogenous variable is crude oil imports which is mostly dependent on crude oil production.","PeriodicalId":403142,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Agriculture","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131021939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can Electricity Consumption Be Useful in Predicting Nigerian Economic Growth? Evidence from Error Correction Model","authors":"A. Adedokun","doi":"10.1111/opec.12042","DOIUrl":"https://doi.org/10.1111/opec.12042","url":null,"abstract":"This paper investigates Granger causality between electricity consumption and Nigerian real GDP, using Vector Error Correction (VEC) Granger Causality/Block Exogeneity Wald Test. The empirical result supports long run bidirectional relationship, which means that electricity consumption predicts economic growth and vice versa in the country, though causality from electricity consumption to economic growth is stronger. This finding is evidence in support of the country's electricity deregulation and reform programme, which is a core component of various projects towards the realisation of Transformation Agenda (TA) and Vision 20: 2020, in which Nigeria aims to become one of the 20 largest economies by the year 2020.","PeriodicalId":403142,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Agriculture","volume":"220 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115651355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Commodity Forward Curve Dynamics with Inventory Information","authors":"Marcel Prokopczuk, Sebastian Vicedom","doi":"10.2139/ssrn.2579834","DOIUrl":"https://doi.org/10.2139/ssrn.2579834","url":null,"abstract":"In this paper we introduce a new two-factor commodity term structure model for which inventories serve as a second state variable. We derive a closed-form formula for futures prices and empirically analyze the model's properties. Besides being economically appealing, our model also outperforms the well-known Gibson and Schwartz (1990) model in terms of hedging abilities.","PeriodicalId":403142,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Agriculture","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130560501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Panel Data Approaches for Estimating Oil Demand Elasticity","authors":"Afshin Javan, Nahl Zahran","doi":"10.1111/opec.12040","DOIUrl":"https://doi.org/10.1111/opec.12040","url":null,"abstract":"This study examines the general relationships between crude oil consumption, real oil price and real GDP using a quarterly time series from 1993 to 2012. Specifically, the long-term and short-term GDP and price elasticities of oil consumption per capita were estimated using dynamic panel and pooled data regressions based on Nerlove's oil demand model for 25 countries that represent 75 per cent of global oil demand. Price elasticities were found for most OECD countries. These estimates were low and consistent with previous estimates. According to the study results, the short-run price elasticity ranged between −0.05 and −0.20 and the long-run between −0.11 and −0.36. Price elasticities for most non-OECD countries were either positive or insignificant. Estimates of GDP elasticities varied. The short-run GDP elasticity was between 0.15 and 1.09, while the long-run was between 0.21 and 1.54. On average, income elasticity for OECD countries was found to be slightly higher than for non-OECD countries. Contrary to expectations, we found China's income elasticity to be 0.34 in the short run, but it was 0.76 in the long run.","PeriodicalId":403142,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Agriculture","volume":"171 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134141424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Future of Shale Gas in China and its Influence on the Global Markets for Natural Gas","authors":"Y. Yegorov, Jalal Dehnavi","doi":"10.2139/ssrn.2524357","DOIUrl":"https://doi.org/10.2139/ssrn.2524357","url":null,"abstract":"The goal of this paper is to evaluate the perspectives of China to become less dependent on gas imports in future. This paper tries to forecast the future production of natural gas in China and demand for imports. The key problem is to what extent China can become the second country after the USA with shale gas revolution and whether it will be able to do that in the next 20 years. We find several obstacles preventing the scenario of China becoming net gas exporter; among them lack of conventional gas reserves and water scarcity in the location of shale gas deposits. However, there is huge uncertainty in the volume of future gas imports from China resulting not only from the uncertainty of its demand but also of supply. Results show that China's energy strategy will lead to an increase in the speed of shale gas extraction.","PeriodicalId":403142,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Agriculture","volume":"262 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116076897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting the Brent Oil Price: Addressing Time-Variation in Forecast Performance","authors":"Cristiana Mǎnescu, Ine Van Robays","doi":"10.2139/ssrn.2906230","DOIUrl":"https://doi.org/10.2139/ssrn.2906230","url":null,"abstract":"This paper demonstrates how the real-time forecasting accuracy of different Brent oil price forecast models changes over time. We find considerable instability in the performance of all models evaluated and argue that relying on average forecasting statistics might hide important information on a model`s forecasting properties. To address this instability, we propose a forecast combination approach to predict quarterly real Brent oil prices. A four-model combination (consisting of futures, risk-adjusted futures, a Bayesian VAR and a DGSE model of the oil market) predicts Brent oil prices more accurately than the futures and the random walk up to 11 quarters ahead, on average, and generates a forecast whose performance is remarkably robust over time. In addition, the model combination reduces the forecast bias and predicts the direction of the oil price changes more accurately than both benchmarks. JEL Classification: Q43, C43, E32","PeriodicalId":403142,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Agriculture","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124812543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Crude Oil Price Dynamics and Transmission Mechanism of the Macroeconomic Indicators in Nigeria","authors":"J. Omojolaibi","doi":"10.1111/opec.12031","DOIUrl":"https://doi.org/10.1111/opec.12031","url":null,"abstract":"The preoccupation of this study is to investigate the dynamic impacts of crude oil price on the economic growth of Nigeria. The technique of estimation used in this study is the structural vector autoregressive type. This method is applied to articulate the transmission mechanism of macroeconomic effects of domestic price level, economic output, money supply and volatile crude oil price in Nigeria. The study sample covers the period between 1985 and 2010. The data used are on a quarterly basis. The results of both the impulse response functions and the forecast error variance decompositions indicate that domestic shocks are responsible for a reasonable portion of crude oil price fluctuations. Although crude oil price volatility has significant positive impacts on economic output, however, money supply shocks are the main cause of gross domestic product fluctuations. This study concludes that crude oil price has very important impact on the Nigerian economy and the monetary policy is the channel through which this impact transmits.","PeriodicalId":403142,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Agriculture","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123874891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}