{"title":"预测石油价格：大型 BVARs 是否有用？","authors":"Bo Zhang , Bao H. Nguyen , Chuanwang Sun","doi":"10.1016/j.eneco.2024.107805","DOIUrl":null,"url":null,"abstract":"<div><p>Large Bayesian vector autoregression (BVAR) is a successful tool for forecasting macroeconomic variables, but the benefits to predict crude oil prices are rarely discussed. In this paper, we test the ability of BVAR to predict the real price of crude oil using a large dataset with 108 variables, taking into account all potential error structures that could affect modeling and forecasting, and performing multivariate analysis of crude oil prices, filling in the gaps in the field. The results demonstrated that the large BVAR having an excellent out-of-sample forecast performance at long horizons. Small and medium sizes BVAR provide more accurate information for short forecast horizons. We also find that the advantages of utilizing a large dataset become more obvious when incorporating non-standard error terms.</p></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"137 ","pages":"Article 107805"},"PeriodicalIF":14.2000,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0140988324005139/pdfft?md5=24e58085cc254bec43b04642422e1dcf&pid=1-s2.0-S0140988324005139-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Forecasting oil prices: Can large BVARs help?\",\"authors\":\"Bo Zhang , Bao H. Nguyen , Chuanwang Sun\",\"doi\":\"10.1016/j.eneco.2024.107805\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Large Bayesian vector autoregression (BVAR) is a successful tool for forecasting macroeconomic variables, but the benefits to predict crude oil prices are rarely discussed. In this paper, we test the ability of BVAR to predict the real price of crude oil using a large dataset with 108 variables, taking into account all potential error structures that could affect modeling and forecasting, and performing multivariate analysis of crude oil prices, filling in the gaps in the field. The results demonstrated that the large BVAR having an excellent out-of-sample forecast performance at long horizons. Small and medium sizes BVAR provide more accurate information for short forecast horizons. We also find that the advantages of utilizing a large dataset become more obvious when incorporating non-standard error terms.</p></div>\",\"PeriodicalId\":11665,\"journal\":{\"name\":\"Energy Economics\",\"volume\":\"137 \",\"pages\":\"Article 107805\"},\"PeriodicalIF\":14.2000,\"publicationDate\":\"2024-07-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0140988324005139/pdfft?md5=24e58085cc254bec43b04642422e1dcf&pid=1-s2.0-S0140988324005139-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Energy Economics\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0140988324005139\",\"RegionNum\":2,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy Economics","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0140988324005139","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
Large Bayesian vector autoregression (BVAR) is a successful tool for forecasting macroeconomic variables, but the benefits to predict crude oil prices are rarely discussed. In this paper, we test the ability of BVAR to predict the real price of crude oil using a large dataset with 108 variables, taking into account all potential error structures that could affect modeling and forecasting, and performing multivariate analysis of crude oil prices, filling in the gaps in the field. The results demonstrated that the large BVAR having an excellent out-of-sample forecast performance at long horizons. Small and medium sizes BVAR provide more accurate information for short forecast horizons. We also find that the advantages of utilizing a large dataset become more obvious when incorporating non-standard error terms.
期刊介绍：
Energy Economics is a field journal that focuses on energy economics and energy finance. It covers various themes including the exploitation, conversion, and use of energy, markets for energy commodities and derivatives, regulation and taxation, forecasting, environment and climate, international trade, development, and monetary policy. The journal welcomes contributions that utilize diverse methods such as experiments, surveys, econometrics, decomposition, simulation models, equilibrium models, optimization models, and analytical models. It publishes a combination of papers employing different methods to explore a wide range of topics. The journal's replication policy encourages the submission of replication studies, wherein researchers reproduce and extend the key results of original studies while explaining any differences. Energy Economics is indexed and abstracted in several databases including Environmental Abstracts, Fuel and Energy Abstracts, Social Sciences Citation Index, GEOBASE, Social & Behavioral Sciences, Journal of Economic Literature, INSPEC, and more.
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