{"title":"Using Local Information to Improve Short-Run Corn Price Forecasts","authors":"Xiaojie Xu","doi":"10.1515/jafio-2017-0018","DOIUrl":null,"url":null,"abstract":"Abstract We examine the short-run forecasting problem in a data set of daily prices from 134 corn buying locations from seven states – Iowa, Illinois, Indiana, Ohio, Minnesota, Nebraska, and Kansas. We ask the question: is there useful forecasting information in the cash bids from nearby markets? We use several criteria, including a Granger causality criterion, to specify forecast models that rely on the recent history of a market, the recent histories of nearby markets, and the recent histories of futures prices. For about 65% of the markets studied, the model consisting of futures prices, a market’s own history, and the history of nearby markets forecasts better than a model only incorporating futures prices and the market’s own history. That is, nearby markets have predictive content. But the magnitude varies with the forecast horizon. For short-run forecasts, the forecast accuracy improvement from including nearby markets is modest. As the forecast horizon increases, however, including nearby prices tends to significantly improve forecasts. We also examine the role played by physical market density in determining the value of incorporating nearby prices into a forecast model.","PeriodicalId":52541,"journal":{"name":"Journal of Agricultural and Food Industrial Organization","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/jafio-2017-0018","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Agricultural and Food Industrial Organization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/jafio-2017-0018","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Business, Management and Accounting","Score":null,"Total":0}
引用次数: 7
Abstract
Abstract We examine the short-run forecasting problem in a data set of daily prices from 134 corn buying locations from seven states – Iowa, Illinois, Indiana, Ohio, Minnesota, Nebraska, and Kansas. We ask the question: is there useful forecasting information in the cash bids from nearby markets? We use several criteria, including a Granger causality criterion, to specify forecast models that rely on the recent history of a market, the recent histories of nearby markets, and the recent histories of futures prices. For about 65% of the markets studied, the model consisting of futures prices, a market’s own history, and the history of nearby markets forecasts better than a model only incorporating futures prices and the market’s own history. That is, nearby markets have predictive content. But the magnitude varies with the forecast horizon. For short-run forecasts, the forecast accuracy improvement from including nearby markets is modest. As the forecast horizon increases, however, including nearby prices tends to significantly improve forecasts. We also examine the role played by physical market density in determining the value of incorporating nearby prices into a forecast model.
期刊介绍:
The Journal of Agricultural & Food Industrial Organization (JAFIO) is a unique forum for empirical and theoretical research in industrial organization with a special focus on agricultural and food industries worldwide. As concentration, industrialization, and globalization continue to reshape horizontal and vertical relationships within the food supply chain, agricultural economists are revising both their views of traditional markets as well as their tools of analysis. At the core of this revision are strategic interactions between principals and agents, strategic interdependence between rival firms, and strategic trade policy between competing nations, all in a setting plagued by incomplete and/or imperfect information structures. Add to that biotechnology, electronic commerce, as well as the shift in focus from raw agricultural commodities to branded products, and the conclusion is that a "new" agricultural economics is needed for an increasingly complex "new" agriculture.