{"title":"COVID-19 之后全球橄榄油价格的趋势和持续性","authors":"Manuel Monge","doi":"10.1057/s41272-024-00481-x","DOIUrl":null,"url":null,"abstract":"<p>Once the coronavirus pandemic was declared by government authorities in March 2020 and several measures were adopted around the world to limit the effects of COVID-19, the limit agroeconomic processing affected important operations such as not being able to prepare the olive trees for the next harvest. This lack of processes has caused the consumer to perceive an increase in prices due to the shortage of product and the growing demand for olive oil around the world. This research paper, through the use of advanced statistical and econometric techniques, attempts to perform a specific analysis and understand the persistence of the data and the trend of global olive oil prices. Artificial intelligence techniques such as neural network models are also used to predict long-term price behavior. Using ARFIMA (p, d, q) model, the results suggest a non-mean reversion behavior, suggesting that the shock is expected to be permanent, causing a change in trend. This result is in line with that obtained using machine learning techniques, where the forecast suggests an increase of the prices around + 11.36% in the next 12 months.</p>","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Trends and persistence in global olive oil prices after COVID-19\",\"authors\":\"Manuel Monge\",\"doi\":\"10.1057/s41272-024-00481-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Once the coronavirus pandemic was declared by government authorities in March 2020 and several measures were adopted around the world to limit the effects of COVID-19, the limit agroeconomic processing affected important operations such as not being able to prepare the olive trees for the next harvest. This lack of processes has caused the consumer to perceive an increase in prices due to the shortage of product and the growing demand for olive oil around the world. This research paper, through the use of advanced statistical and econometric techniques, attempts to perform a specific analysis and understand the persistence of the data and the trend of global olive oil prices. Artificial intelligence techniques such as neural network models are also used to predict long-term price behavior. Using ARFIMA (p, d, q) model, the results suggest a non-mean reversion behavior, suggesting that the shock is expected to be permanent, causing a change in trend. This result is in line with that obtained using machine learning techniques, where the forecast suggests an increase of the prices around + 11.36% in the next 12 months.</p>\",\"PeriodicalId\":1,\"journal\":{\"name\":\"Accounts of Chemical Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":16.4000,\"publicationDate\":\"2024-04-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Accounts of Chemical Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1057/s41272-024-00481-x\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1057/s41272-024-00481-x","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Trends and persistence in global olive oil prices after COVID-19
Once the coronavirus pandemic was declared by government authorities in March 2020 and several measures were adopted around the world to limit the effects of COVID-19, the limit agroeconomic processing affected important operations such as not being able to prepare the olive trees for the next harvest. This lack of processes has caused the consumer to perceive an increase in prices due to the shortage of product and the growing demand for olive oil around the world. This research paper, through the use of advanced statistical and econometric techniques, attempts to perform a specific analysis and understand the persistence of the data and the trend of global olive oil prices. Artificial intelligence techniques such as neural network models are also used to predict long-term price behavior. Using ARFIMA (p, d, q) model, the results suggest a non-mean reversion behavior, suggesting that the shock is expected to be permanent, causing a change in trend. This result is in line with that obtained using machine learning techniques, where the forecast suggests an increase of the prices around + 11.36% in the next 12 months.
期刊介绍:
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.