{"title":"两步医疗通胀预测:计量经济表现和相关问题","authors":"Scott D. Gilbert, Gene A. Trevino","doi":"10.5085/jfe-492","DOIUrl":null,"url":null,"abstract":"\n This paper examines medical inflation forecasting based on a two-step method proposed by Gilbert (2019), whereby the medical inflation rate is forecast via the sum of two terms: a broad inflation published forecast and a historical average of the inflation gap—this being the difference between medical inflation and broad inflation. In a simple forecasting experiment, the two-step method compares favorably to the one-step method of forecasting medical inflation based on its past values alone. Stationarity tests applied to the inflation gap mostly support stationarity, with a possible historical break. The econometric results generally support the use of the two-step method, with a limited historical window for inflation gap averaging, consistent with Gilbert (2019).","PeriodicalId":265321,"journal":{"name":"Journal of Forensic Economics","volume":"101 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Two-Step Medical Inflation Forecasts: Econometric Performance and Related Issues\",\"authors\":\"Scott D. Gilbert, Gene A. Trevino\",\"doi\":\"10.5085/jfe-492\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n This paper examines medical inflation forecasting based on a two-step method proposed by Gilbert (2019), whereby the medical inflation rate is forecast via the sum of two terms: a broad inflation published forecast and a historical average of the inflation gap—this being the difference between medical inflation and broad inflation. In a simple forecasting experiment, the two-step method compares favorably to the one-step method of forecasting medical inflation based on its past values alone. Stationarity tests applied to the inflation gap mostly support stationarity, with a possible historical break. The econometric results generally support the use of the two-step method, with a limited historical window for inflation gap averaging, consistent with Gilbert (2019).\",\"PeriodicalId\":265321,\"journal\":{\"name\":\"Journal of Forensic Economics\",\"volume\":\"101 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Forensic Economics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5085/jfe-492\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Forensic Economics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5085/jfe-492","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Two-Step Medical Inflation Forecasts: Econometric Performance and Related Issues
This paper examines medical inflation forecasting based on a two-step method proposed by Gilbert (2019), whereby the medical inflation rate is forecast via the sum of two terms: a broad inflation published forecast and a historical average of the inflation gap—this being the difference between medical inflation and broad inflation. In a simple forecasting experiment, the two-step method compares favorably to the one-step method of forecasting medical inflation based on its past values alone. Stationarity tests applied to the inflation gap mostly support stationarity, with a possible historical break. The econometric results generally support the use of the two-step method, with a limited historical window for inflation gap averaging, consistent with Gilbert (2019).