{"title":"用新凯恩斯主义菲利普斯曲线预测通货膨胀:频率问题*","authors":"Manuel M. F. Martins, Fabio Verona","doi":"10.1111/obes.12618","DOIUrl":null,"url":null,"abstract":"<p>We forecast US inflation with a new Keynesian Phillips curve (NKPC) in the frequency domain. Our method consists of decomposing the time series of inflation and its NKPC predictors into several frequency bands, forecasting separately each frequency component of inflation, and then summing up those forecasts to obtain the forecast for aggregate inflation. We find that (i) accurately forecasting the low frequency of inflation is, on average, crucial to successfully forecast inflation; (ii) our NKPC low-frequency forecast model consistently and significantly outperforms the time-series NKPC and standard benchmark models; (iii) the low frequencies of inflation expectations and unemployment are the key predictors; and (iv) optimally switching on / off the forecasts of each frequency components of inflation at each period allows to outstandingly track inflation and show that all frequencies of inflation matter.</p>","PeriodicalId":54654,"journal":{"name":"Oxford Bulletin of Economics and Statistics","volume":"86 4","pages":"811-832"},"PeriodicalIF":1.5000,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Forecasting Inflation with the New Keynesian Phillips Curve: Frequencies Matter*\",\"authors\":\"Manuel M. F. Martins, Fabio Verona\",\"doi\":\"10.1111/obes.12618\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>We forecast US inflation with a new Keynesian Phillips curve (NKPC) in the frequency domain. Our method consists of decomposing the time series of inflation and its NKPC predictors into several frequency bands, forecasting separately each frequency component of inflation, and then summing up those forecasts to obtain the forecast for aggregate inflation. We find that (i) accurately forecasting the low frequency of inflation is, on average, crucial to successfully forecast inflation; (ii) our NKPC low-frequency forecast model consistently and significantly outperforms the time-series NKPC and standard benchmark models; (iii) the low frequencies of inflation expectations and unemployment are the key predictors; and (iv) optimally switching on / off the forecasts of each frequency components of inflation at each period allows to outstandingly track inflation and show that all frequencies of inflation matter.</p>\",\"PeriodicalId\":54654,\"journal\":{\"name\":\"Oxford Bulletin of Economics and Statistics\",\"volume\":\"86 4\",\"pages\":\"811-832\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2024-05-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Oxford Bulletin of Economics and Statistics\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/obes.12618\",\"RegionNum\":3,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Oxford Bulletin of Economics and Statistics","FirstCategoryId":"96","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/obes.12618","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECONOMICS","Score":null,"Total":0}
Forecasting Inflation with the New Keynesian Phillips Curve: Frequencies Matter*
We forecast US inflation with a new Keynesian Phillips curve (NKPC) in the frequency domain. Our method consists of decomposing the time series of inflation and its NKPC predictors into several frequency bands, forecasting separately each frequency component of inflation, and then summing up those forecasts to obtain the forecast for aggregate inflation. We find that (i) accurately forecasting the low frequency of inflation is, on average, crucial to successfully forecast inflation; (ii) our NKPC low-frequency forecast model consistently and significantly outperforms the time-series NKPC and standard benchmark models; (iii) the low frequencies of inflation expectations and unemployment are the key predictors; and (iv) optimally switching on / off the forecasts of each frequency components of inflation at each period allows to outstandingly track inflation and show that all frequencies of inflation matter.
期刊介绍:
Whilst the Oxford Bulletin of Economics and Statistics publishes papers in all areas of applied economics, emphasis is placed on the practical importance, theoretical interest and policy-relevance of their substantive results, as well as on the methodology and technical competence of the research.
Contributions on the topical issues of economic policy and the testing of currently controversial economic theories are encouraged, as well as more empirical research on both developed and developing countries.