Forecasting Inflation with the New Keynesian Phillips Curve: Frequencies Matter
Manuel Martins and
Fabio Verona
Oxford Bulletin of Economics and Statistics, 2024, vol. 86, issue 4, 811-832
Abstract:
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.
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://doi.org/10.1111/obes.12618
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:bla:obuest:v:86:y:2024:i:4:p:811-832
Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0305-9049
Access Statistics for this article
Oxford Bulletin of Economics and Statistics is currently edited by Christopher Adam, Anindya Banerjee, Christopher Bowdler, David Hendry, Adriaan Kalwij, John Knight and Jonathan Temple
More articles in Oxford Bulletin of Economics and Statistics from Department of Economics, University of Oxford Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().