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.
机构:
Renmin Univ China, Sch Finance, Beijing, Peoples R ChinaRenmin Univ China, Sch Finance, Beijing, Peoples R China
Zhang, Chengsi
Osborn, Denise R.
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Univ Manchester, Ctr Growth & Business Cycle Res, Manchester M13 9PL, Lancs, EnglandRenmin Univ China, Sch Finance, Beijing, Peoples R China
Osborn, Denise R.
Kim, Dong Heon
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Univ Manchester, Ctr Growth & Business Cycle Res, Manchester M13 9PL, Lancs, England
Korea Univ, Dept Econ, Seoul, South KoreaRenmin Univ China, Sch Finance, Beijing, Peoples R China
机构:
NE Illinois Univ, Dept Econ, BBH 344G,5500 N St Louis Ave, Chicago, IL 60625 USANE Illinois Univ, Dept Econ, BBH 344G,5500 N St Louis Ave, Chicago, IL 60625 USA