Tierney, Heather L.R. (2011): Forecasting and tracking realtime data revisions in inflation persistence.

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Abstract
This paper presents three local nonparametric forecasting methods that are able to utilize the isolated periods of revised realtime PCE and core PCE for 62 vintages within a historic framework with respect to the nonparametric exclusionfromcore inflation persistence model. The flexibility, provided by the kernel and window width, permits the incorporation of the forecasted value into the appropriate time frame. For instance, a low inflation measure can be included in other low inflation time periods in order to form more optimal forecasts by combining values that are similar in terms of metric distance as opposed to chronological time. The most efficient nonparametric forecasting method is the third model, which uses the flexibility of nonparametrics to its utmost by making forecasts conditional on the forecasted value.
Item Type:  MPRA Paper 

Original Title:  Forecasting and tracking realtime data revisions in inflation persistence 
Language:  English 
Keywords:  Inflation Persistence, RealTime Data, Monetary Policy, Nonparametrics, Forecasting 
Subjects:  C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C53  Forecasting and Prediction Methods ; Simulation Methods C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C14  Semiparametric and Nonparametric Methods: General E  Macroeconomics and Monetary Economics > E5  Monetary Policy, Central Banking, and the Supply of Money and Credit > E52  Monetary Policy 
Item ID:  34439 
Depositing User:  Heather L.R. Tierney 
Date Deposited:  01 Nov 2011 23:28 
Last Modified:  24 Feb 2016 01:05 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/34439 