Conditional Term Structure of Inflation Forecast Uncertainty: The Copula Approach
Wojciech Charemza,
Carlos Díaz () and
Svetlana Makarova ()
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Svetlana Makarova: University College London, UK.
Journal for Economic Forecasting, 2019, issue 1, 5-18
Abstract:
The paper introduces the concept of conditional inflation forecast uncertainty. It is proposed that the joint and conditional distributions of the bivariate forecast uncertainty can be derived from the estimation of the unconditional distributions of these uncertainties and applying appropriate copula function. Empirical results have been obtained for Canada and the US. Term structure has been evaluated in the form of unconditional and conditional probabilities of hitting the inflation range of ±1% around the Canadian inflation target. The paper suggests a new measure of inflation forecast uncertainty that accounts for possible inter-country dependence. It is shown that the evaluation of targeting precision can be effectively improved with the use of ex-ante formulated conditional and unconditional probabilities of inflation being within the pre-defined band around the target.
Keywords: macroeconomic forecasting; inflation; uncertainty; non-normality; density forecasting; forecast term structure; copula modelling (search for similar items in EconPapers)
JEL-codes: C53 E37 E52 (search for similar items in EconPapers)
Date: 2019
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http://www.ipe.ro/rjef/rjef1_19/rjef1_2019p5-18.pdf
Related works:
Working Paper: Conditional Term Structure of Inflation Forecast Uncertainty: The Copula Approach (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:rjr:romjef:v::y:2019:i:1:p:5-18
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