Conditional Term Structure of Inflation Forecast Uncertainty: The Copula Approach

Wojciech Charemza, Carlos Díaz () and Svetlana Makarova ()

No 15/07, Discussion Papers in Economics from Division of Economics, School of Business, University of Leicester

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 estimation unconditional distributions of these uncertainties and applying appropriate copula function. Empirical results have been obtained for Canada and 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 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: 2015-05
New Economics Papers: this item is included in nep-cba, nep-ecm, nep-for and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Related works:
Journal Article: Conditional Term Structure of Inflation Forecast Uncertainty: The Copula Approach (2019)
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