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Learning, endogenous indexation and disinflation in the New-Keynesian model

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  • Wieland, Volker

Abstract

This paper introduces adaptive learning and endogenous indexation in the New-Keynesian Phillips curve and studies disinflation under inflation targeting policies. The analysis is motivated by the disinflation performance of many inflation-targeting countries, in particular the gradual Chilean disinflation with temporary annual targets. At the start of the disinflation episode price-setting firms’ expect inflation to be highly persistent and opt for backward-looking indexation. As the central bank acts to bring inflation under control, price-setting firms revise their estimates of the degree of persistence. Such adaptive learning lowers the cost of disinflation. This reduction can be exploited by a gradual approach to disinflation. Firms that choose the rate for indexation also re-assess the likelihood that announced inflation targets determine steady-state inflation and adjust indexation of contracts accordingly. A strategy of announcing and pursuing short-term targets for inflation is found to influence the likelihood that firms switch from backward-looking indexation to the central bank’s targets. As firms abandon backward-looking indexation the costs of disinflation decline further. We show that an inflation targeting strategy that employs temporary targets can benefit from lower disinflation costs due to the reduction in backward-looking indexation. --

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Bibliographic Info

Paper provided by Center for Financial Studies (CFS) in its series CFS Working Paper Series with number 2008/17.

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Date of creation: 2008
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Handle: RePEc:zbw:cfswop:200817

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Keywords: Learning; Monetary Policy; New-Keynesian Model; Indexation; Inflation Targeting; Disinflation; Recursive Least Squares;

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Cited by:
  1. Schmidt, Sebastian & Wieland, Volker, 2013. "The New Keynesian Approach to Dynamic General Equilibrium Modeling: Models, Methods and Macroeconomic Policy Evaluation," Handbook of Computable General Equilibrium Modeling, Elsevier.

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