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Estimating Output Gap, Core Inflation, and the NAIRU for Peru

Author

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  • Rodríguez, Gabriel

    (Central Reserve Bank of Peru and Pontificia Universidad Católica del Perú)

Abstract

Following Doménech and Gómez (2006), and using quarterly Peruvian data for 1970:1-2007:4, I estimate a model that exploits the information contained in the inflation, unemployment and private investment rates in order to estimate non-observable variables as output gap, the NAIRU and the core inflation. The unknown parameters are esti- mated by maximun likelihood using a Kalman filter initialized with a partially difuse prior, and the unobserved components are estimated using a smoothing algorithm. The results suggest that only the infla- tion rate contains useful information in order to estimate the output gap. Estimates suggest poor performance for the unemployment and private investment rates. I explain this issue as related to the poor quality of the construction of these variables. In order to perform a sensitivity analysis, I estimate the output gap using other alternative methods. The correlations are very different and very far away from the estimates obtained in this paper. It is clear that estimates obtained from simple statistical filters give poor approximations.

Suggested Citation

  • Rodríguez, Gabriel, 2009. "Estimating Output Gap, Core Inflation, and the NAIRU for Peru," Working Papers 2009-011, Banco Central de Reserva del Perú.
    • Handle: RePEc:rbp:wpaper:2009-011
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    File URL: https://www.bcrp.gob.pe/docs/Publicaciones/Documentos-de-Trabajo/2009/Working-Paper-11-2009.pdf
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    References listed on IDEAS

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    Cited by:

    1. Alejandro Gaviria Jaramillo & Santiago Téllez Alzate, 2010. "Expectativas de inflación en Colombia," Vniversitas Económica 8299, Universidad Javeriana - Bogotá.

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    More about this item

    Keywords

    Potential Output; Core Inflation; NAIRU; Latent Variables; Investment;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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