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Forecasting Chilean Inflation with International Factors

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  • Pablo Pincheira
  • Andrés Gatty

Abstract

In this paper we build forecasts for Chilean year-on-year inflation using simple time-series models augmented with different measures of international inflation. Broadly speaking, we construct two families of international inflation factors. The first family is built using year-on-year inflation of 18 Latin American (LA) countries (excluding Chile). The second family is built using year-on-year inflation of 30 OECD countries (excluding Chile). We show sound in-sample and pseudo out-ofsample evidence indicating that these international factors do help forecast Chilean inflation at several horizons. Incorporating the international factors reduce the Root Mean Squared Prediction Error of pure univariate SARIMA models statistically speaking. We also show that the predictive pass-through from international to local inflation has increased in the recent years. As a final exercise we construct another international inflation factor as an average of the inflation of fifteen countries from which Chile gets a high percentage of its imports. With the aid of this factor the models outperform our univariate benchmarks but also underperform the results obtained with the broader factors built with LA or OECD countries, suggesting that imported inflation is not the only channel explaining our findings.

Suggested Citation

  • Pablo Pincheira & Andrés Gatty, 2014. "Forecasting Chilean Inflation with International Factors," Working Papers Central Bank of Chile 723, Central Bank of Chile.
  • Handle: RePEc:chb:bcchwp:723
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Duncan, Roberto & Martínez-García, Enrique, 2019. "New perspectives on forecasting inflation in emerging market economies: An empirical assessment," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1008-1031.
    2. Pincheira, Pablo M. & West, Kenneth D., 2016. "A comparison of some out-of-sample tests of predictability in iterated multi-step-ahead forecasts," Research in Economics, Elsevier, vol. 70(2), pages 304-319.
    3. Pincheira, Pablo, 2017. "A Power Booster Factor for Out-of-Sample Tests of Predictability," MPRA Paper 77027, University Library of Munich, Germany.
    4. Jose Luis Nolazco & Pablo Pincheira & Jorge Selaive, 2016. "The evasive predictive ability of core inflation," Working Papers 15/34, BBVA Bank, Economic Research Department.
    5. Pablo Pincheira & Andrés Gatty, 2016. "Forecasting Chilean inflation with international factors," Empirical Economics, Springer, vol. 51(3), pages 981-1010, November.
    6. Pincheira, Pablo & Hernández, Ana María, 2019. "Forecasting Unemployment Rates with International Factors," MPRA Paper 97855, University Library of Munich, Germany.
    7. Carlos A. Medel & Michael Pedersen & Pablo M. Pincheira, 2016. "The Elusive Predictive Ability of Global Inflation," International Finance, Wiley Blackwell, vol. 19(2), pages 120-146, June.
    8. Felipe Leal & Carlos Molina & Eduardo Zilberman, 2020. "Proyección de la Inflación en Chile con Métodos de Machine Learning," Working Papers Central Bank of Chile 860, Central Bank of Chile.
    9. David Coble & Pablo Pincheira, 2021. "Forecasting building permits with Google Trends," Empirical Economics, Springer, vol. 61(6), pages 3315-3345, December.
    10. Chris Heaton & Natalia Ponomareva & Qin Zhang, 2020. "Forecasting models for the Chinese macroeconomy: the simpler the better?," Empirical Economics, Springer, vol. 58(1), pages 139-167, January.
    11. Pincheira, Pablo & Hardy, Nicolas, 2022. "Correlation Based Tests of Predictability," MPRA Paper 112014, University Library of Munich, Germany.

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    JEL classification:

    • 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
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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