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Forecasting Inflation with a Simple and Accurate Benchmark: a Cross-Country Analysis

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  • Pablo Pincheira
  • Carlos A. Medel

Abstract

We explore the ability of several univariate models to predict inflation in a number of countries and at several forecasting horizons. We place special attention on forecasts coming from a family of ten seasonal models that we call the Driftless Extended Seasonal ARIMA (DESARIMA) family. Using out-of-sample Root Mean Squared Prediction Errors (RMSPE) we compare the forecasting accuracy of the DESARIMA models with that of traditional univariate time-series benchmarks available in the literature. Our results show that DESARIMA-based forecasts display lower RMSPE at short horizons for every single country, except one. We obtain mixed results at longer horizons. Roughly speaking, in half of the countries, DESARIMA-based forecasts outperform the benchmarks at long horizons. Remarkably, the forecasting accuracy of our DESARIMA models is surprisingly high in stable inflation countries, for which the RMSPE is barely higher than 100 basis points when the prediction is made 24- and even 36-months ahead.

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Bibliographic Info

Paper provided by Central Bank of Chile in its series Working Papers Central Bank of Chile with number 677.

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Date of creation: Aug 2012
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Handle: RePEc:chb:bcchwp:677

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  1. Groen, Jan J.J. & Kapetanios, George & Price, Simon, 2009. "A real time evaluation of Bank of England forecasts of inflation and growth," International Journal of Forecasting, Elsevier, vol. 25(1), pages 74-80.
  2. Pincheira, Pablo, 2013. "A Bunch of Models, a Bunch of Nulls and Inference about Predictive Ability," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 26-43, October.
  3. Giacomini, Raffaella & White, Halbert, 2003. "Tests of Conditional Predictive Ability," University of California at San Diego, Economics Working Paper Series qt5jk0j5jh, Department of Economics, UC San Diego.
  4. Andersson, Michael K. & Karlsson, Gustav & Svensson, Josef, 2007. "The Riksbank’s Forecasting Performance," Working Paper Series 218, Sveriges Riksbank (Central Bank of Sweden).
  5. Graham Elliott & Allan Timmermann, 2008. "Economic Forecasting," Journal of Economic Literature, American Economic Association, vol. 46(1), pages 3-56, March.
  6. Capistrán, Carlos & Constandse, Christian & Ramos-Francia, Manuel, 2010. "Multi-horizon inflation forecasts using disaggregated data," Economic Modelling, Elsevier, vol. 27(3), pages 666-677, May.
  7. Pablo Pincheira, 2012. "Are Forecast Combinations Efficient?," Working Papers Central Bank of Chile 661, Central Bank of Chile.
  8. Pablo Pincheira & Carlos Medel, 2012. "Forecasting Inflation With a Random Walk," Working Papers Central Bank of Chile 669, Central Bank of Chile.
  9. Pablo Pincheira & Roberto Álvarez, 2012. "Evaluation of Short Run Inflation Forecasts in Chile," Working Papers Central Bank of Chile 674, Central Bank of Chile.
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Cited by:
  1. Pablo Pincheira & Carlos Medel, 2012. "Forecasting Inflation With a Random Walk," Working Papers Central Bank of Chile 669, Central Bank of Chile.
  2. Pablo Pincheira B., 2014. "Predictive Evaluation of Sectoral and Total Employment Based on Entrepreneurial Confidence Indicators," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 12(1), pages 66-87, April.

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  1. Econometría Aplicada II

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