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En busca de un buen marco de referencia predictivo para la inflación en Chile


  • Pincheira, Pablo

    (Banco Central de Chile)

  • García, Álvaro

    (Universidad de California, Los Ángeles)


In this article we analyze the accuracy and stability of short-run inflation forecasts for Chile coming from Extended Seasonal Arima (ESARIMA) models. We compare ESARIMA forecasts to those coming from surveys and traditional time series bench- marks available in the literature. Our results show that ESARIMA based forecasts display lower out-of-sample Mean Squared Prediction Error than forecasts coming from traditional benchmarks when the predictive horizon ranges from 1 to 4 months. At longer horizons, the worst models from the ESARIMA family are outperformed by the best univariate traditional benchmarks. We obtain opposite results when compar-ing ESARIMA outcomes to survey-based forecasts: the survey provides more accurate forecasts at every single horizon. Our results are, in general, statistically significant at usual confidence levels. We also notice that ESARIMA forecasts are more stable than traditional time series methods but less stable than survey-based forecasts.// En este artículo investigamos la precisión y estabilidad de las proyecciones de corto plazo de la inflación en Chile provenientes de una determinada subfamilia extendida de modelos SARIMA que denominamos ESARIMA. Las proyecciones ESARIMA son comparadas con las provenientes de encuestas y de simples modelos univariados, incluyendo algunos que han sido tradicionalmente utilizados como marcos de referencia predictivos en la bibliografía. Nuestros resultados indican que el error cuadrático medio fuera de muestra de las proyecciones ESARIMA es menor que el de los métodos univariados considerados, cuando el horizonte predictivo varía de 1 a 4 meses. En horizontes superiores, los peores representantes de nuestra familia ESARIMA comienzan a ser superados por los mejores marcos de referencia univariados. Al comparar con la encuesta de expectativas económicas, los resultados van en la dirección opuesta: la encuesta es más precisa que la subfamilia ESARIMA en todos los horizontes. En general nuestros resultados son estadísticamente significativos a niveles de confianza usuales. Observamos también que la familia ESARIMA ofrece proyecciones más estables que los marco de referencia univariados, pero menos estables que las provenientes de la encuesta de analistas.

Suggested Citation

  • Pincheira, Pablo & García, Álvaro, 2012. "En busca de un buen marco de referencia predictivo para la inflación en Chile," El Trimestre Económico, Fondo de Cultura Económica, vol. 0(313), pages 85-123, enero-mar.
  • Handle: RePEc:elt:journl:v:79:y:2012:i:313:p:85-123

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    Cited by:

    1. Pablo Pincheira & Andrés Gatty, 2016. "Forecasting Chilean inflation with international factors," Empirical Economics, Springer, vol. 51(3), pages 981-1010, November.
    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. Pablo Pincheira, 2012. "Are Forecast Combinations Efficient?," Working Papers Central Bank of Chile 661, Central Bank of Chile.
    4. 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.
    5. Pablo Pincheira, 2012. "A Joint Test of Superior Predictive Ability for Chilean Inflation Forecasts," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 15(3), pages 04-39, December.
    6. 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. 17(1), pages 66-87, April.
    7. Pablo Pincheira & Carlos Medel, 2012. "Forecasting Inflation With a Random Walk," Working Papers Central Bank of Chile 669, Central Bank of Chile.

    More about this item


    Predicción de inflación; encuesta de expectativas; predicción fuera de muestra; evaluación predictiva; estacionalidad multiplicativa;

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation


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