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Consumer Prices Trends in Colombia: Detecting Breaks and Forecasting Infation

Author

Listed:
  • Héctor M. Zárate-Solano
  • Norberto Rodríguez-Niño

Abstract

Colombia’s annual infation reached 13.3% in March of 2023, the highest rate since the start of the infation-targeting regime for monetary policy in 2000. However, some groups in the basket show signs of lower infation, while others show higher infation. The persistence of this trend is a matter of active debate that involves analyzing the trend component of both year-to-year and month-to-month changes in the price indices. This paper employs time series models to identify infation shift levels based on the 188 price indices in the basket. We categorize trend breaks as positive or negative and further classify them into tradable versus non-tradable, core versus regulated, and other CPI categories. Using trend models that incorporate these breaks, we forecast total and group infation. Our results show that including trend breaks enhances prediction accuracy for monthly annual infation across all time horizons. **** RESUMEN: En marzo de 2023, la inflación anual en Colombia alcanzó el 13,3%, la tasa más alta desde que se implementó el régimen de inflación objetivo en el año 2000. Sin embargo, mientras algunos grupos de la canasta básica muestran signos de menor inflación, otros experimentan un aumento. La persistencia de esta tendencia es objeto de un debate activo, que ha utilizado las variaciones anuales y mensuales en los índices de precios para detectar posibles cambios en la tendencia. En este documento, empleamos modelos de series de tiempo para identificar cambios en los niveles de inflación, basándonos en los 188 índices de precios que conforman la canasta. Clasificamos las rupturas de tendencia como positivas o negativas y las agrupamos según diversas categorías, tales como transables y no transables, básicos y regulados, entre otros grupos del IPC. Adicionalmente, utilizamos estos modelos de tendencia, con posibles quiebres, para pronosticar la inflación total y la inflación por grupos. Nuestros resultados indican que incorporar los quiebres en las tendencias mejora la precisión de los pronósticos de acuerdo con las medidas de evaluación tradicionales.

Suggested Citation

  • Héctor M. Zárate-Solano & Norberto Rodríguez-Niño, 2024. "Consumer Prices Trends in Colombia: Detecting Breaks and Forecasting Infation," Borradores de Economia 1289, Banco de la Republica de Colombia.
  • Handle: RePEc:bdr:borrec:1289
    DOI: 10.32468/be.1289
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    References listed on IDEAS

    as
    1. Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
    2. Hidalgo, Javier & Seo, Myung Hwan, 2013. "Testing for structural stability in the whole sample," Journal of Econometrics, Elsevier, vol. 175(2), pages 84-93.
    3. Barbara Rossi, 2021. "Forecasting in the Presence of Instabilities: How We Know Whether Models Predict Well and How to Improve Them," Journal of Economic Literature, American Economic Association, vol. 59(4), pages 1135-1190, December.
    4. M. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2006. "Forecasting Time Series Subject to Multiple Structural Breaks," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 73(4), pages 1057-1084.
    5. Jingjing Yang, 2017. "Consistency of Trend Break Point Estimator with Underspecified Break Number," Econometrics, MDPI, vol. 5(1), pages 1-19, January.
    6. Shanika L. Wickramasuriya & George Athanasopoulos & Rob J. Hyndman, 2019. "Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 804-819, April.
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    More about this item

    Keywords

    Consumer Price Indexes; Linear Trend Models; Structural Breaks; Forecasting; Forecasting Evaluation; Índices de Precios al Consumidor; Modelos de Tendencia Lineal; Quiebres Estructurales; Pronósticos; Evaluación de pronósticos;
    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
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • 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

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