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Forecasting Inflation in Argentina: A Probabilistic Approach

Author

Listed:
  • Tomás Marinozzi

    (University of CEMA (UCEMA))

Abstract

Probability forecasts are gaining popularity in the macroeconomic discipline as point forecasts lack the ability to capture the level of uncertainty in fundamental variables like inflation, growth, exchange rate, or unemployment. This paper explores the use of probability forecasts to predict inflation in Argentina. Scoring rules are used to evaluate several autoregressive models relative to a benchmark. Results show that parsimonious univariate models have a relatively similar performance to that of the multivariate models around central scenarios but fail to capture tail risks, particularly at longer horizons.

Suggested Citation

  • Tomás Marinozzi, 2023. "Forecasting Inflation in Argentina: A Probabilistic Approach," Ensayos Económicos, Central Bank of Argentina, Economic Research Department, vol. 1(81), pages 81-110, May.
    • Handle: RePEc:bcr:ensayo:v:1:y:2023:i:81:p:81-110
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    References listed on IDEAS

    as
    1. Svensson, Lars E. O., 2000. "Open-economy inflation targeting," Journal of International Economics, Elsevier, vol. 50(1), pages 155-183, February.
    2. Anthony Garratt & Kevin Lee & M. Hashem Pesaran & Yongcheol Shin, 2003. "A Long run structural macroeconometric model of the UK," Economic Journal, Royal Economic Society, vol. 113(487), pages 412-455, April.
    3. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    4. Garratt A. & Lee K. & Pesaran M.H. & Shin Y., 2003. "Forecast Uncertainties in Macroeconomic Modeling: An Application to the U.K. Economy," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 829-838, January.
    5. Francis X. Diebold & Todd A. Gunther & Anthony S. Tay, "undated". "Evaluating Density Forecasts," CARESS Working Papres 97-18, University of Pennsylvania Center for Analytic Research and Economics in the Social Sciences.
    6. James H. Stock & Mark W. Watson, 2008. "Phillips curve inflation forecasts," Conference Series ; [Proceedings], Federal Reserve Bank of Boston.
    7. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    8. Gary Koop & Dimitris Korobilis, 2012. "Forecasting Inflation Using Dynamic Model Averaging," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(3), pages 867-886, August.
    9. Adam J. Check & Anna K Nolan & Tyler C. Schipper, 2019. "Forecasting GDP Growth using Disaggregated GDP Revisions," Economics Bulletin, AccessEcon, vol. 39(4), pages 2580-2588.
    10. Malte Knüppel, 2015. "Evaluating the Calibration of Multi-Step-Ahead Density Forecasts Using Raw Moments," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(2), pages 270-281, April.
    11. Raffaella Giacomini & Barbara Rossi, 2010. "Forecast comparisons in unstable environments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 595-620.
    12. James E. Matheson & Robert L. Winkler, 1976. "Scoring Rules for Continuous Probability Distributions," Management Science, INFORMS, vol. 22(10), pages 1087-1096, June.
    13. Francis X. Diebold & Minchul Shin, 2017. "Assessing point forecast accuracy by stochastic error distance," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 588-598, October.
    14. Koop, Gary & Korobilis, Dimitris, 2011. "UK macroeconomic forecasting with many predictors: Which models forecast best and when do they do so?," Economic Modelling, Elsevier, vol. 28(5), pages 2307-2318, September.
    15. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    16. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    17. Chen, Pu & Schneider, Elena & Frohn, Joachim, 2008. "A Long-Run Structural Macroeconometric Model for Germany: An Empirical Note," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 2, pages 1-12.
    18. Rossi, Barbara & Sekhposyan, Tatevik, 2019. "Alternative tests for correct specification of conditional predictive densities," Journal of Econometrics, Elsevier, vol. 208(2), pages 638-657.
    19. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    20. Dean Croushore & Simon van Norden, 2018. "Fiscal Forecasts at the FOMC: Evidence from the Greenbooks," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 933-945, December.
    21. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    More about this item

    Keywords

    continuous ranked probability scores; inflation forecast; probability forecast;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • 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

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