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Forecasting Inflation in Argentina: Individual Models or Forecast Pooling?


Author Info

  • Laura D´Amato

    (Central Bank of Argentina)

  • Lorena Garegnani

    (Central Bank of Argentina)

  • Emilio Blanco

    (Central Bank of Argentina)


Inflation forecasting plays a central role in monetary policy formulation. At the same time, recent international empirical evidence suggests that with the decline in inflation of recent years, the joint dynamics of this variable and its potential predictors has changed and inflation has become more unpredictable. Using a univariate model as a benchmark, we evaluate the predictive capacity of certain causal models linked to di¤erent inflation theories, such as the Phillips Curve and a monetary VAR. We also analyze the predictive power of models that use factors that combine the overall variability of a large number of business cycle time series as predictors. We compare their relative performance using a set of parametric and non-parametric tests proposed by Diebold and Mariano (1995). Although the univariate model performs best, as the forecast horizon lengthens, multivariate models performance improves. In particular, a monetary VAR performs better than the univariate ARMA model in the case of a one-year horizon. Nevertheless, when tests are calculated to evaluate the statistical significance of di¤erences in the predictive capacity of models, taking a univariate ARMA model as a benchmark, diferences are not statistically significant. Finally, estimated models are pooled to forecast inflation. Some of the forecast combinations outperform the best individual forecast over a one-year horizon. Taking into account that a one year-horizon is relevant for economic policy decisions, the possibility of combining both univariate and multivariate models for forecasting purpose is interesting, because it it can also be helpful to answer specific economic policy questions.

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Bibliographic Info

Paper provided by Central Bank of Argentina, Economic Research Department in its series BCRA Working Paper Series with number 200835.

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Length: 29 pages
Date of creation: Jul 2008
Date of revision:
Handle: RePEc:bcr:wpaper:200835

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Keywords: Argentina; inflation forecast; multivariate models; pooling; univariate models;

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  1. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
  2. Laura D´Amato & Lorena Garegnani & Juan M. Sotes Paladino, 2007. "Inflation Persistence and Changes in the Monetary Regime: The Argentine Case," BCRA Working Paper Series 200723, Central Bank of Argentina, Economic Research Department.
  3. Jordi Galí & Mark Gertler, 1998. "Inflation dynamics: A structural econometric analysis," Economics Working Papers 341, Department of Economics and Business, Universitat Pompeu Fabra.
  4. Clements, Michael P. & Hendry, David F., 2006. "Forecasting with Breaks," Handbook of Economic Forecasting, Elsevier.
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Cited by:
  1. Laura D’Amato & Lorena Garegnani & Emilio Blanco, 2011. "Using the Flow of High Frequency Information for Short Term Forecasting of Economic Activity in Argentina," Ensayos Económicos, Central Bank of Argentina, Economic Research Department, vol. 1(64), pages 7-33, October -.


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