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Forecasting Inflation in Argentina: Individual Models or Forecast Pooling?

  • 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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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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  1. Marcellino, Massimiliano, 2002. "Forecast Pooling for Short Time Series of Macroeconomic Variables," CEPR Discussion Papers 3313, C.E.P.R. Discussion Papers.
  2. Gali, Jordi & Gertler, Mark, 1999. "Inflation dynamics: A structural econometric analysis," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 195-222, October.
  3. Clements, Michael P. & Hendry, David F., 2006. "Forecasting with Breaks," Handbook of Economic Forecasting, Elsevier.
  4. 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.
  5. Andrew Atkeson & Lee E. Ohanian., 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Win, pages 2-11.
  6. Laura D´Amato & Lorena Garegnani & Juan M. Sotes, 2008. "Inflation Persistence and Changes in the Monetary Regime: The Argentine Case," Ensayos Económicos, Central Bank of Argentina, Economic Research Department, vol. 1(50), pages 127-167, January -.
  7. David Hendry & Michael Clements, 2001. "Pooling of Forecasts," Economics Series Working Papers 2002-W09, University of Oxford, Department of Economics.
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