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Aggregate Inflation Forecast with Bayesian Vector Autoregressive Models

Author

Listed:
  • Cesar Carrera

    (Banco Central de Reserva del Perú)

  • Alan Ledesma

    (UC Santa Cruz)

Abstract

We forecast 18 groups of individual components of the Consumer Price Index (CPI) using a large Bayesian vector autoregressive model (BVAR) and then aggregate those forecasts in order to obtain a headline inflation forecast (bottom-up approach). De Mol et al. (2006) and Banbura et al. (2010) show that BVAR's forecasts can be significantly improved by the appropriate selection of the shrinkage hyperparameter. We follow Banbura et al. (2010)’s strategy of “mixed priors," estimate the shrinkage parameter, and forecast inflation. Our findings suggest that this strategy for modeling outperform the benchmark random walk as well as other strategies for forecasting inflation.

Suggested Citation

  • Cesar Carrera & Alan Ledesma, 2015. "Aggregate Inflation Forecast with Bayesian Vector Autoregressive Models," Working Papers 50, Peruvian Economic Association.
    • Handle: RePEc:apc:wpaper:2015-050
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    References listed on IDEAS

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    Cited by:

    1. César Carrera & Jairo Flores, 2017. "Modelling and forecasting money demand: divide and conquer," Working Papers 91, Peruvian Economic Association.

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    More about this item

    Keywords

    Inflation forecasting; aggregate forecast; Bayesian VAR;
    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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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