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Moldova Gdp Forecasting Using Bayesian Multivariate Models

Author

Listed:
  • PARTACHI Ion

    (Academy of Economic Studies of Moldova)

  • MIJA Simion

    (Academy of Economic Studies of Moldova)

Abstract

Building a multivariate GDP forecasting model based on relevant macroeconomic indicators selected through a proper selection process. This paper assesses whether alternative specifications of the Bayesian model can provide higher forecast accuracy compared to a standard VECM (Vector Error Correction Model). To achieve this, a Bayesian VAR (Vector Autoregressive) model is estimated using the Litterman precedent (1979). Compare the result based on the Bayesian VAR (Vector Autoregressive) model with the DFM (Dynamic Factor Model). The out-of-sample forecast performance of the models is then evaluated over a 5-year period (20 quarters), where model efficiency for a long forecast period is ascertained.

Suggested Citation

  • PARTACHI Ion & MIJA Simion, 2024. "Moldova Gdp Forecasting Using Bayesian Multivariate Models," Revista Economica, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 76(1), pages 85-93, March.
    • Handle: RePEc:blg:reveco:v:76:y:2024:i:1:p:85-93
    DOI: 10.56043/reveco-2024-0008
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    References listed on IDEAS

    as
    1. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    2. Sims, Christopher A & Zha, Tao, 1998. "Bayesian Methods for Dynamic Multivariate Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 949-968, November.
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    More about this item

    Keywords

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    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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