MOLDOVA GDP FORECASTING USING BAYESIAN MULTIVARIATE MODELS

Ion Partachi and Simion Mija
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Ion Partachi: Academy of Economic Studies of Moldova
Simion Mija: Academy of Economic Studies of Moldova

Revista Economica, 2024, vol. 76, issue 1, 85-93

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.

Keywords: GDP Forecast; Econometric Models; Bayesian VAR (search for similar items in EconPapers)
JEL-codes: C10 C22 C38 C52 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:blg:reveco:v:76:y:2024:i:1:p:85-93

DOI: 10.56043/reveco-2024-0008

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