| _version_ | 1866901209517916160 |
|---|---|
| author | Partachi, Ion Mija, Simion |
| author_facet | Partachi, Ion Mija, Simion |
| contents | <p>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.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_56043_reveco-2024-0008 |
| institution | Zenodo |
| language | |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | MOLDOVA GDP FORECASTING USING BAYESIAN MULTIVARIATE MODELS Partachi, Ion Mija, Simion <p>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.</p> |
| title | MOLDOVA GDP FORECASTING USING BAYESIAN MULTIVARIATE MODELS |
| url | https://doi.org/10.56043/reveco-2024-0008 |