A Large-Scale Bayesian VAR for Disaggregated GDP Components

Thomas M. Menino Convention & Exhibition Center 

We introduce a large scale Bayesian VAR for monthly indicators of disaggregated GDP
components and estimate it using Bayesian methods. By linking monthly indicators with relevant
GDP subcomponents and aggregating them following the national accounting convention,
we generate GDP forecasts as well as a few key macroeconomic variables. By accommodating
the heterogeneous release schedule of monthly indicators, our model can sequentially update
GDP forecasts at higher (daily or weekly) frequency than feasible based on only aggregate
variables. Our model's nowcasts are comparable to the widely used reference GDP tracking
model for most GDP components. The model's predictive density of GDP growth across data
vintages suggests that surprises in macroeconomic indicators can generate frequent changes
in downside tail risk, which has often attributed to changes in financial market conditions.
The departure from normality is pronounced when the predictive density is tilted to match the
previous quarter's headline GDP growth.

Large-Scale Bayesian VAR

Disaggregated GDP Components

Density Forecasting