Forecasting the production side of GDP

We evaluate the forecasting performance of time series models for the production side of gross domestic product (GDP)—that is, for the sectoral real value‐added series summing up to aggregate output. We focus on two strategies to model a large number of interdependent time series simultaneously: a Bayesian vector autoregressive model (BVAR) and a factor model structure; and compare them to simple aggregate and disaggregate benchmarks. We evaluate point and density forecasts for aggregate GDP and the cross‐sectional distribution of sectoral real value‐added growth in the euro area and Switzerland. We find that the factor model structure outperforms the benchmarks in most tests, and in many cases also the BVAR. An analysis of the covariance matrix of the sectoral forecast errors suggests that the superiority can be traced back to the ability to capture sectoral comovement more accurately.(This abstract was borrowed from another version of this item.)