Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We produce predictions of Norwegian GDP. To this end, we estimate a Bayesian dynamic factor model on a panel of fourteen variables (all followed closely by market operators) ranging from 1990 to 2011. By means of a pseudo real-time exercise, we show that the Bayesian dynamic factor model performs well both in terms of point forecast and in terms of density forecasts. Results indicate that our model outperforms standard univariate benchmark models, that it performs as well as the Bloomberg survey, and that it outperforms the predictions published by the Norges Bank in its Monetary Policy Report.