Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
This paper compares the forecast precision of the Functional Signal plus Noise (FSN), the Dynamic Nelson–Siegel (DL), and a random walk model. The empirical results suggest that both outperform the random walk at short horizons (one-month) and that the FSN model outperforms the DL at the one- and three-months forecasting horizon. The conclusions provided in this paper are important for policy makers, fixed income portfolio managers, financial institutions and academics.