Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
No-arbitrage macro-finance models use variance decompositions to gauge the extent of association between the macro variables and yields. We show that results generated by this approach are sensitive to the order of variables in the recursive identification scheme. In a four-factor model, one may obtain 18 different sets of answers out of 24 possible. We propose an alternative measure that is based on levels of macro variables as opposed to shocks. We account for the correlation between the macro and latent factors via projection of the latter onto the former. As a result, the association between macro variables and yields can be computed uniquely via an R2. Macro variables explain 80% of the variation in the short rate and 50% of the slope, and 54% to 68% of the term premia.