Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
In conventional structural vector autoregressive models it is assumed that there are at most as many structural shocks as there are variables in the model. It is pointed out that heteroskedasticity can be used to identify more shocks than variables. Results are provided that allow a researcher to assess how many shocks can be identified from specific forms of heteroskedasticity.