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A nice advantage of predictive representations of stochastic processes is that they can be expressed in terms of families of linear operators --- the "observable operators" of Jaeger (oddly, not cited in this paper; also, see Upper, and the appendix to Shalizi and Crutchfield). This paper proposes (following some earlier work) to exploit this fact, by using the instrumental variables technique from econometrics to simplify the estimation of such models. Doing so results in an estimation procedure very similar to that of Langford et al. from 2009 (reference [16] in the paper), but with some advantages in terms of avoiding iterative re-estimation. However, there seems to be an important issue which isn't (that I saw) addressed here. The instrumental variable needs to be correlated with the input variable to the regression, but independent of the noise in the regression.