A.k.a the “auxiliary method”. Also possibly a.k.a Approximate Bayesian Computation? (Should work out if that is so.)

Here be economists and ecologists.

Maybe this will solve my current weird intractable model issues?

There is an R package for at least some versions of it: pomp

Quoting Cosma:

[…] your model is too complicated for you to appeal to any of the usual estimation methods of statistics. […] there is no way to even calculate the likelihood of a given data set under parameters in closed form, which would rule out even numerical likelihood maximization, to say nothing of Bayesian methods […] Yet you can simulate; it seems like there should be some way of saying whether the simulations look like the data.

This is where indirect inference comes in […] Introduce a new model, called the “auxiliary model”, which is mis-specified and typically not even generative, but is easily fit to the data, and to the data alone. (By that last I mean that you don't have to impute values for latent variables, etc., etc., even though you might know those variables exist and are causally important.) The auxiliary model has its own parameter vector , with an estimator . These parameters describe aspects of the distribution of observables, and the idea of indirect inference is that we can estimate the generative parameters by trying to match those aspects of observations, by trying to match the auxiliary parameters.

Surely those conditions in themselves don't *necessarily* rule out Bayesian methods?
But anyway that's the drift.
I clearly have more to learn here.

Aaron King's lab at UMichigan does a lot of this, although they do even more recursive estimation, which seems less insane for my purposes.

## Refs

A.k.a the “auxiliary method”. Also possibly a.k.a Approximate Bayesian Computation? (Should work out if that is so.)

Here be economists and ecologists.

Maybe this will solve my current weird intractable model issues?

There is an R package for at least some versions of it: pomp

Quoting Cosma:

[…] your model is too complicated for you to appeal to any of the usual estimation methods of statistics. […] there is no way to even calculate the likelihood of a given data set under parameters in closed form, which would rule out even numerical likelihood maximization, to say nothing of Bayesian methods […] Yet you can simulate; it seems like there should be some way of saying whether the simulations look like the data.

This is where indirect inference comes in […] Introduce a new model, called the “auxiliary model”, which is mis-specified and typically not even generative, but is easily fit to the data, and to the data alone. (By that last I mean that you don't have to impute values for latent variables, etc., etc., even though you might know those variables exist and are causally important.) The auxiliary model has its own parameter vector , with an estimator . These parameters describe aspects of the distribution of observables, and the idea of indirect inference is that we can estimate the generative parameters by trying to match those aspects of observations, by trying to match the auxiliary parameters.

Surely those conditions in themselves don't *necessarily* rule out Bayesian methods?
But anyway that's the drift.
I clearly have more to learn here.

Aaron King's lab at UMichigan does a lot of this, although they do even more recursive estimation, which seems less insane for my purposes.

## Refs

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