**Michael
Creel**

michael.creel @ uab.cat

Tel. +34 93 581 1696

Office
B3-1104

Departament d'Economia i d'Història Econòmica

*Papers*
*and
code* *and
CV*

Information
about published papers is here

Computer
code is at https://github.com/mcreel

**Teaching**

Econometrics
notes Graduate level notes for econometrics, with numerous
examples using the Julia language
embedded as links.

*Research*

Fall
2023: Constructing
Efficient Simulated Moments Using Temporal Convolutional Networks
(with
Jonathan Chassot). This paper continues work with simulated moments
estimation using neural nets. In this case, the net, which uses the
TCN architecture, takes the entire sample as the input, instead of
statistics. Thus, no information is lost due to selection of
statistics. We show by example that MSM estimation using the output
of the net as the statistics for indirect inference can equal or beat
maximum likelihood estimation, in terms of RMSE, for several test
models, when the sample size is not too large. The methods are
applied to a jump diffusion model for S&P500 data. One result is
that measurement error is clearly present.

29
Sept.
2021:
“Inference
Using Simulated Neural Moments”. This short paper looks at how
estimators that simulate from a model and base estimation on
statistics (moments) can benefit from filtering the statistics
through a trained neural net to improve reliability of inferences.
This works pretty well, I am pleased
to
say. There are 4 example models, including a small DSGE model, and
there is an empirical application of a jump-diffusion model for S&P
500 returns. See the code archive https://github.com/mcreel/SNM.
This paper continues the project started in my paper “Neural
Nets for Indirect Inference”.