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I am using a simple ACD (autoregressive conditional duration) model with expoential or Burr distributed residuals and 1 lag, i.e. ACD(1,1).

I am modelling durations for transactions data on a 'medium' liquidity market.

I obtain a correct fit, but not very good out-of-sample results. Does anyone have experience with handling this model on real data? and using it for out-of-sample prediction.

I am interested by practitioner's ressource: code, tutorial, example, notebooks, etc. or direct feedback.

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I have tested several Multiplicative Errors Models, including the standard ACD model. I used these models to forecast the bid ask spread ( non-negative data) and I obtain interesting results. As a main information I have found that the choice of the error term distribution is pivotal with the Burr distribution being by far the best (tested: Exp, Weibull, Generalized Gamma). Also I have found that long memory models (FIACD by Jasiak (1998), LMACD by Karanasos (2003)) have better out-of-sample density forecast accuracy than other MEM models. So I recommend you to test these latter models. Finally I have compared those models with ARMA ones and found that MEM models should be preferable for tails forecasts.

I plan to release freely the code (in Ox) that I made to estimate those models, I still need to do a minimal help documentation. I'll edit this answer when I'll do it.

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