I am currently working on a linear DSGE model with matching frictions in the labor market. The aim is to compare the second moments of this model (and particularly the standard deviations and autocorrelations) with those of the data.
A standard assumption in the literature is to assume that employment is predetermined. I therefore calibrate the model such that one period corresponds to one month. However, some data are only available quarterly (real GDP, productivity…).
My question is therfore the following: how could I reconcile the moments of the model (which are monthly) with those of the data (which are quarterly) ?
Thanks a lot by advance.
All the best.
Calibrate your model at monthly frequency and then aggregate the monthly data to quarterly data, which you can compare to the empirical data. An example of this can be found in Born/Pfeifer (2014): “Risk Matters: A comment” at aeaweb.org/articles.php?doi=10.1257/aer.104.12.4231
Thank you Johannes.
Is it possible to manage the change in periodicity within Dynare and therefore obtain theoretical moments (but in this case what would be the required command) ? Or should I have to use Matlab to perform simulations and then obtain empirical moments ?
Thanks by advance.
You cannot do this automatically in Dynare, because the state space transformation required to generate theoretical moments for aggregate data is not supported. You need to perform simulations.