This paper implements a methodology that exploits firms and households’ optimality conditions to measure money laundering for the Italian economy. This approach, first implemented by Ingram, Kocherlakota, and Savin (1997) to the household production sector, and by Busato, Chiarini and Di Maro (2006) for measuring the underground economy, allows to generate high frequency series for the money laundering using a theoretical two-sector dynamic general equilibrium model calibrated over the sample 1981:01-2001:04. The analysis of the generated series suggests two main results. First, money laundering accounts for approximately 12 percent of aggregate GDP; second, money laundering is more volatile than aggregate GDP, and it is negatively correlated with it.