2 Convolution equations in classes of models with independence or conditional indepen- dence This section derives systems of convolution equations for some important classes of models. The first class of model is measurement error models with some independence (classical or Berkson error) and possibly a second mea- surement; the second class is regression models with classical or Berkson type error; the third is models with conditional independence. For the first two classes the distributional assumptions for each model and the corresponding convolution equations are summarized in tables; it is indicated which of the functions are known and which unknown; a brief discussion of each model and derivation of the convolution equations follows. The last part of this sec- tion discusses convolution equations for two specific models with conditional independence; one is a panel data model studied by Evdokimov (2011), the other a regression model where independence of measurement error of some regressors obtains conditionally on a covariate. The general assumption made here is that all the functions in the convo- lution equations belong to the space of generalized functions S $^{∗}$. Assumption 1. All the functions defined by the statistical model are in the space of generalized functions S $^{∗}$. This space of generalized function includes functions from most of the 8