7. Nonparametric regression with error in the regressor, where Berkson type instruments are assumed available. This model was proposed by Newey (2001), examined in the univarite case by Schennach (2007) and Zinde-Walsh (2009), in the multivariate case in Zinde-Walsh (2012), where the convolution equations given here in Table 2 were derived. All the models 1-7 can be extended to include some additional variables where conditionally on those variables, the functions in the model (e.g. conditional distributions) are defined and all the model assumptions hold conditionally. Evdokimov (2011) derived the conditional version of the model 4 from a very general nonparametric panel data model. Model 8 below describes the panel data set-up and how it transforms to conditional model 4 and 4a and possibly model 3 with relaxed independence condition (if the focus is on identifying the regression function). Model 8. Panel data model with conditional independence. Consider a two-period panel data model with an unknown regression func- 26 2.4 Convolution equations in models with conditional independence conditions.