Then the model maps into the model 3 with the functions in the convolution equations representing conditional densities and allows to identify distribu- tion of x ∗ (function m in the model). But the conditional distribution of the second-period error in this set-up is not identified. Evdokimov introduced parametric AR(1) or MA(1) dependence in the errors U and to accommodate that extended the model to three periods. Here this would lead in the AR case to the equations in (3) . Model 9. Errors in variables regression with classical measurement error conditionally on covariates. The panel model can be considered with relaxed independence assump- tions. Here in the two-period model we look at forms of dependence that assume zero conditional mean of the second period error, rather than full independence of the first period error: The convolution equations in 4a are similar to Evdokimov; they allow for equations for f$_{u}$, f$_{u}$$_{x}$ that do not rely on f$_{x}$ ∗ . The advantage of those lies in the possibility of identifying the conditional error distributions without placing the usual non-zero restrictions on the characteristic function of x ∗ (that represents the function m for the panel model). 28 f$_{Ui}$$_{1}$ |$_{X}$$_{i}$$_{1}$$_{,α}$$_{i}$$_{,X}$$_{i}$$_{2}$$_{,Ui}$$_{2}$ ( u$_{t}$ | x, ... ) = f$_{Ui}$$_{1}$ |$_{Xi}$$_{1}$ ( u$_{t}$ | x ); E ( U$_{i}$$_{2}$ | X$_{i}$$_{1}$, α$_{i}$, X$_{i}$$_{2}$, U$_{i}$$_{1}$ ) = 0; f$_{Ui}$$_{2}$ |$_{α}$$_{i}$$_{,X}$$_{i}$$_{2}$$_{=}$$_{X}$$_{i}$$_{1}$$_{=}$$_{x}$ ( u$_{t}$ | x, ... ) = f$_{Ui}$$_{2}$ |$_{Xi}$$_{2}$ ( u$_{t}$ | x ) .