Estimates of parameters from cross section data are often introduced into time series regression as known with certainty, which leads to conditional estimates of the time series regression. This paper develops a method of pooling cross section and time series data from the Bayesian point of view, to estimate all the parameters simultaneously. It is shown that the parameters which are common to both the regressions will have on the average sharper posterior distributions. It is also demonstrated that the traditional method often leads to underestimates of the standard errors of the time series estimates. The method is applied to estimate a statistical demand function for the U.S. based on cross section and time series data given in Tobin [18].