Second, we have repeated the same regressions with added sectoral dummy variable i. e. we estimated the equation
The results of the second step are shown in Table 3 (constants and coefficients at dummy varables are omitted). The low DurbinWatson statistics^{6} indicate presence of autocorrelation, and the residual variances calculated separately
for each sector (see Table 4) show a presence of heteroscedasticity 
Third, we have estimated the sectoral autoregression coefficients l_{i}^{(1)} and l_{i}^{(2)} from the second step residuals (see Table 5) and used them to transform variables according the formula:
where x_{it} stands for any of the left or right hand variables, including the constant and dummy variables. Then we have repeated the estimation of equation (ii) with
transformed variables, but only for one model in each country (see Table 6). As before, constant and parameters at dummy variables are not shown.
 
The satisfactory DurbinWatson statistics indicated that the autocorrelation was to a large extent eliminated. The check of the sectoral residual variances (see Table 7) showed that the remaining heteroscedasticity was very small, so that no further steps for eliminating it were necessary  
Before giving the economic interpretation to our estimates, let us briefly comment on their statistical properties  
With very few exceptions estimates in all three steps are very good and seem to be quite reliable. Most of the parameters were estimated with very small standard errors so that they are significantly different from zero. The main exceptions are parameters r_{t} in the first step [ except for Czechoslovakia’s model (2)] and the third step estimates of the model (2) for Bulgaria. The second step estimates show that the introduction of  
One interesting feature of our estimates is that the estimated parameters in all three steps tell basically the same story about the dynamic behavior of the economy. The least satisfactory in this sense are estimates of the capital elasticity for Bulgaria and Rumania. This can be perhaps explained by relatively large errors in measurement of capital for these two countries, as mentioned above. The fit statistics are good in all three steps, but of course, the best they are in the third step. The R^{2}s of the first step may seem to be somewhat low, but due to the large number of degrees of freedom they are highly significant as the Fstatistics on the joint explanatory power of all right hand variables indicate. Even in the case of Rumania where R^{2}s = .22 the computed Fstatistic is about 10 times larger than the tabulated value of Fdistribution for 99 per cent level of confidence and respective degrees of freedom. As could have been expected, the elimination of sectoral components from the residuals in the second step and the removal of serial correlation out of the residuals in the third step, increased substantially the R^{2}s and the Fstatistics as well as the DurbinWatson statistics. It should be, however, mentioned here that the gain of fit statistics was paid by increased multicolinearity, which was almost nonexistent in the first step. This multicollinearity together with already mentioned errors in measurement of capital may be a cause of the “strange” third step result for Bulgaria and Rumania. It is therefore questionable whether the third step estimates are better and more reliable than the first or second step estimates. With high multicollinearity the estimated parameters are not very reliable, therefore, we are inclined to trust the first and second step estimates more than the third step results. 



