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A new and useful parameter estimating method for dynamic econometric model and an novel forecasting method are proposed in this paper. These methods could deal with the fitting and forecasting of economy dynamic model and could greatly decrease the forecasting errors result from the singularity of the real data. Moreover, the strict hypothetical conditions in least squares method can be released in the method presented in this paper, which overcome the shortcomings of least squares method and expanded the application of data barycentre method. The new methods are applied to Chinese steel consumption forecasting based on the historic data. It is shown that the result of fitting and forecasting was satisfactory. From the comparison between the new forecasting method and the least squares method, we conclude that the fitting and forecasting results using data barycentre method are more stable than that using least squares regression forecasting method, and the computation of data barycentre forecasting method is simpler than that of least squares method. As a result, the data barycentre method is convenient to use in technical economy.