A new and useful parameter estimating method for econometric dynamic model is proposed in this paper. Moreover, a new forecasting method is also proposed in this paper based on it. 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 were not necessary 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 new forecasting method and least squares method, we could conclude that the fitting and forecasting results using data barycentre method was more stable than that using least squares regression forecasting method, and the computation of data barycentre forecasting method was simpler than that of least squares method. As a result, the data barycentre method was convenient to use in technical economy.
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