Updating of Input-Output tables in Russia by machine learning methods

Input-output tables are the basis for many types of analysis of the real sector, which are necessary to build a well-thought-out long-term and short-term policy. Evaluation of input-output tables is an expensive and time-consuming procedure. At the same time, national statistical agencies publish additional forecast information, which makes it possible to extend the input-output tables, for example, output and intermediate consumption by sector. The main methods of extending the RAS tables (or its modification GRAS) and Cross Entropy, use data on intermediate demand, the calculation of which requires additional time-consuming work. The use of information only for the previous period and the current period is the main disadvantage of this method. In recent decades, machine learning methods have been gaining popularity, the main advantage of which is finding relationships that can be hard to identify, for example, due to the large dimension of the task or the lack of evidence of cause-and-effect relationships. These methods have proven themselves well in all kinds of image recognition tasks, voice-to-text conversion, and so on. Currently, attempts are being made to apply machine learning methods to economic problems. The application of machine learning methods to the task of updating input-output tables carries a scientific novelty. The purpose of the study is to extend the input-output tables by machine learning methods. The method of extending the input-output tables using convolutional neural networks is the result of the work, as well as the forecast of the coefficients of the direct cost matrix for Russia. Conclusion: the use of input-output tables can improve the quality of forecasts of input-output tables. Recommendations: it is necessary to continue the research in this direction