Potential output and output gap model based on GRNN algorithm
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Abstract:
Based on the advantages of learning and non-parametric estimating of neural networks, we built a general regression neural network (GRNN) model to estimate the output gap and the potential output growth of China according to the data from 1978 to 2015, and predict the data from 2016 to 2020. Meanwhile, the relationship between the mentioned data and the economic growth was empirically analyzed. The model can overcome the problems of the function form determined by the presupposition in the production function method and different results coming from different functions, and the estimated output growth has a high degree of fit with the economic growth. Based on the model analysis, it is concluded that if China's economic growth rate is maintained at the level of potential output growth rate, the inflation rate will be maintained at a reasonable level, and the balance between supply and demand will be well achieved.
