The Research On The China Inflation Forecast Based On Neural Network

As an early index of judging whether the economy development is stable or not, inflation rate directly or indirectly affects wages, interest rates, foreign exchange rates and other macroeconomic indicators. Since the1990s, controlling the inflation rate in a reasonable level, promoting economic steady development is gradually becoming the target center of each country’s central bank monetary policy. Under this background, as a new kind of intermediate target for monetary policy, the inflation forecast is playing a more and more important role in improving the effectiveness of monetary policy and stabilizing prices, especially in countries with inflation targeting regimes. At present, inflation forecast method is numerous but has shortcomings, therefore, it’s theoretical and practical significant to construct a new prediction method for the central bank to provide decision-making reference.This paper followed the ideas from the theory, empirical analysis to measures and suggestions.As a new prediction method, neural network, with advantages of nonlinearity, data-driving performance and stability, widely applies in foreign countries. Though domestic research is numerous, inflation forecast is less. Therefore, this paper based on the related research results at home and abroad for reference, combined the qualitative analysis and quantitative analysis together, studied the applicability of neural network in China’s inflation forecast aspects from theoretical and empirical. Firstly, this paper briefly introduced the basic theory of the neural network and inflation forecast, yet illustrated the relevant concepts. Secondly, this paper listed some work processes and characteristics of inflation forecast in some experienced countries, and contrasted deficiencies with our country. Thirdly, this paper used monthly data of influencing inflation from March2005to December2011to construct a BP neural network model for short-term forecasting. The empirical results showed that the optimized network can forecast coming CPI trend at least6months according to the existing data, and without optimization, the network also gave the reasonable forecast results. Finally, this paper put forward the following relevant policy suggestions on how to improve the central bank inflation forecast accuracy: Improving the quality of statistical data; Enhancing collecting and analyzing information ability of central bank; perfecting our country’s macroeconomic model; Dredging monetary policy transmission channels.