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Research on the Relationship between the Supply of Money and the Consumption of Residents

摘要


In the fourth decade of reform and opening up, although the contribution rate of household consumption to economic growth has increased significantly. However, the consumption rate of residents is declining, and the money supply, as the intermediary target of monetary policy, has important reference significance for China's economic development. Therefore, this paper mainly studies the relationship between money supply and consumption level and consumption structure. This paper selects the generalized money supply as the proxy variable of money supply, the total retail sales of social consumer goods as the proxy variable of residents' consumption level, and the composition of urban residents' consumption expenditure as the proxy variable of residents' consumption structure. The empirical research in this paper mainly includes stationarity test, cointegration test, error correction model, Granger causality test, impulse response analysis and variance decomposition analysis. The main conclusions of this empirical study are: there is a long-term stable equilibrium relationship between money supply and consumption level, and the error correction model conforms to the negative correction mechanism, but the effect of reflecting the interaction between the two variables is not obvious. Granger causality test results show that money supply is the Granger cause of residents' consumption level. There is also a long-term stable equilibrium relationship between money supply and consumption structure, and the error correction model also conforms to the negative correction mechanism. However, there is no Granger relationship between money supply and consumption structure. Through the analysis of variance decomposition, it is found that the mutual contribution rate between money supply and consumption expenditure of urban residents is heterogeneous.

參考文獻


Chen Bangzhu. Comparative analysis of seasonal adjustment methods based on CPI time series [D]. Tsinghua University, 2016.
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