The Study On The Co-movement Mechanism Between Monetary Policy Cycles And Business Cycles

Monetary policy has always been a hotly argued topic in macroeconomics.Among all these researches, the study on the relationship between monetary policyand economic growth is most common. Different scholars choose different economicvariables to study on the effectiveness of monetary policy, but the vast majority ofthem base on vector autoregression (VAR) model or vector error correction (VEC)model. Since we consider different frequency data of variables in this paper, in orderto avoid the impact of the loss of sample information or subjective factors, we usemixed frequency vector autoregression (MF-VAR) model in the empirical part. Thuswe can deeply explore the association between monetary policy and economicgrowth.Since1996, China has experienced three downturns of business cycles, and eachoccurred after the end of tightening stages of monetary policy cycles. Based on thestudy on the effectiveness of monetary policy, we are concerned about the problem ofthe co-movement between monetary policy cycles and business cycles. This paperquantifies the association and establish the discriminant function between them. Wecan determine which economic indicator, and to what extent, could predict thedownside risk of business cycles following tightening monetary policy bydiscriminant analysis. And this year’s government work report proposed thatmonetary policy should remain moderate loose and tight, under this circumstance, theconclusion of the paper is of theoretical and practical significance.This paper uses four variables (GDP, CPI and M1growth rate, and the overnightinterbank offered rate) to construct the MF-VAR model based on Bayesian mixingfrequency (BMF) algorithm. After the model parameters were estimated, we focus onanalyzing the impulse response function to explain the correlations between the variables. In the study on the predictability of business cycles, we selected fourcategories and21sorts financial variables, each of which can reflect the state of themonetary policy. By using discriminant analysis and logistic regression model, aswell as taking the discriminant accuracy, probability of discriminant error andMcFadden R-squared into consideration, we can select the most effective variablesand combined variables.The empirical results show that the effects that money supply and interest ratehave on economic growth are obvious, and the former is longer and more stable.Loose monetary policy would push up the inflation rate and in turn promote theeconomic growth. There are four indictors of the highest accuracy in predicting thebusiness cycles, that is, overnight ibor, M1growth rate, growth rate of RMB loans offinancial institutions and semi-annual deposit rate. The indicator D, a constructedindicator of the first three ones, can attain a discriminant accuracy of100%. We findthat D can not only predict the condition of the following business cycle, but alsoanticipate the GDP trend. So it can serve as a reference for the formulation ofmonetary policy. In the analysis of the discriminant of the recent indicators’ data andthe latest economic data, we conceive that the downward pressure on business cyclesshould not be overlooked at least from the monetary policy perspective. So themonetary authority should use appropriate monetary policy to adjust themacroeconomy.