Abstract
AbstractThe activity of the Shadow Banks in China has been the subject of considerable interest in recent years. Total shadow banking lending has reached over 60% of GDP and has grown faster than regular bank lending. It has been argued that unregulated shadow banking has fuelled a credit boom that poses a risk to the stability of the financial system. This paper estimates a model of the Chinese economy using a DSGE framework that accommodates a banking sector that isolates the effects of lending to the private sector including shadow bank lending. A refinement of the model allows for bank lending including lending by the shadow banks to affect the credit premium on private investment. The main finding is that while financial shocks are significant, it is real shocks that dominate. The model is used to simulate the frequency of growth slowdowns in China and concludes that these are more likely to be driven by real sector shocks rather than financial sector, including shadow bank shocks. This paper differs from other applications in its use of indirect inference to test the fitted model against a three‐equation VAR of inflation, output gap and interest rate.
Highlights
Modelling the Chinese macroeconomy using the DSGE framework has become vogue.1 But there has been little effort to model the Chinese business cycle with a banking sector that interacts with shadow banking
We take the model to the data and estimate the parameters using the method of indirect inference (II)
The method of indirect inference was used to estimate the model which was used to carry out an accounting exercise in the shocks causing the growth slowdown in the global financial crisis (GFC) episode
Summary
Modelling the Chinese macroeconomy using the DSGE framework has become vogue. But there has been little effort to model the Chinese business cycle with a banking sector that interacts with shadow banking. There has been little effort to model the Chinese business cycle with a banking sector that interacts with shadow banking. A notable exception is Funke, Mihaylovski, and Zhu (2015) who develop a detailed calibrated DSGE model with a shadow banking sector that incorporates some Chinese economy features. This paper builds on this work by adding a fuller monetary sector, the quantity of money and bank credit, and interaction with a shadow banking sector. The basic idea is that the monetary base acts as collateral for loans because it is entirely liquid and riskless. It is a powerful agent of credit growth in a way that has hitherto been relatively neglected in DSGE models
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