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Banking and the Macroeconomy in China: A Banking Crisis Deferred?

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Abstract

The downturn in the world economy following the global banking crisis has left the Chinese economy relatively unscathed. This paper develops a model of the Chinese economy using a DSGE framework with a banking sector to shed light on this episode. It differs from other applications in the use of indirect inference procedure to test the fitted model. The model finds that the main shocks hitting China in the crisis were international and that domestic banking shocks were unimportant. However, directed bank lending and direct government spending was used to supplement monetary policy to aggressively offset shocks to demand. The model finds that government expenditure feedback reduces the frequency of a business cycle crisis but that any feedback effect on investment creates excess capacity and instability in output.

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Notes

  1. For example Mehrotra et al. (2013), Chang et al. (2013), Sun and Sen (2012) but also a number of papers by Chinese scholars published in Chinese.

  2. Estimates of average cost inefficiency of Chinese banks have been in the region of 50 %, see Fu and Heffernan (2007) and Matthews et al. (2007).

  3. The big 4 banks that constituted the SOCBs in 1997 were, Industrial and Commercial Bank of China, Bank of China, China Construction Bank and Agricultural Bank of China. By 2006 a fifth bank, Bank of Communication was added to the group.

  4. Chen et al. (2011) report that 60 % of bank loans remain at the regulated benchmark rate or below it.

  5. Chang et al. (2013) use a DSGE model to analyse optimal sterilisation policies in China which includes capital controls, an exchange rate target and stabilisation of the exchange rate.

  6. The Second National Economic Census, 2008 and Industrial Enterprises Survey Dataset, China Statistical Bureau

  7. See Shen et al. (2009)

  8. ICBC 2011 Annual Report.

  9. The term SME is used generically to mean the private sector. While there are some SMEs that are state-owned and some large enterprises (Huawei, Alibaba, Tencent, Baidu etc.) that are private, the private sector is dominated by SMEs and MSEs (Medium Sized Enterprises) and most large enterprises are part of the state sector. Many large scale joint-ventures are also with SOEs or their subsidiaries and therefore cannot strictly be called private.

  10. Although recent press reports indicate that the creation of a deposit insurance scheme could be instituted in the next phase of financial market reform. http://www.nytimes.com/2012/12/14/business/global/china-is-said-to-consider-plan-to-deal-with-failed-banks.html

  11. For example they find that credit quotas are effective in reducing inflation in the case of demand side shocks but reduces output in the case of supply side shocks.

  12. Some equations may involve calculation of expectations. The method we use here is the robust instrumental variables estimation suggested by McCallum (1976) and Wickens (1982): we set the lagged endogenous data as instruments and calculate the fitted values from a VAR(1)—this also being the auxiliary model chosen in what follows.

  13. After log-linearisation a DSGE model can usually be written in the form

    $$ A(L){y}_t=B{E}_t{y}_{t+1}+C(L){x}_t+D(L){e}_t $$
    (A1)

    where y t are p endogenous variables and x t are q exogenous variables which we assume are driven by

    $$ \varDelta {x}_t=a(L)\varDelta {x}_{t-1}+d+c(L){\varepsilon}_t. $$
    (A2)

    The exogenous variables may contain both observable and unobservable variables such as a technology shock. The disturbances e t and ε t are both iid variables with zero means. It follows that both y t and x t are non-stationary. L denotes the lag operator z t − s  = L s z t and A(L), B(L) etc. are polynomial functions with roots outside the unit circle.

    The general solution of y t is

    $$ {y}_t=G(L){y}_{t-1}+H(L){x}_t+f+M(L){e}_t+N(L){\varepsilon}_t. $$
    (A3)

    where the polynomial functions have roots outside the unit circle. As y t and x t are non-stationary, the solution has the p cointegration relations

    $$ \begin{array}{c}\hfill {y}_t={\left[I-G(1)\right]}^{-1}\left[H(1){x}_t+f\right]\hfill \\ {}\hfill =\prod {x}_t+g.\hfill \end{array} $$
    (A4)

    Hence the long-run solution to x t , namely, \( {\overline{x}}_t={\overline{x}}_t^D+{\overline{x}}_t^S \) has a deterministic trend \( {\overline{x}}_t^D={\left[1-a(1)\right]}^{-1} dt \) and a stochastic trend \( {\overline{x}}_t^S={\left[1-a(1)\right]}^{-1}c(1){\zeta}_t \).

    The solution for y t can therefore be re-written as the VECM

    $$ \begin{array}{c}\hfill \varDelta {y}_t=-\left[I-G(1)\right]\left({y}_{t-1}-\prod {x}_{t-1}\right)+P(L)\varDelta {y}_{t-1}+Q(L)\varDelta {x}_t+f+M(L){e}_t+N(L){\varepsilon}_t\hfill \\ {}\hfill =-\left[I-G(1)\right]\left({y}_{t-1}-\prod {x}_{t-1}\right)+P(L)\varDelta {y}_{t-1}+Q(L)\varDelta {x}_t+f+{\omega}_t\hfill \\ {}\hfill {\omega}_t=M(L){e}_t+N(L){e}_t\hfill \end{array} $$
    (A5)

    Hence, in general, the disturbance ω t is a mixed moving average process. This suggests that the VECM can be approximated by the VARX

    $$ \varDelta {y}_t=K\left({y}_{t-1}-\prod {x}_{t-1}\right)+R(L)\varDelta {y}_{t-1}+S(L)\varDelta {x}_t+g+{\zeta}_t $$
    (A6)

    where ζ t is an iid zero-mean process.

    As

    $$ {\overline{x}}_t={\overline{x}}_{t-1}+{\left[1-a(1)\right]}^{-1}\left[d+{\varepsilon}_t\right] $$

    the VECM can also be written as

    $$ \varDelta {y}_t=K\left[\left({y}_{t-1}-{\overline{y}}_{t-1}\right)-\prod \left({x}_{t-1}-{\overline{x}}_{t-1}\right)\right]+R(L)\varDelta {y}_{t-1}+S(L)\varDelta {x}_t+h+{\zeta}_t. $$
    (A7)

    Either equations (A6) or (A7) can act as the auxiliary model. Here we focus on (A7); this distinguishes between the effect of the trend element in x and the temporary deviation from its trend. In our models these two elements have different effects and so should be distinguished in the data to allow the greatest test discrimination.

    It is possible to estimate (A7) in one stage by OLS. Meenagh et al. (2012) do Monte Carlo experiments to check this procedure and find it to be extremely accurate.

  14. We do not attempt to match the time trends and the coefficients on non-stationary trend productivity; we assume that the model coefficients yielding these balanced growth paths and effects of trend productivity on the steady state are chosen accurately. However, we are not interested for our exercise here in any effects on the balanced growth path, as this is fixed. As for the effects of productivity shocks on the steady state we assume that any inaccuracy in this will not importantly affect the business cycle analysis we are doing here- any inaccuracy would be important in assessing the effect on the steady state which is not our focus. Thus our assessment of the model is as if we were filtering the data into stationary form by regressing it on the time trends and trend productivity.

  15. The bootstraps in our tests are all drawn as time vectors so contemporaneous correlations between the innovations are preserved.

  16. Specifically, they found on stationary data that the bias due to bootstrapping was just over 2 % at the 95 % confidence level and 0.6 % at the 99 % level. Meenagh et al. (2012) found even greater accuracy in Monte Carlo experiments on nonstationary data.

  17. We use a Simulated Annealing algorithm due to Ingber (1996). This mimics the behaviour of the steel cooling process in which steel is cooled, with a degree of reheating at randomly chosen moments in the cooling process—this ensuring that the defects are minimised globally. Similarly the algorithm searches in the chosen range and as points that improve the objective are found it also accepts points that do not improve the objective. This helps to stop the algorithm being caught in local minima. We find this algorithm improves substantially here on a standard optimisation algorithm. Our method used our standard testing method: we take a set of model parameters (excluding error processes), extract the resulting residuals from the data using the LIML method, find their implied autoregressive coefficients (AR(1) here) and then bootstrap the implied innovations with this full set of parameters to find the implied Wald value. This is then minimised by the SA algorithm.

  18. Jian et al. (2010) use a standard sticky-price DSGE, to identify the effects of oil price shocks on productivity. They confirm that oil price shocks have permanent negative effects on output.

  19. Chinese banks had only a limited exposure to the sub-prime market. The Bank of China, ICBC and China Construction Bank together held RMB11.9bn in sub-price mortgage backed securities and CDOs.

  20. For the discussion of this paragraph we used a different solution method, the Extended Path Algorithm set out in Minford et al. (1984, 1986) (it is of the same type as Fair and Taylor 1983), because the feedback creates difficulties in solving for the steady state that Dynare is not set up to handle. The EPA method generates more stable solutions and reduces the frequency of crises; hence here in the benchmark case of no feedback we define a ‘crisis’ as zero growth or less for only 1 year.

  21. The last point was made forcefully by our discussant, Haizhou Huang, of the China International Capital Corporation, at the 2013 Konstanz Seminar. He recommended starting the sample even later, at the end of the 1990s, in the grounds that only then was there a banking system worthy of the name.

References

  • Bernanke BS, Gertler M, Gilchrist S (1999) The financial accelerator in a business cycle framework. In: Taylor JB, Woodford M (eds) Handbook of macroeconomics, vol 1, ch. 21. Elsevier, pp 1341–1393

  • Canova F (2005) Methods for applied macroeconomic research. Princeton University Press, Princeton

    Google Scholar 

  • Chang C, Liu Z, Spiegel M (2013) Capital controls and optimal monetary policy. Federal Reserve Bank of San Francisco, Working Paper 2010–13

  • Chen H, Chen Q, Gerlach S (2011) The implementation of monetary policy in China. Hong Kong Institute of Monetary Research, Working Paper

  • Chen Q, Funke M, Paetz (2012) Market and non-market monetary policy tools in a calibrated DSGE model for Mainland China. Bank of Finland Discussion Paper, 16

  • Christiano LJ, Eichenbaum M, Evans CL (2005) Nominal rigidities and the dynamic effects of a shock to monetary policy. J Polit Econ 113(1):1–45

    Article  Google Scholar 

  • Coase R, Wang N (2012) How China became capitalist. Palgrave Macmillan, Basingstoke

    Google Scholar 

  • Dai L (2012) Does the DSGE model fit the Chinese economy? A Bayesian and indirect inference approach. PhD Thesis, Cardiff University

  • Del Negro M, Schorfheide F, Smets F, Wouters R (2007) On the fit of new Keynesian models. J Bus Econ Stat 25:123–143

    Article  Google Scholar 

  • Fair RC, Taylor JB (1983) Solution and maximum likelihood estimation of dynamic nonlinear rational expectations models. Econometrica 51:1169–1186

    Article  Google Scholar 

  • Fernandez-Villaverde J, Rubio-Ramirez JF (2004) Comparing dynamic equilibrium models to data. J Econ 123:153–187

    Article  Google Scholar 

  • Friedman M (1968) The role of monetary policy. Am Econ Rev 58(1):1–17

    Google Scholar 

  • Fu X, Heffernan S (2007) Cost X-efficiency in China’s banking sector. China Econ Rev 18(1):35–53

    Article  Google Scholar 

  • Gerali A, Neri S, Sessa L, Signoretti FM (2010) Credit and banking in a DSGE model of the euro area. J Money Credit Bank 42:107–141

    Article  Google Scholar 

  • Gourieroux C, Monfort A (1995) Simulation based econometric methods. CORE Lectures Series, Louvain-la-Neuve

    Google Scholar 

  • Gourieroux C, Monfort A, Renault E (1993) Indirect inference. J Appl Econ 8:S85–S118

    Article  Google Scholar 

  • Gregory A, Smith G (1991) Calibration as testing: inference in simulated macro models. J Bus Econ Stat 9:293–303

    Google Scholar 

  • Gregory A, Smith G (1993) Calibration in macroeconomics. In: Maddala G (ed) Handbook of statistics, vol 11. Elsevier, St. Louis, pp 703–719

    Google Scholar 

  • Iacoviello M (2005) House prices, borrowing constraints and monetary policy in the business cycle. Am Econ Rev 95:735–764

    Article  Google Scholar 

  • Ingber L (1996) Adaptive simulated annealing (ASA): lessons learned. Lester Ingber Papers 96as, Lester Ingber

  • Jian Z-H, Li S, Zheng J-Y (2010) Impact of oil price shocks on China’s economy: a DSGE-based analysis. 17th International Conference on Management Science & Engineering, Melbourne Australia

  • Kimball MS (1995) The quantitative analytics of the basic neomonetarist model. J Money Credit Bank 27(4):1241–1277

    Article  Google Scholar 

  • King RG, Plosser CI, Rebelo ST (1988) Production, growth and business cycles: I. The basic neoclassical model. J Monet Econ 21(2–3):195–232

    Article  Google Scholar 

  • Le VPM, Meenagh D, Minford P, Wickens M (2011) How much nominal rigidity is there in the US economy—testing a New Keynesian model using indirect inference. J Econ Dyn Control 35(12):2078–2104

    Article  Google Scholar 

  • Le VPM, Meenagh D, Minford P (2012a) What causes banking crises? An empirical investigation. Cardiff Economics Working Paper E2012/14

  • Le VPM, Meenagh D, Minford P, Wickens M (2012b) Testing DSGE models by indirect inference and other methods: some Monte Carlo experiments. Cardiff Economics Working Paper E2012/15

  • Le VPM, Meenagh D, Minford P, Ou Z (2013) What causes banking crises? An empirical investigation for the world economy. Cardiff Economics Working Paper

  • Liu L, Yuan S (2012) The financial accelerator effects on Chinese economy: a DSGE analysis. South China Journal of Economics 8:102–114 [In Chinese]

    Google Scholar 

  • Matthews K, Guo J, Zhang X (2007) Rational inefficiency and non-performing loans in Chinese Banking: a non-parameteric bootstrapping approach. China Financ Rev 1(3):1–21

    Google Scholar 

  • McCallum BT (1976) Rational expectations and the natural rate hypothesis: some consistent estimates. Econometrica 44:4–52

    Article  Google Scholar 

  • Meenagh D, Minford P, Wickens M (2012) Testing macroeconomic models by indirect inference on unfiltered data. Cardiff Economics Working Papers E2012/17

  • Mehrotra A, Nuutilainen R, Pääkkönen J (2013) Changing economic structures and impacts of shocks: evidence from a dynamic stochastic general equilibrium model for China. Pac Econ Rev 18(1):92–107

    Article  Google Scholar 

  • Minford P, Sprague A, Matthews K, Marwaha S (1984) The Liverpool macroeconomic model of the United Kingdom. Econ Model 1:24–62

    Article  Google Scholar 

  • Minford P, Agenor PR, Nowell E (1986) A new classical econometric model of the world economy. Econ Model 3(3):154–176

    Article  Google Scholar 

  • Minford P, Theordoridis K, Meenagh D (2009) Testing a model of the UK by the method of indirect inference. Open Econ Rev 20(2):265–291

    Article  Google Scholar 

  • Peng T (2012) Business cycles and macroeconomic policies in China: evidence from an estimated DSGE model. South Western University of Finance and Economics Discussion Paper

  • Qin D, Cagas M-A, Ducanes G, He X, Liu R, Liu S, Magtibay-Ramos N, Quising P (2007) A macroeconometric model of the Chinese economy. Econ Model 24:814–822

    Article  Google Scholar 

  • Shen Y, Shen M, Zhong X, Ying B (2009) Can more small and medium sized banks provide more loans to small and medium sized enterprises? Evidence from China, China Center for Economic Research, Peking University

  • Smets F, Wouters R (2003) An estimated dynamic stochastic general equilibrium model of the Euro area. J Eur Econ Assoc 1(5):1123–1175

    Article  Google Scholar 

  • Smets F, Wouters R (2007) Shocks and frictions in US business cycles: a Bayesian DSGE approach. Am Econ Rev 97:586–606

    Article  Google Scholar 

  • Smith A (1993) Estimating nonlinear time-series models using simulated vector autoregressions. J Appl Econ 8:S63–S84

    Article  Google Scholar 

  • Sun L, Sen S (2012) Monetary policy rules and business cycle in China: a Bayesian DSGE model simulation. University of Birmingham Economics Discussion Paper

  • Sun X, Zhang J, Shao Y, Yang X (2009) Chinese small-scale quarterly macroeconomic model under indirect regulation of monetary policy. 2009 International Joint Conference on Computational Sciences and Optimization, IEEE Conference Publication Vol 2

  • Wan J, Xu T (2010) Technology shocks and monetary policy in China. 2010 International Conference on E-Business and E-Government

  • Wickens MR (1982) The efficient estimation of econometric models with rational expectations. Rev Econ Stud 49:55–67

    Article  Google Scholar 

  • Xi J, He Y (2010) The welfare losses of China’s monetary policy and the selection of intermediate target: a new Keynesian DSGE model. J Financ Econ 36(2):89–98 [In Chinese]

    Google Scholar 

  • Xu W, Chen B (2009) Bank lending and economic fluctuations in China: 1993–2005. China Econ Q 8(3):969–994 [In Chinese]

    Google Scholar 

  • Yuan S, Chen P, Liu L (2011) Exchange rate system, financial accelerator and economic fluctuation. Econ Res J 1:57–70 [In Chinese]

    Google Scholar 

  • Zhang W (2009) China’s monetary policy: quantitiy versus price rules. J Macroecon 31:473–484

    Article  Google Scholar 

Download references

Acknowledgments

We thank Yi Wang for her assistance with collecting Chinese data. This paper was prepared for the 2013 Konstanz Seminar on Monetary Theory and Policy. Zhiguo Xiao acknowledges the support of the National Science Foundation of China (Grant #11001059 and 11171074). We thank, without implication Haizhou Huang and other partcipants of the 2013 Konstanz Seminar and members of the La Pour-Societe of Fudan University for helpful comments.

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Authors and Affiliations

  1. University of Sheffield, Sheffield, UK

    Vo Phuong Mai Le

  2. Cardiff Business School, Cardiff University, Colum Drive, Cardiff, CF10 3EU, UK

    Kent Matthews, David Meenagh & Patrick Minford

  3. Fudan University, Shanghai, People’s Republic of China

    Zhiguo Xiao

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  1. Vo Phuong Mai Le
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  2. Kent Matthews
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  3. David Meenagh
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  4. Patrick Minford
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  5. Zhiguo Xiao
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Correspondence to Kent Matthews.

Appendices

Appendix 1: SWUS Model Listing with Banking

Consumption Euler equation

$$ {c}_t=\frac{{\scriptscriptstyle \frac{\lambda }{\gamma }}}{1+{\scriptscriptstyle \frac{\lambda }{\gamma }}}{c}_{t-1}+\frac{1}{1+{\scriptscriptstyle \frac{\lambda }{\gamma }}}{E}_t{c}_{t+1}+\frac{\left({\sigma}_c-1\right){\scriptscriptstyle \frac{W_{\ast }{L}_{\ast }}{C_{\ast }}}}{\left(1+{\scriptscriptstyle \frac{\lambda }{\gamma }}\right){\sigma}_c}\left({l}_t-{E}_t{l}_{t+1}\right)-\left(\frac{1-{\scriptscriptstyle \frac{\lambda }{\gamma }}}{\left(1+{\scriptscriptstyle \frac{\lambda }{\gamma }}\right){\sigma}_c}\right)\left({r}_t-{E}_t{p}_{t+1}\right)+e{b}_t $$

Investment Euler equation

$$ in{n}_t=\frac{1}{1+\beta {\gamma}^{\left(1-{\sigma}_c\right)}} in{n}_{t-1}+\frac{\beta {\gamma}^{\left(1-{\sigma}_c\right)}}{1+\beta {\gamma}^{\left(1-{\sigma}_c\right)}}{E}_t in{n}_{t+1}+\frac{1}{\left(1+\beta {\gamma}^{\left(1-{\sigma}_c\right)}\right){\gamma}^2\varphi }q{q}_t+ ein{n}_t $$

Tobin Q equation

$$ q{q}_t=\frac{1-\delta }{1-\delta +{R}_{*}^K}{E}_tq{q}_{t+1}+\frac{R_{*}^K}{1-\delta +{R}_{*}^K}{E}_tr{k}_{t+1}-{E}_tc{y}_{t+1} $$

Capital Accumulation equation

$$ {k}_t=\left(\frac{1-\delta }{\gamma}\right){k}_{t-1}+\left(1-\frac{1-\delta }{\gamma}\right) in{n}_t+\left(1-\frac{1-\delta }{\gamma}\right)\left(1+\beta {\gamma}^{\left(1-{\sigma}_c\right)}\right)\left({\gamma}^2\right)\left(\varphi \right)\left( ein{n}_t\right) $$

Price Setting equation

$$ \begin{array}{c}\hfill r{k}_t={\omega}^r\left[\begin{array}{c}\hfill {\scriptscriptstyle \frac{1}{\alpha \left({\scriptscriptstyle \frac{1}{1+\beta {\gamma}^{\left(1-{\sigma}_c\right)}{\iota}_P}}\right)\left({\scriptscriptstyle \frac{\left(1-\beta {\gamma}^{\left(1-{\sigma}_c\right)}{\xi}_p\right)\left(1-{\xi}_p\right)}{\xi_p\left(\left({\varphi}_p-1\right){\varepsilon}_p+1\right)}}\right)}}\hfill \\ {}\hfill \left\{\begin{array}{c}\hfill {p}_t-{\scriptscriptstyle \frac{\beta {\gamma}^{\left(1-{\sigma}_c\right)}{\iota}_P}{1+\beta {\gamma}^{\left(1-{\sigma}_c\right)}{\iota}_P}}{E}_t{p}_{t+1}-{\scriptscriptstyle \frac{\iota_P}{1+\beta {\gamma}^{\left(1-{\sigma}_c\right)}{\iota}_P}}{p}_{t-1}+\left({\scriptscriptstyle \frac{1}{1+\beta {\gamma}^{\left(1-{\sigma}_c\right)}{\iota}_P}}\right)\hfill \\ {}\left({\scriptscriptstyle \frac{\left(1-\beta {\gamma}^{\left(1-{\sigma}_c\right)}{\xi}_p\right)\left(1-{\xi}_p\right)}{\xi_p\left(\left({\varphi}_p-1\right){\varepsilon}_p+1\right)}}\right)\left(\left(1-\alpha \right){w}_t-e{a}_t\right)-e{p}_t\hfill \end{array}\right\}\hfill \end{array}\right]\hfill \\ {}\hfill +\left(1-{\omega}^r\right)\left[\frac{e{a}_t}{\alpha }-\frac{1-\alpha }{\alpha }{w}_t\right]\hfill \end{array} $$

Wage Setting equation

$$ \begin{array}{c}\hfill {w}_t={\omega}^w\left[\begin{array}{c}\hfill {\scriptscriptstyle \frac{\beta {\gamma}^{\left(1-{\sigma}_c\right)}}{1+\beta {\gamma}^{\left(1-{\sigma}_c\right)}}}{E}_t{w}_{t+1}+{\scriptscriptstyle \frac{1}{1+\beta {\gamma}^{\left(1-{\sigma}_c\right)}}}{w}_{t-1}+{\scriptscriptstyle \frac{\beta {\gamma}^{\left(1-{\sigma}_c\right)}}{1+\beta {\gamma}^{\left(1-{\sigma}_c\right)}}}{E}_t{p}_{t+1}-{\scriptscriptstyle \frac{1+\beta {\gamma}^{\left(1-{\sigma}_c\right)}{\iota}_w}{1+\beta {\gamma}^{\left(1-{\sigma}_c\right)}}}{p}_t\hfill \\ {}\hfill +{\scriptscriptstyle \frac{\iota_w}{1+\beta {\gamma}^{\left(1-{\sigma}_c\right)}}}{p}_{t-1}-{\scriptscriptstyle \frac{1}{1+\beta {\gamma}^{\left(1-{\sigma}_c\right)}}}\left({\scriptscriptstyle \frac{\left(1-\beta {\gamma}^{\left(1-{\sigma}_c\right)}{\xi}_w\right)\left(1-{\xi}_w\right)}{\left(1+{\varepsilon}_w\left({\varphi}_w-1\right)\right){\xi}_w}}\right)\hfill \\ {}\hfill \left({w}_t-{\sigma}_l{l}_t-\left({\scriptscriptstyle \frac{1}{1-{\scriptscriptstyle \frac{\lambda }{\gamma }}}}\right)\;\left({c}_t-{\scriptscriptstyle \frac{\lambda }{\gamma }}{c}_{t-1}\right)\right)+e{w}_t\hfill \end{array}\right]+\hfill \\ {}\hfill \left(1-{\omega}^w\right)\left[{\sigma}_l{l}_t+\left({\scriptscriptstyle \frac{1}{1-{\scriptscriptstyle \frac{\lambda }{\gamma }}}}\right)\left({c}_t-{\scriptscriptstyle \frac{\lambda }{\gamma }}{c}_{t-1}\right)-\left({\pi}_t-{E}_{t-1}{\pi}_t\right)+e{w}_t^S\right]\hfill \end{array} $$

Labour demand

$$ {l}_t=-{w}_t+\left(1+\frac{1-\psi }{\psi}\right)r{k}_t+{k}_{t-1} $$

Market Clearing condition in goods market

$$ {y}_t=\frac{C}{Y}{c}_t+\frac{I}{Y} in{n}_t+{R}_{*}^K{k}_y\frac{1-\psi }{\psi }r{k}_t+{c}_y^e{c}_t^e+e{g}_t $$

Aggregate Production equation

$$ {y}_t=\phi \left[\alpha \frac{1-\psi }{\psi }r{k}_t+\alpha {k}_{t-1}+\left(1-\alpha \right){l}_t+e{a}_t\right] $$

Taylor Rule

$$ {r}_t=\rho {r}_{t-1}+\left(1-\rho \right)\left({r}_p{p}_t+{r}_y{y}_t\right)+{r}_{\varDelta y}\left({y}_t-{y}_{t-1}\right)+e{r}_t $$

Premium

$$ {E}_tc{y}_{t+1}-\left({r}_t-{E}_t{p}_{t+1}\right)=\chi \left(q{q}_t+{k}_t-{n}_t\right)+ ep{r}_t $$

Net worth

$$ {n}_t=\frac{K}{N}\left(c{y}_t-{E}_{t-1}c{y}_t\right)+{E}_{t-1}c{y}_t+\theta {n}_{t-1}+ en{w}_t $$

Entrepreneurial consumption

$$ {c}_t^e={n}_t $$

Appendix 2: VAR IRFs

In this appendix we show how the model, given that it jointly predicts (within 95 % bounds) the VAR coefficients that determine the IRFs of shocks on the three key macro variables, thereby also broadly predicts these IRFs. Because the Wald test is of the joint behaviour of the VAR coefficients and on the variances of the three variable residuals, there is not a perfect correspondence with the individual IRFs. However, it can be seen, as expected, that most of the IRFs lie mostly within the bounds.

It is the IRFs that policymakers are interested in, as pointed out by Christiano et al. (2005). They need to be assured that empirically the IRFs the model implies should appear in the data actually do so within statistical bounds (of course the IRFs implied for data behaviour reflect both the model structural IRFs and sample shock variations). Then they feel able to use the model’s (structural) IRFs to determine the effect of shocks and of policies to offset shocks.

The VAR innovations are identified throughout by the model; we have no independent way of identifying the VAR innovations (any such ways suggested are based on some ‘non-controversial’ model restrictions; however, the model here is non-controversial in its current innovation structure and so we use it.) The testing kicks in on the variances of the VAR innovations and on the lagged effects of each variable (the VAR coefficients).

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Le, V.P.M., Matthews, K., Meenagh, D. et al. Banking and the Macroeconomy in China: A Banking Crisis Deferred?. Open Econ Rev 25, 123–161 (2014). https://doi.org/10.1007/s11079-013-9301-9

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