worth shock), a data-rich estimation with stochastic volatility may be more reliable than an unenriched Bayesian estimation method.

Third, another important observation from the historical decomposition results is the behaviour of the bank’s net-worth shock. The bank’s net-worth shock declined sharply during the Great Recession and was the main source of the sharp decline in output and investment, as shown in Figures 4 and 5. But then, right after the Great Recession period, the bank’s net-worth shock quickly reversed its direction and contributed positively to output and investment. Considering the timing of this reversal, it is quite possible that the implementation of the TARP is behind this reversal. In other words, the implementation of TARP may have successfully countered the bank’s negative net-worth shock. Interpreting further, considering the positive contribution of the bank’s net-worth shock to the output and to the investment right after the Great Recession period, the implementation of TARP may be one of the major reasons behind the stopping of the Great Recession and contributing to the recovery (albeit weak) of the U.S. economy in recent years.

## 7 Conclusion

According to the NBER, the Great Recession, in which the financial crisis played a significant role in the failure of key businesses, declined consumer wealth—estimated in trillions of US dollars—and caused a downturn in economic activity leading to the 2008–2012 global recession and contributing to the European sovereign-debt crisis. This recession is reported to have begun in December 2007 and to have ended in June 2009. The purpose of this study is to analyze mutual relationship among macroeconomic and financial endogenous variables in terms of business cycles and to identify what structural exogenous shocks contributed to the Great Recession in light of a DSGE model. Because we obtained a broad consensus that solvency and liquidity problems of the financial institutions are chief among the fundamental factors causing the recession itself, it is plausible to embed financial frictions in both the banking and the corporate sectors of a New Keynesian DSGE model. To this end, we followed Nishiyama et al. (2011) who already studied the US economy using a New Keynesian DSGE model with these two financial frictions in a data-rich environment. In this model, with asymmetric information about borrowers and lenders, banks have two roles, generating two agency costs: one is as the lenders to the corporate sector and the other is as the borrowers from the depositors. Furthermore, the structural shocks in the model are assumed to possess SV with a leverage effect. Then, we estimated the model using the data-rich estimation method and utilized up to 40 macroeconomic time series in the estimation. Our study is the first attempt to combine the data-rich approach with the time-varying volatilities of structural disturbances.

We considered four alternative cases based on the number of observation variables (11 vs. 40 variables) and the specification of the volatilities of the structural shocks (constant volatility vs. time-varying-volatility). Comparing these four cases, we suggested the following three empirical evidences in the Great Recession: (1) the negative bank net worth shock gradually spread before the corporate net worth shock burst, (2) the data-rich approach and the structural shocks with SV evaluated the contribution of the corporate net worth shock to a substantial portion of the macroeconomic fluctuations after the Great Recession, in contrast to a standard DSGE model, and (3) the Troubled Asset Relief Program (TARP) would work to bail out financial institutions, whereas balance sheets in the corporate sector could not yet have stopped deteriorating.

Incorporating time-varying volatilities of the shocks into the DSGE model made their credible bands narrower than half of the constant volatilities, implying that this is a realistic assumption of dynamics of the structural shocks. It is plausible that the tiny volatilities