dogenous variables with a mutual relationship and measurement errors from a significant amount of macroeconomic panel data, indicating to identify more robustly the structural shocks. This study will extend the estimated sample period to 2012Q2 and incorporate a stochastic volatility (hereafter SV) model with a leverage effect, which has been recently developed for measuring the volatilities of stock returns in the financial market, as by explaining the dynamics of the structural shocks in the DSGE model described above. Because we can more efficiently extract structural shocks as well as model variables by adopting the data-rich approach, we will be able to relax the specifications of the structural shocks and measure the impact of financial shocks on the real economy both during the Great Recession and after it.

We will consider four alternative cases, based on the number of observation variables (11 vs. 40 observable variables) and the specification of the volatilities of the structural shocks (constant volatility vs. time-varying volatility). We expect that by adopting forty macroeconomic time series as observable variables, data-rich information will make the decomposition between the measurement errors and the model variables from data more robust, and that relaxation of specifying the volatilities makes the rapid change of the shocks more detailed. Comparing the four cases, we suggest 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 found the contribution of the corporate net worth shock to a substantial portion of macroeconomic fluctuations after the Great Recession, which is unlike 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 still not have stopped deteriorating. Incorporating time-varying-volatilities of shocks into the DSGE model makes their credible bands narrower than half of the constant volatilities, which result implies it is a realistic assumption of the dynamics of structural shocks. It is plausible that the tiny volatilities (or the uncertainty) in ordinary times change to an extraordinary magnitude at the turning points of the business cycles. We also estimate that monetary policy shock has an opposite leverage effect of SV, which effect implies that tightening policies makes interest rates more volatile.

Finally, we will compare the achievements of our study with earlier studies from three aspects: financial frictions, the time-varying volatilities of the structural shocks, and the data-rich approach. First, there are many previous studies that have estimated a large-scale DSGE model by adopting a New Keynesian framework with the nominal rigidities proposed by Christiano, Eichenbaum and Evans (CEE)(2005), e.g., Smets and Wouters (2007). And one financial friction between the bank and the corporate sectors was developed by Bernanke et al. (1999), and then Christensen and Dib (2008) incorporated it into a CEE model. Meanwhile, Gertler and Karadi (2011) and Gertler and Kiyotaki (2010) recently proposed the other financial friction between banks and depositors. To the best of our knowledge, there is no literature combining these two frictions with a CEE model except Nishiyama et al.'s (2011), whose model we are using. Second, some research studies have allowed for a time variation in the volatility of the structural disturbances. In this respect, Justiniano and Primiceri (2008) focused on the Great Moderation using a model with structural shocks, including SV models but neither the financial frictions nor the leverage effect. And Liu et al (2011) estimated a DSGE model without financial frictions by another approach, by regime-switching the volatilities for analysing sources of the Great Moderation. And third, a few studies have dealt with the data-rich approach proposed by Boivin and Giannoni (2006), but they excluded Kryshko (2011), Nishiyama et al. (2011) and Iiboshi et al. (2012). Our study is the first attempt to combine the data-rich approach with the time-varying volatilities of structural disturbances.