### 5.2 Calibrations and Priors of Parameters

We calibrated the subset of the structural parameters in the model that are not identifiable (i.e., the parameters that are only used to pin down the steady states) or that are difficult to identify from the observed data. Calibrated parameters with their descriptions are reported in Table 2. We assumed the discount factor was $ \beta = 0.995 $ so as to make the steady state, real interest rate to be 2% (annual rate). We assumed the profit margin of the retailers to be 10% in the steady state and, thus, set the elasticity of substitution of intermediate goods as $ \epsilon = 11 $ . Having no reliable information regarding the new entry rate of entrepreneurs (i.e., $ \xi^{E} $ ), we simply set it as equal to the calibration for a new banker's entry rate, as did Gertler and Kiyotaki (2011). The rest of the calibrated parameter values were borrowed from Smets and Wouters (2003), Christensen and Dib (2008), and Gertler and Kiyotaki (2011).

## [Insert Table 2 and 3 around here]

Most of the steady states were pinned down by equilibrium conditions of the model, but some others need to be calibrated. For the steady-state value of the external finance premium, we followed the calibration of Christensen and Dib (2008). For the steady state corporate borrowing rate (real, quarterly rate), we calculated the historical average of the yields of Moody's Baa-rated corporate bonds and set it as the steady-state rate. In the same way, we calculated the historical average of the non-farm, non-financial business leverage ratio based on Flow of Funds and set it as the steady state of the corporate leverage ratio. Finally, the government expenditure-to-output ratio in the steady state is set to be 0.2 because of being borrowed from Gertler and Kiyotaki's (2011) calibration.

Next, we turn to describe the prior distribution of interest as a preamble for the Bayesian estimation. The settings of priors are reported in Table 3. We set $ \varphi = 0.05 $ for the prior mean of this parameter, which controls the sensitivity of the external finance premium with respect to the corporate leverage ratio, following the calibration of BGG. For the AR(1) persistence parameters for the structural shocks, we set the prior mean equal to 0.5 for all of them. For standard errors of the structural shocks, we set the prior mean equal to 1% for each standard error, except for the monetary policy shock (where a change of policy rate for more than 25 basis point is rare). By the same token, we set the prior mean equal to 1% for most of the measurement errors, except for the data related to interest rates.

### 5.3 Data Description

For adopting the data-rich approach, a relatively large and quarterly panel dataset with as many as 40 observable variables was used, as described in detail in Data Appendix (Table A1). The sample period of the estimation was between 1985Q2 and 2012Q2, because we wanted to avoid the effect on the estimation result from the instability of the monetary policy regime changes, especially around the end of the 1970s and the early 1980s; i.e., pre- and post-regimes, by Volcker and Greenspan, (See Clarida et al. 2000, Lubik and Schorfheide 2004, and Boivin 2005) and from the structural change of the Great Moderation, which began in mid-1980s (See Bernanke 2004, Stock and Watson 2002, Kim and Nelson 1999, and McConnell and Perez-Quiros 2000). And another reason the sample period was determined as it was had to do with the availability of financial data, because charge-off rates for banks are available only from 1985Q1.

In Cases A and C, we looked at the following eleven series: (1) output, $ y_{t} $ , (2) consumption, $ c_{t} $ , (3) investment, $ i_{t}^{k} $ , (4) inflation, $ \pi_{t} $ , (5) real wage, $ w_{t} $ , (6) labour input, $ l_{t} $ , (7) the nominal interest rate, $ R_{t} $ , (8) the nominal corporate borrowing rate, $ R_{t}^{E} $ , (9) the external finance premium, $ s_{t} $ , (10) the corporate leverage ratio, $ q_{t}k_{t}/n_{t}^{E} $ , and (11) the bank leverage.