### 6.2 Implications of estimates of structural parameters

In Table 5, we obtained the estimates of structural parameters the values of which are reflected by variations in the estimated model concepts in Section 6.1. First, we focus on the parameters of household consumption and labor, such as $ \theta $ , $ \sigma_{c} $ and $ \sigma_{L} $ . As can be seen from Table 5(a), (b) and (c), $ \theta $ and $ \sigma_{c} $ in Cases A and B are similar to those in Case SW, a regular DSGE model, but those in Case C change significantly, demonstrating greater swings of the concept “consumption” than in the other cases. That is, habit formation $ \theta $ increases from around 0.54 to 0.69, and the inverse of elasticity of inter-temporal substitutions, $ \sigma_{c} $ , shrinks from almost 1.5 to 0.94 (more flexible between substitutions of present and future consumption). In contrast to consumption, labor supply elasticity becomes more rigid so that the inverse of labor supply elasticity $ \sigma_{L} $ increases from 0.57 to 1.04 from Case A to Case B (Table 5(a) and (b)). In Case C, $ \sigma_{L} $ returns to 0.59 (Table 5(c)) as labor input fluctuates more actively according to the estimated concept “labor” in Fig. 1. This implies that households are likely to decide that the quantity of their labor supply is more elastic depending on the real wage compared to Case SW.

## [Insert Table 5 about here]

Next, we consider the discrepancy in estimates of rigidity of price and wages in goods and labor markets in each case. The Calvo price, $ \xi_{p} $ , which represents a no-update frequency for price, is 0.77 in Case SW and levels out between 0.81 and 0.84 in Cases A, B and C. In contrast, the price index, $ \gamma_{p} $ , reflecting the degree of present inflation to past inflation, is 0.30 in Case SW, and in a data-rich approach it gradually decreases from 0.82 (Case A) to 0.51 (Case C), becoming more flexible toward Case C, which reflects the estimation of inflation in Fig. 1. On the other hand, the Calvo wage, $ \xi_{w} $ , with a no frequency no-update of wages, is 0.36 (Case SW) and more rigid (nearly 0.68) in Cases A, B and C. The wage index, $ \gamma_{w} $ , increases from 0.36 (Case SW) to around 0.69 in all data-rich cases. The changes in $ \xi_{w}, \gamma_{w} $ and $ \sigma_{L} $ cause the labor supply curve to be more elastic with respect to wages.

### 6.3 Implications of policy evaluations

For analyzing policy evaluations using the DSGE model, we usually employ tools such as impulse response functions, variance decomposition and historical decomposition, the derivations of which are explained in Appendix A3. Before considering the empirical results of the policy evaluations, we start to compare the structural shocks in the three cases, which are thought to be factors of monetary policy and business cycles. By introducing a data-rich approach – primarily via three structural shocks, namely productivity, labor supply, and equity premium shocks – we make considerable changes. As shown in Table 5, the standard deviation of productivity shock becomes 10%, 50%, and 90% in Cases A, B and C, respectively, compared to Case SW. The time series of the shock is displayed in Fig. 2. As described in the next subsection (6.4), output is dominated primarily by investment and preference shocks, so that a productivity shock does not contribute to variation in output in Cases A and B. Turning to labor supply shock, in Fig. 2, we see considerable differences between Cases A and B, especially in the size of the labor supply shock. This size grows further in Case C with the enrichment of the volume