does it reduce the uncertainty of structural shocks, as accuracy and uncertainty depend on the degree of correlation between the data indicators specifying the model concepts. However, this caution does not mean that in estimating a DSGE model it is enough to use a small data set; rather, it means that a data-rich approach should not be used injudiciously by blindly expanding data. To allow the approach to work effectively, future studies will need to consider two aspects in particular, i.e., which data indicators are selected as sensor and information series, and what weights are configured among the data to distinguish between concepts and errors. In other words, they will need to establish what kind of prior information is configured to identify each measurement error. However, the evidence also suggests that our approach is potentially a strong tool for making the theoretical model coherent with the broad information formalized from a large data set. In fact, we found that a discrepancy in the movement of the real wage between the new Keynesian model and the actual data – which implies a new theory dealing with additional friction in the labor market – would be needed to explain this.

## 7 Conclusion

The DSGE model is becoming the most influential tool for analyzing macroeconomics and policy evaluations. In addition, developing methods to specify a DSGE model, to source business cycles empirically, and to identify structural shocks are important topics in advanced macroeconometrics. On the other hand, the importance of adopting existing statistical approaches for macroeconomic analysis have not fallen into disuse because there are many panel data sets that cannot be incorporated into a DSGE model. In estimating a DSGE model, researchers have so far assumed that all relevant information for estimation is adequately summarized by a small number of data sets. Central banks and financial market participants monitor and analyze literally hundreds of data sets. Nowadays, DFMs – widely applied by empirical research studies in macroeconomics – show that there may be a large set of primary macroeconomic variables needed to capture economic dynamics adequately. This model exploits common factors, which are key factors in the fluctuation of primary macroeconomic series from large panel data sets. Boivin and Giannoni (2006) and Kryshko (2011) reconstructed a DFM in a structural manner, i.e., combining a DSGE model with a DFM as a data-rich DSGE model, in which the model variables used in the DGSE model are regarded as common factors derived from large data sets.

The purpose of our study is to verify whether we observe evidence pointed out by Boivin and Giannoni (2006) in the Japanese economy, and to examine how and to what extent the estimation of parameters and calibration influences the Japanese economy by exploiting its latent common factors from information gleaned from large panels of data series as the DSGE model variables. To this end, following Boivin and Giannoni (2006), we also adopt a Smets and Wouters (2003, 2007) type of DSGE model and examine three cases using the data-rich approach, depending on the number of data indicators and on whether each indicator is linked to a specific model concept or not, compared to a regular DSGE model adopted as a reference model. The difference between our study and that of Boivin and Giannoni (2006) is that we replace price and wage markup shocks with measurement errors.