overcome the drawbacks of DFMs, Boivin and Giannoni (2006) and Kryshko (2011) reconstructed a DFM using structural means, i.e., combining a DSGE model with a DFM to comprise a data-rich DSGE model, in which the model variables are regarded as common factors derived from large data sets.

According to Boivin and Giannoni (2006), there are three benefits of a data-rich DSGE model. First, as a consequence of the DFM, the latent model variables (common factors) and measurement errors (idiosyncratic components) can consistently be identified from the cross-section of macroeconomic indicators, and not exclusively from the dynamic structure implied by the DSGE model. Using multiple indicators in the model estimation makes it possible to consider a potentially large number of imperfectly measured model variables without restricting the number of structural shocks that can be identified within the model. Rather than taking a stance on whether measurement errors or structural shocks are part of the DSGE model, an agnostic position can be adopted and their relative importance determined by empirical means.

Second, we can exploit the information from indicators that are not directly or unambiguously linked to a specific model concept, such as output, consumption and inflation. If the additional information considered is relevant, it should make the estimation more efficient. This is particularly important to determine more accurately the state of the economy and helps in forecasting.

Third, a data-rich DSGE model can be interpreted as a DFM in which the full structure of the DSGE model is imposed on the transition equation of the latent common factors. This has the added benefit of allowing very clear economic interpretation of all estimated latent common factors, i.e., the factors correspond to the model variables in the DSGE model. In contrast, in standard DFMs, the latent factors do not have a clear interpretation as they are identified only “up to a rotation.”

Two other relevant studies in particular have employed a data-rich DSGE approach. Matsumae et al. (2011) point out that a DSGE model should be adopted to account for measurement errors for any observable variables in terms of model selection using a Bayesian factor. Iiboshi (2012) proposes a new method of measuring the effect of monetary policy on a large number of macroeconomic series by combining a DSGE model with a DFM in a data-rich environment, including a broad range of information useful both to central banks and the private sector. This method can resolve two problems inherent in the factor-augmented vector autoregressive (FAVAR) approach proposed by Bernanke et al. (2005), and can also enjoy its benefits.

The purpose of this study is to verify whether we also observe the evidence pointed out by Boivin and Giannoni (2006) in the Japanese economy, and to examine the means and extent of the influence of estimates of parameters and results calibrated to Japanese economy by exploiting their latent common factors from information derived from a large panel of data series, used as the DSGE model variables. To this end, following Boivin and Giannoni (2006), we also adopt the new Keynesian DSGE model proposed by Smets and Wouters (2003, 2007) and examine three cases of 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. We compare this with 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