that the approach is a tool with potential for specifying a theoretical model coherent with broad information formalized from large data set if we customize it by adding prior information for the data set. We see a mismatch between the new Keynesian model and data in term of the fluctuation in real wages, which implies that new theory concerning additional friction in the labor market will be needed to explain this.

The remainder of this paper is organized as follows. Section 2 addresses the framework and methodology of the data-rich approach using a DSGE model. Section 3 describes the Smets and Wouters-type new Keynesian DSGE model. Section 4 explains Bayesian estimation methods in a data-rich approach. Section 5 explains the preliminary settings and data description. We present the estimation results in Section 6. Section 7 concludes. Appendix A1 and A2 describe the algorithm of the MCMC estimation method presented in Section 4 in more detail. Appendix A3 explains the methods of policy simulation analyzed in Section 6.

## 2 The data-rich DSGE approach

Estimation methods for DFMs are rapidly being developed and applied in many fields of macroeconomics and finance. One type of DFM applied is the data-rich DSGE model. For example, recently, Schorfheide et al. (2010) published an empirical study applying a data-rich DSGE approach to forecasting economic fluctuations. In this section, we explain the data-rich estimation method following Boivin and Giannoni (2006) and Kryshko (2011), and describe how to apply this data-rich estimation method to our DSGE model. The idea of data-rich estimation is to extract the common factor from a large panel of macroeconomic and financial time series data and to match the state variable of the model to the extracted common factor. A virtue of this approach is that even if the definition or the concept of a state variable and observed data are slightly detached from each other, one can estimate the model by matching state variables to the common factors extracted from the large panel data (i.e., the data-rich environment), and one can expect improved efficiency in estimating the parameters and structural shocks of the model. We first explain DFMs and then turn to the data-rich DSGE model.

### 2.1 The Dynamic Factor Model (DFM)

DFMs, which are statistical models for estimating the common factors of business cycles, were proposed by Sargent and Sims (1977) and empirically applied by Stock and Watson (1989), who extracted one unobserved common factor of business fluctuation from many macroeconomic time series using a Kalman filter. Stock and Watson (2002a, 2002b) also developed approximate DFMs using principal components analysis, extracting several common factors from more than 100 macroeconomic time series and verifying that these factors included useful information on forecasting of macroeconomic time series. Nowadays, there are many theoretical and empirical studies of DFMs in the literature, including those of Boivin and Ng (2005, 2006), and Stock and Watson (2002a, 2002b, 2005). Kose et al. (2003) tried to extract common factors of world-wide and regional business cycles using a Kalman filter and a DFM. Stock and Watson (2006, 2010) provide a survey of