and wage markup shocks with measurement errors as Smets and Wouters (2003, 2007) configured these markup shocks as ad hoc shocks, which have no role in providing a theoretical explanation of nominal prices and real wages. Previous studies employing similar means to our study are those of Justiniano and Primiceri (2008) and Fueki et al. (2010).

Following Boivin and Giannoni (2006) and Kryshko (2011), our estimation involves the Markov chain Monte Carlo (MCMC) technique, which efficiently resolves the problem of large dimensions for parameters caused by dealing with complicated non-linear models, such as a data-rich DSGE model. In contrast, direct estimation by maximum likelihood is usually not feasible in practice. Unlike these previous works, by adopting the simulation smoother developed by de Jong and Shephard (1995), we successfully estimate latent model variables (common factors) and latent exogenous shocks in an efficient manner, representing a methodological contribution.

By implementing the Bayesian MCMC method, we estimate Japan’s economy between 1981Q1 and 1995Q4 in terms of a data-rich environment with 55 macroeconomic indicators, and calculate posterior means and credible intervals of (1) model variables, (2) structural shocks and (3) impulse response functions of monetary policy to all model variables, and (4) posterior means of variance decomposition and historical decomposition of data series, as well as the posterior distribution of the structural parameters of the Smets and Wouters-type new Keynesian DSGE model.

Boivin and Giannoni (2006) report three important findings as a result of estimating a data-rich model. First, by considering information from a larger data set and by relaxing the link between some indicators and model concepts, they are able to improve considerably the estimates of the model concepts and structural shocks. Second, the estimates of model parameters and estimated variances of structural shocks differ significantly depending on the assumed link between theory and data. This implies that different estimates also result in very different conclusions regarding the source of economic fluctuations. Third, as more data series are used in the estimation, they find that fewer shocks are necessary to explain business cycles, and that shocks to the efficiency of investment goods become a main source of economic fluctuations.

Based on our validation of the model for Japanese data, we agree with Boivin and Giannoni's (2006) second finding, but disagree with parts of their other findings. In relation to their first finding, we note that enriching information by increasing the volume of data does not necessarily improve the accuracy of discrimination between the model variables (common factors) and measurement errors (idiosyncratic components), nor does it reduce the uncertainty of structural shocks, as the accuracy and uncertainty depend on the degree of correlation between the data indicators linked to specific model concepts. This evidence suggests the need for future research to resolve what data indicators are to be selected and what kind of prior information is configured to identify each measurement error in the data. In terms of their third finding, we observe that the influence of investment shock is relatively weaker; rather, labor supply shock is a stronger source of economic fluctuations as more data series are used in the estimation. Thus, we conclude that the functional capability of the data-rich approach is more restrictive than the benefits advocated by Boivin and Giannoni (2006). However, our estimation also suggests