Bayesian estimation of DSGE models: Identification using a diagnostic indicator

Koop et al. (2013) suggest a simple diagnostic indicator for the Bayesian estimation of the parameters of a DSGE model. They show that, if a parameter is well identified, the precision of the posterior should improve as the (artificial) data size T increases, and the indicator checks the speed at which precision improves. As it does not require any additional programming, a researcher just needs to generate artificial data and estimate the model with increasing sample size, T. We apply this indicator to the benchmark Smets and Wouters (2007) DSGE model of the US economy, and suggest how to implement this indicator on DSGE models.