We review recent findings in the application of indirect inference to DSGE models. We show that researchers should tailor the power of their test to the model under investigation in order to achieve a balance between high power and finding a robust model; this will involve choosing only a limited number of variables on whose behaviour they should focus. Also recent work reveals that it makes little difference which these variables are or how their behaviour is measured whether via a VAR, IRFs or moments. We also review identification issues, how to test part of a model and whether alternative evaluation methods such as forecasting or likelihood ratio tests are potentially helpful.