| Abstract: |
In this paper we show analytically, with simulation experiments and with
actual data that a mismatch between the time scale of a DSGE model and that of
the time series data used for its estimation generally creates identfication
problems, introduces estimation bias and distorts the results of policy
analysis. On the constructive side, we prove that the use of mixed frequency
data, combined with a proper estimation approach, can alleviate the temporal
aggregation bias, mitigate the identfication issues, and yield more reliable
policy conclusions. The problems and possible remedy are illustrated in the
context of standard structural monetary policy models. |