A0383
Title: A small-sigma approximation for LIML and FLIML to estimation bias in the dynamic simultaneous equation model
Authors: Emma Iglesias - University of A Coruna (SPAIN) (Spain) [presenting]
Garry Phillips - University of Exeter (United Kingdom)
Abstract: Small-sigma approximations for estimator bias in the dynamic simultaneous equation model (DSEM) have previously been presented for OLS and 2SLS in the literature, where both dynamic and simultaneity bias components are present. FLILM has been shown to be useful in removing the bias of order T 1 in the static SEM (SSEM), but its performance is unknown in the DSEM. A bias approximation is provided for FLIML that shows that only a dynamic bias component exists and the simultaneity component disappears. A bias approximation is also included for LIML. It is shown that the FLIML estimator is the only one that removes the simultaneity bias component of common k-class estimators in the DSEM, which, surprisingly, all have the same dynamic component. Theoretical and simulation evidence also shows that FLIML works best overall.
