Robust Econometrics

Robust Econometrics

-V-

P. Clzek and W. Hardle*

Econometrics often deals with data under, from the statistical point of
view, non-standard conditions such as heteroscedasticity or measurement
errors and the estimation methods need thus be either adopted to such con-
ditions or be at least insensitive to them. The methods insensitive to vi-
olation of certain assumptions, for example insensitive to the presence of
heteroscedasticity, are in a broad sense referred to as robust (e.g., to het-
eroscedasticity). On the other hand, there is also a more specific meaning
of the word ‘robust’, which stems from the field of robust statistics. This
latter notion defines robustness rigorously in terms of behavior of an esti-
mator both at the assumed (parametric) model and in its neighborhood in
the space of probability distributions. Even though the methods of robust
statistics have been used only in the simplest setting such as estimation of
location, scale, or linear regression for a long time, they motivated a range
of new econometric methods recently, which we focus on in this chapter.

* This work was supported by the Deutsche Forschungsgemeinschaft through the SFB
649 “Economic Risk”.

<<   1   2   3   4   5   >>

More intriguing information