Automatic selection of indicators in a fully saturated regression

TY - JOUR

T1 - Automatic selection of indicators in a fully saturated regression

AU - Hendry, David F.

AU - Johansen, Søren

AU - Santos, Carlos

PY - 2008

Y1 - 2008

N2 - We consider selecting a regression model, using a variant of Gets, when there are more variables than observations, in the special case that the variables are impulse dummies (indicators) for every observation. We show that the setting is unproblematic if tackled appropriately, and obtain the finite-sample distribution of estimators of the mean and variance in a simple location-scale model under the null that no impulses matter. A Monte Carlo simulation confirms the null distribution, and shows power against an alternative of interest.

AB - We consider selecting a regression model, using a variant of Gets, when there are more variables than observations, in the special case that the variables are impulse dummies (indicators) for every observation. We show that the setting is unproblematic if tackled appropriately, and obtain the finite-sample distribution of estimators of the mean and variance in a simple location-scale model under the null that no impulses matter. A Monte Carlo simulation confirms the null distribution, and shows power against an alternative of interest.

KW - Faculty of Social Sciences

KW - regression saturation

KW - subset selection

KW - Model selection

U2 - 10.1007/s00180-007-0054-z

DO - 10.1007/s00180-007-0054-z

M3 - Journal article

SN - 0943-4062

VL - 23

SP - 317

EP - 335

JO - Computational Statistics

JF - Computational Statistics

IS - 2

ER -