Create a free account
Join Calaméo to publish and share documents with the world!
Rate and comment your favorite publications, download documents and share your readings with your friends.
Omitted Variable Bias
5 pages
Published by
Ram Singh
Omitted Variable Bias
In statistics, omitted-variable bias (OVB) occurs when a model is created which incorrectly
leaves out one or more important causal factors.
The bias is created when the model
compensates for the missing factor...
[More]
Omitted Variable Bias
In statistics, omitted-variable bias (OVB) occurs when a model is created which incorrectly
leaves out one or more important causal factors.
The bias is created when the model
compensates for the missing factor by over- or under-estimating one of the other factors.
More specifically, OVB is the bias that appears in the estimates of parameters in a regression
analysis, when the assumed specification is incorrect, in that it omits an independent variable
(possibly non-delineated) that should be in the model.
Effects on Ordinary Least Square
Gauss–Markov theorem states that regression models which fulfill the classical linear
regression model assumptions provide the best, linear and unbiased estimators.
With respect
to ordinary least squares, the relevant assumption of the classical linear regression model is
that the error term is uncorrelated with the regressors.
The presence of omitted variable bias violates this particular assumption.
The violation causes
[Less]
Tags
analysis,
bias,
biased,
covariance,
data,
estimates,
estimator,
factor,
linear,
model,
occurs,
omitted,
regression,
statistics,
tutorvista,
variable,
variables
