Z: Understanding Unbiased Estimators in Regression Analysis

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first of all...what is an unbiased estimator??
how to check whether a reggression provide an unbiased estimator?

thanks!
 
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The following three events can cause biased estimators:

1) Omitted variable bias.
2) cov(error,regressors) [itex]\not=[/itex] 0
3) cov(regressor1, regressor2) [itex]\not=[/itex] 0
4) Model mis-specification (eg not including a squared term when you should - do a RAMSEY RESET test).
 
An unbiased estimator is a sample function:
[tex] Z_n = f(X_1, \ldots, X_n)[/tex]
such that, for an i.i.d. sample with a parameter of the distribution θ that we are trying to estimate, has the property:
[tex] \mathrm{E}\left[Z_n \right] = \theta[/tex]
If this does not hold for a finite n, but is true as [itex]n \rightarrow \infty[/itex], then we say that the estimator is asymptotically unbiased.

In general, if the function f is some non-polynomial function, it is very hard to check the bias of the estimator. If, on the other hand, the estimator is a (symmetric) polynomial of degree p (pth moment), we may use some rules for the expectation values. For example, the mean:
[tex] \bar{X}_n \equiv \frac{1}{n} \, \sum_{k = 1}^{n}{X_k}[/tex]
has the property:
[tex] \mathrm{E} \left[\bar{X}_n \right] = \frac{1}{n} \, \sum_{k = 1}^{n}{\mathrm{E} \left[ X_k \right]} = E \left[ X \right][/tex]
is the unbiased estimator of the mathematical expectation of the random variable X.
 
can i say if a regression with very low r^2 it doesn't provide unbiased estimator?
 
can i say if a regression with very low r^2 it doesn't provide unbiased estimator?