What is the difference between bias and inconsistency? I am trying to learn about bias in simple linear regression. Specifically, I want to see what happens when the $cov(e,x) = 0$ assumption of the simple regression is violated.
If this assumption is violated, I arrive at
\begin{equation}
\hat{\beta}_1 \rightarrow \beta_1 + \frac{{cov(e,x)}}{{var(x)}}.
\end{equation}
This derivation is from this web page (equations 1 through 6). The web page says

If cov(e,x) =\= 0, the OLS estimator is inconsistent, i.e. its value does not converge to the true value
  of the parameter with the sample size. Moreover, the OLS estimator is biased.

To me, it is clear that $\hat{\beta}_1$ converges to a value that is not the true value $\beta_1$, so that makes it biased. However, the web page seems to conclude that this makes it inconsistent. Somehow, they conclude that the estimator is biased, but I am not sure (they simply use "moreover").
So here are my questions:


*

*What is the difference between bias and inconsistency in this case? (When they conclude that it is inconsistent, I conclude that it is biased.)

*Does it ever make sense to say that $\beta_1$ is biased? Or, can only an estimator $\hat{\beta_1}$ be biased?

*If the $cov(e,x) = 0$ assumption is violated, how can I find out what happens to the variance of $\hat{\beta}_1$? Can I tell if it increases or decreases?


EDIT To clarify question 3, I am wondering if there is a proof/argument for: 

When the second assumption ($cov(e,x) = 0$) of Ordinary Least Squares is violated, the variance of $\hat{\beta_1}$ changes.

The only answer I can think of is using the result from omitted-variable bias. That is, comparing $var(\hat{\beta_1})$ and $var(\tilde{\beta_1})$ using the equations
\begin{align}
var(\hat{\beta_1}) = \sigma^2/[SST_1(1-R_1^2)]
\end{align}
\begin{align}
var(\tilde{\beta_1})  = \sigma^2/SST_1.
\end{align}
The full argument for the omitted-variable case comes from Wooldridge's text.
Since having an omitted variable is sufficient to violate $cov(e,x) = 0$, is the argument given by Wooldridge sufficient to prove that the variance is less than it would be if $cov(e,x) = 0$ held true?
(If my understanding is correct, I think that $\tilde{\beta_1}$ is the assumption-violating case. $\hat{\beta_1}$ is the 'true' case.)
 A: I make some additional assumptions and simplify notations, nothing of which should cause confusion. Suppose for simplicity that data is generated according to $Y = \beta X + \epsilon$, where all variables are $\mathbb R-$valued and $\epsilon$ has zero mean and variance $\sigma^2$. Assume $X$ has the necessary moments. We have $n$ independent copies of the pair $(Y, X)$; $x = [x_1, \dots, x_n]'$ and $y = [y_1,\dots, y_n]'$. 
The OLS estimator of $\beta$ is 
\begin{align}
\hat{\beta} &=y'x / x'x \\
&= (x\beta + e)'x/x'x \\
& = \beta + \frac{e'x}{x'x} \\
&= \beta + \frac{\frac{1}{n}\sum_i \epsilon_i x_i}{\frac{1}{n}\sum_i x_i^2}
\end{align}
where $e = [\epsilon_1, \dots, \epsilon_n]'$. The assertion that this approaches $\beta + {\rm Cov}(X, \epsilon) / {\rm Var}(X)$ as $n\to\infty$ is usually in the sense of convergence in probability. According to standard definitions, an estimator is consistent if it converges in probability to the true parameter value, i.e. in this case if $\hat{\beta} \to \beta$ in probability. Here, we had an extra term, in general non-zero, on the right hand side so the estimator is inconsistent.
On the other hand, we say that $\hat{\beta}$ is unbiased if $\mathbb E \hat{\beta} = \beta$. This statement has nothing to do with convergence. All the same, the expectation of the right hand side is again $\beta$ + some term which is not zero in general. Thus, $\hat{\beta}$ is also biased. This answers the first question.
Regarding the second question, notice that bias is usually regarded as a property of estimators, and $\beta$ is unknown so it's not an estimator. Therefore, it does not make sense to speak of the bias of $\beta$. If we are liberal in the usage of bias and let it apply to anything, we see that $\mathbb E \beta = \beta$ so it would be "unbiased".
Without further assumptions on the dependence between $X$ and $\epsilon$ there really isn't any way to tell what the answer to three is, I believe. I should say I did not have time to go through the calculations so I may be wrong.
In light of the discussion under the other answers: If one declares a new definition of consistency in terms of the variance approaching zero, I don't see how inconsistency under the assumption ${\rm Cov}(X, \epsilon) \neq 0$ can be either confirmed or disproved without more assumptions. It's also important to note that consistency in the standard definition says nothing about decreasing variance or decreasing bias. These are separate concepts and should not be mixed up.
A: *

*Here is a definition quoted from page 30 of this PDF written by
the same author (Rubaszek's homepage) of your link's PDF:

"Consistent estimators: for N -> infinity, the variance converges to
  0"

Consistency appears to be about the variance (of the estimator).
Bias appears to be about the value (of the estimator).
I can imagine an estimation procedure where the variance (of the
estimator) converges to 0 even though the value (of the estimator)
does not converge to its population value, and vice versa.

*Theoretically, there are estimation procedures that produce
estimators whose variance oscillates forever as N -> infinity.

*A population parameter such as $\beta_1$ is taken to be the true
value. It cannot be biased. Technically speaking, what is biased is the estimation procedure itself, not the value (such as $\hat{\beta_1}$) that it produces.

A: When the asymptotic distribution of $\hat \beta_1$ becomes concentrated over the true value of $\beta_1$, it is said to be consistent. This concentration means that in the limit (as the sample size grows), our estimator has zero bias and zero variance as the sampling distribution collapses around the true value. This means that consistency can be loosely thought of as the large-sample equivalent of unbiasedness.
Only estimators can be biased. Similarly, it does not make sense to speak of the variance of $\beta_1$.   
A: Here's an example. 
Suppose you have a set of n data points from a normal distribution. We assume that the observations are independent. 
$$
X_1, X_2, \cdots, X_n \sim N(\mu,\sigma^2)
$$
We want to estimate $\mu$. The usual way is a sample average $\overline{x} = \frac{1}{n} \sum_{i=1}^{n} x_i$. The sample average estimator $(\hat{\mu} = \overline{x})$ is consistent and unbiased for the normal distribution. We could define other estimators however, for instance we could just use the last observation for our estimate of the mean $(\hat{\mu} = x_n)$. 
The sample average $\overline{x}$ is an unbiased and consistent estimator for $\mu$. The `latest-value' estimator $x_n$ is unbiased but not consistent, the expected value of this estimator is $\mu$ but the variance does not decrease with more observations. 
Bias and consistency are not directly related. Neither implies the other. 
However if the bias remains non-zero and does not decrease with more observations then the estimator must be inconsistent. 
In your supplied document, the author showed that the estimator had a non-zero bias which does not decrease with $n$. 
It doesn't make sense to call a parameter or an estimator biased. An estimator is biased with respect to a parameter. 
