What makes constant function an estimator? This is a theoretical one. This question is inspired by recent question and discussion on bootstrap, where a constant estimator, i.e. a constant function
$$f(x) = \lambda$$
was used as an example of estimator to show problems with estimating bias using bootstrap. My question is not if it is "good" or "bad" estimator, since it is independent of data and so it has to be poor. However, while I agree with the definition that Larry Wasserman gave in his handbook "All the Statistics":

A reasonable requirement for an estimator is that it should converge
  to the true parameter value as we collect more and more data. This
  requirement is quantified by the following definition:
6.7 Definition. A point estimator $\hat{\theta}_n$ of a parameter $\theta$ is consistent if $\hat{\theta}_n \overset{P}{\rightarrow} \theta$.

then what bothers me is that $\hat{\theta}_n$ estimated using a constant function does not approach $\theta$ even with $n \rightarrow \infty$, since it is constant.
So my questions are: What makes constant function an estimator? What justifies it? What are its properties? What are the similarities between constant function and other estimators? Could you also provide some references?
 A: An estimator is simply some function of a potential sample of data that seeks to estimate an unknown population parameter. It's a recipe or a formula. Your constant is an estimator that does not depend on the data at all: the estimate that is produces will always be the same.
There's an infinite number of estimators, and most of them are "bad". What does that mean? Estimators have desirable properties, which leads them to produce "good" estimates under certain conditions. Some of these are


*

*Computational cost

*Unbiasedness

*Consistency

*Efficiency

*Robustness (insensitivity to violations of the assumptions under which the estimator retains its desirable properties)


These goals are often at odds with each other. The constant has the lowest computational cost, but arguably none of the others. 
A: I think it's not so much a question of ''what makes constant function'' an estimator but ''what makes estimator an estimator''.
First, from mathematical point of view an estimator is a function of special kind, it's a random variable, that fulfills some requirements - it's a statistic, which means it has to be independent from $\theta$ (its ''estimand''). Constant function is independent of $\theta$, (it's independent of anything :).
Example. $T =\bar{X}$ is a statistic of $\mu$, and $S=\bar{X}-\mu$ is not a statistic of $\mu$ ('because it's dependent on $\mu$ itself).
So, a constant function is an object that possess these two qualities, that justify calling it ''an estimator''. 
The quite important thing is that what we desire is not "any" estimator. 
Any estimator may be biased, which means that with every sample we obtain it adds or subtracts something. F.e. you want your bathroom scale to show your weight exacly as it is (or maybe women more tend to cheat themselves:).
We want an estimator that will minimize Mean Square Error ($MSE=E(\hat{\theta}-\theta)^2$). But there is no one estimator, that minimize this error - it's a family of such estimators. So which one is the best one? A good estimator is the one, that fulfills some requirements. I know about three of them:


*

*unbiasedness - it does not adds or subtracts anything. Mathematically
it's $E\hat{\theta} = \theta$

*consistency (this what is written in your book)

*maximal efficiency which refers to estimator's variance - we want as small variance as possible.


Someone wrote about computational cost, but it's not mathematical/probabilistic issue.
Thus, a constant function actually is an estimator, however is not desired one, 'cause at least it's biased and not consistent (as you noticed). These are differences between constant function and other (good) estimators.
This is more or less my answer to your question. I think, going further will make us dig in some mathematical equations to show more differences or similarities, etc.
A: Constant estimators/predictors have a use as benchmarks against which one judges the performance of "proper" estimators/predictors.
A standard example is in the context of binary logistic regression, where we attempt to estimate conditional probabilities, exploiting the information that possibly resides in the regressors in order to predict better, in some sense, the probability related to the dependent variable,  
$$P(Y_i=1 \mid \mathbf x_i) = \Lambda(g(\mathbf x_i'\beta))$$
where $\Lambda()$ is the Logistic cumulative distribution function, and $g(\mathbf x_i'\beta)$ is the logit.
But since we have the sample available, we can also very cheaply estimate the unconditional probability, 
$$\hat P(Y=1) = \frac 1n \sum_{i=1}^n y_i$$
We can then compare the predictive performance of $\hat P(Y_i=1 \mid \mathbf x_i) = \Lambda(g(\mathbf x_i'\hat \beta))$ against the "naive" (and constant) estimator $\hat P(Y=1)$. The former should do better, otherwise all the trouble we went into trying to use the information about the probability of $Y$ included in the $X$'s did not pay off.  
A CV thread exactly on this issue can be found here (look also at the comments). 
