Does "Inference" include estimation or only testing? Does the term "statistical inference" include only hypothesis testing or does it also include point estimation, interval estimation etc.
Authoritative references will be greatly appreciated. 
 A: It includes any procedure in which you try to draw conclusions about an underlying population or data generating process from data using statistics.  Yes, this definitely includes point estimation and interval estimation, etc.
References? - I would start with any book with "statistical inference" in the title, but Wikipedia would also do.
Edit/addition: here are some specific references.
First and very directly to your point is from page 1 of Paul H. Garthwaite, Ian T. Jolliffe and Bryon Jones (1995), Statistical Inference, Prentice Hall.

"In statistical inference we use a sample of data to draw inferences about some aspect of the population (real or hypothetical)
from which the data were taken.  Often the inference concerns the
value of one or more unknown parameters, which describe some attribute
of the population such as its location or spread.
There are three main types of inference, namely point estimation, interval estimation and hypothesis testing..."

And here is my favourite, A. H. Welsh (1996), Aspects of Statistical Inference, John Wiley & Sons

"Statistical inference is concerned with using data to answer
substantive questions.  In the kind of problems to which statistical inference can usefully be applied, the data are variable in the
sense that, if the data could be collected more than once, we would
not obtain identical numerical results each time." (p.1)
"The components of the inference problem are:

*

*a substantive question

*data z which we interpret as a realization of a random
variable Z with a distribution $F_0$

*a model for $F_0$
The objective of inference is to answer the substantive question by
reformulating it as a question about the underlying distribution $F_0$
and then using the data z, the model and any other information we have
to answer the question about $F_0$.  The kinds of questions we ask
about $F_0$ are typically of one or both of two types:

*

*Can the
model be viewed as a reasonably close approximation to the data
generating process?

or

*

*Can we determine a set of plausible values for a parameter
$\theta(F_0)$, or can we determine whether a particular value of a
given parameter $\theta(F_0)$ is plausible?

The answers to these questions are derived from the data through the
calculation and interpretation of the realized values $t(z)$ of
statistics $t(Z)$, which are functions of the data which do not depend
on any unknown parameters." (pp. 31-32)

A: 
In a typical problem of statistics it is ... a class of laws [which is specified], any of which may possibly be the one which actually governs the chance device or experiment whose outcome we shall observe.  We know that the underlying probability law is a member of this class, but we do not know which one it is.  The object might then be to determine a "good" way of guessing, on the basis of the outcome of the experiment, which of the possible underlying probability laws is the one which actually governs the experiment whose outcome we are to observe. ...
... statistical inference [is] the subject of obtaining good guessing methods. ...
It is possible to discuss all of the important ideas of modern statistical inference ... and we shall try to do this.

-- Jack Karl Kiefer, Introduction to Statistical Inference, pp 1-3.  Springer Verlag, New York (1987).
Kiefer's discussion of "all of the important ideas" fills the rest of this text.  Thus the main chapter headings (following preliminary general material) may serve to document what he felt comprised statistical inference:

*

*Linear Unbiased Estimation (the general linear model)


*Sufficiency (concepts of the maximum likelihood theory)


*Point Estimation


*Hypothesis Testing


*Confidence Intervals
Notably, statistical prediction is not included in any of this.
