Variance and covariance in the context of deterministic variables Questions:


*

*Can we talk about:
variance of a deterministic variable?;
covariance between a deterministic variable and a stochastic variable?;
covariance between two deterministic variables? 

*Are these concepts well defined in sample?; in population?


Motivation
Take a simple regression
$$y = \beta_0 + \beta_1 x + \varepsilon.$$ 
Suppose the regressor $x$ is stochastic. The OLS estimate of $\beta_1$ will be 
$$\hat{\beta}_1=\frac{\widehat{\text{Cov}}(x,y)}{\widehat{Var}(x)}$$ 
where hats denote sample counterparts of the population concepts. No problem here.
Now suppose $x$ is deterministic. I am not sure if I can use terms like variance and covariance in this context. Should I exchange $\hat{\beta}_1=\frac{\widehat{\text{Cov}}(x,y)}{\widehat{Var}(x)}$ for something like 
$$\hat{\beta}_1=\frac{\frac{1}{n-1}\sum(x_i-\bar{x})(y_i-\bar{y})}{\frac{1}{n-1}\sum(x_i-\bar{x})^2}$$ 
to be correct? But then again, how meaningful is $\bar{x}$ when $x$ is deterministic? So should I go all the way to 
$$\hat{\beta}_1=\frac{\frac{1}{n-1}\sum_{i=1}^n(x_i-\frac{1}{n}\sum_{j=1}^n x_j)(y_i-\frac{1}{n}\sum_{j=1}^n y_j)}{\frac{1}{n-1}\sum(x_i-\frac{1}{n}\sum_{j=1}^n x_j)^2}?$$
I am picking on details here and this may not be too important; my main questions are listed at the top of the post.
 A: All five questions have "yes" answers--but we have to be careful about what they mean.


*

*"Variance of a deterministic variable."
Let's understand a "deterministic variable" to be a univariate dataset.  It's just a bunch of values $X=x_1, x_2, \ldots, x_n$, with no probability model.  By definition its variance is
$$\text{Var}(X) = \frac{1}{n}\sum_{i=1}^n \left(x_i - \bar X\right)^2$$
where $$\bar X = \frac{1}{n}\sum_{i=1}^n x_i$$ is its mean.  There is no justification whatsoever to use $n-1$ instead of $n$ in any of these fractions--and this is never legitimately done--because no estimates are being made.
We may always think of $X$ as defining a "population."  This is the definition of a population variance.

*"Covariance between a deterministic variable and a stochastic variable."
One way to understand this is to assume it refers to a sequence of the form $(x_1, Y_1), (x_2,Y_2), \ldots, (x_n,Y_n)$ where the $x_i$ are numbers and the $Y_i$ are random variables.  Then we may define the random variable $$\bar Y = \frac{1}{n}\sum_{i=1}^n Y_i,$$ via which the covariance of $x$ and $Y$ is defined as
$$\text{Cov}(x,Y) = \frac{1}{n}\sum_{i=1}^n (x_i - \bar x)(Y_i - \bar Y).$$
It is a linear combination of the $Y_i$ and consequently is itself a random variable.  This notation is frequently used as a shorthand in linear regression calculations.

*"Covariance between two deterministic variables."
"Two deterministic variables" can be considered a dataset of ordered pairs $(x_1, y_1), (x_2,y_2), \ldots, (x_n,y_n)$.  The covariance can be defined exactly as in (2) and interpreted similarly.  In fact, this is a direct consequence of (1): after all, covariances are variances.

*"Are these concepts well defined in samples?"
Because they are well-defined for any dataset, they are well-defined for a sample.  Note that similar expressions with $n-1$ in the (outer) denominator are estimators: they are not the sample variance or sample covariance.

*"Are these concepts well defined in populations?"
Because they are well-defined for any dataset, and a population can be considered a dataset (when fully enumerated), they are well-defined for a population.
A: The simple answer to your first three questions is no: it makes no sense in general to talk about variance or covariance involving a deterministic variable.
However, if we begin with a deterministic variable but then use some method of imposing a probability distribution on it, it then becomes a random variable, and the concept of variance then makes sense. For example, any deterministic variable can become a random variable simply by imposing a degenerate (i.e., constant) distribution on it; in this case, the variance (along with its covariance with any other random variable) becomes 0.
A more interesting way of imposing a distribution on deterministic random variables is to use the empirical distribution, based on an observed sample. That is, if you observe $x_1,\dots,x_n$ in a sample, then we can define a discrete probability distribution on $x$ by $$P(x=x_0)=\frac1n \cdot\text{the number of $i$ such that $x_i=x_0$}$$
for all $x_0$. For example, in the case where $x_1,\dots,x_n$ are all distinct, we get $P(x=x_i)=\frac1n$ for each $i=1,\dots,n$. If we use this probability distribution on $x$, then the mean of $x$ becomes simply the sample mean $\overline x=\frac1n\sum_{i=1}^n x_i$, and the variance of $x$ becomes $\hat\sigma^2=\frac1n\sum_{i=1}^n (x_i-\overline x)^2$.
This same idea can be applied in the situation where we have observed vectors $(x_1,y_1),\dots,(x_n,y_n)$. We can define a joint probability distribution on $(x,y)$ again by using the empirical distribution, and the covariance between $x$ and $y$ then becomes $$\text{Cov}(x,y)=\frac1n\sum_{i=1}^n (x_i-\overline x)(y_i-\overline y)$$
