Regression over the whole population What's the meaning of the standard error of a coefficient in a regression when the whole population is included?

I've been so puzzled by this question. Because it seems to me, standard errors make no sense when the whole population is included -- there is no need for statistical inference since you already have the whole population.

But it is so widely used even by many articles published in top journals. For example, if I am examining the relationship between a country's GDP growth rate and its population density, I run the regression:

$$ GDP_i = \alpha + \beta Pop_i + \gamma \mathbf{X}_i + \epsilon_i$$

with all 195 countries on the earth. In the case, all countries (the population) are included. But all literature still talk about the statistical significance of the coefficients. 

Could somebody explain is it a misuse of statistical inference when regressing over the whole population?
It's important to consider what exactly the population is about which an inference is being drawn.  It's easy to overlook the time aspect in this context.

Suppose for example that the aim is to forecast the next two years' GDP for each country in the world. Then the population of interest is a set of pairs of the form "country, year".  It isn't simply "all countries", and even if a forecast model has been estimated by regression on current and past years' data for each country, that doesn't mean that the whole population of interest has been included.

If one really does start from a complete dataset for the whole population of interest, then all one can do is calculate summary statistics. That could include standard deviations, but it would be inappropriate to call these standard errors, since that term relates to a sampling distribution whereas the only "sample" in this case is the whole population.