from Lars Syll
When we cannot accept that the observations, along the time-series available to us, are independent, or cannot by some device be divided into groups that can be treated as independent, we get into much deeper water. For we have then, in strict logic, no more than one observation, all of the separate items having to be taken together. For the analysis of that the probability calculus is useless; it does not apply. We are left to use our judgement, making sense of what has happened as best we can, in the manner of the historian. Applied economics does then come back to history, after all.
I am bold enough to conclude, from these considerations that the usefulness of ‘statistical’ or ‘stochastic’ methods in economics is a good deal less than is now conventionally supposed. We have no business to turn to them automatically; we should always ask ourselves, before we apply them, whether they are appropriate to the problem at hand. Very often they are not. Thus it is not at all sensible to take a small number of observations (sometimes no more than a dozen observations) and to use the rules of probability to deduce from them a ‘significant’ general law. For we are assuming, if we do so, that the variations from one to another of the observations are random, so that if we had a larger sample (as we do not) they would by some averaging tend to disappear. But what nonsense this is when the observations are derived, as not infrequently happens, from different countries, or localities, or industries — entities about which we may well have relevant information, but which we have deliberately decided, by our procedure, to ignore. By all means let us plot the points on a chart, and try to explain them; but it does not help in explaining them to suppress their names. The probability calculus is no excuse for forgetfulness.
John Hicks’ Causality in economics is an absolute masterpiece. It ought to be on the reading list of every course in economic methodology.