Given the data in the last post, the temptation to fit some curve is irresistible. The data looked superficially hyperbolic, but that turns out to not be a very good fit. The blue curve above is y = 1/sqrt(-0.00011832x+0.01026645). Of course, I don't have the slightest theoretical justification for this curve, so the extrapolation has no basis beyond guesswork. But, just in case you were curious, it asymptotes at 86.8mbd (but don't forget the data being fit is effectively low pass filtered by the five month moving average, and is an average of three data sources). Probably for entertainment value only...1.
1. It's worth emphasizing for the less mathematical reader than there are always an infinite number of mathematical functions that can be made to fit a set of data like this about equally well, but will do different things outside the range of the data. So unless there is some kind of underlying theory of why a particular model is likely to be correct, this kind of extrapolation should be taken fairly cautiously.
Isn't this just the short run supply curve? My ancient econ text says that in some industries the short run extends over many years. And, in fact, there are very significant lags in the oil biz.
ReplyDeleteSo, from a econ perspective one could say that this graph shows that in the short run (say 5-7 years) it would take a very high price to get close to 87 mbpd.
ReplyDeleteOf course, the optimists would argue that over the immediate term, the curve shifts right.
Datamunger - the question of lags on the supply side is complex - I think there are a variety of processes happening at different scales. Certainly project inception to plateau production is anywhere from 2-7 years, which tends to suggest a long lag response - but only to surprising developments. To the extent the industry is able to anticipate demand changes (and changes in decline rates) they can have the projects in the pipeline already. Other processes operate very quickly - eg OPEC deliberately cutting back, or increasing production out of spare capacity (which they presumably have a little of at present). That can happen in a couple of months. And of course, the existing project pipeline at each oil company can be accelerated or put on hold depending on perceived price signals, which will tend to alter the way in which new projects overcome, or fail to overcome, the decline rate and thus cause production to rise or fall.
ReplyDeleteI would point to two things to suggest that the lags are not an overwhelming consideration here. One is this graph from Monday with the 85% R^2 at zero lag. The other is the 2009 data, which pretty much lies on top of 2006-2007 data. To the extent lags were game-changes, the industry should have been able to move the curve to the right over those 2-3 years, but it didn't.
Agreed.
ReplyDeleteI mentioned lags, not as game changers, but to to support conceiving this time series data as a supply curve: i.e. quantity v. price but ignoring the time dimension. Perhaps on economist in the oil biz would say that the so-called 'fixed factors' (kept constant in supply curves) don't have time vary in the few years under consideration here. Dunno really.
Ugh, we don't have the luxury of quizzing the industry about its hypothetical response to price changes. Maybe the craziness of the last few years revealed a few things.
Thanks for another cool graph.
wow.. you were serious when you said you were starting to blog again.. i can't keep up with this.. so here's a very belated response:
ReplyDeleteDid you see my article on the "The Economics of Volatile Oil Prices" earlier in the year":
http://anz.theoildrum.com/node/5110
Schematically I used a very similar supply curve to explain the price yo-yo over the last couple of years. As usual you've taken the analysis even further..