Indeed, I have been sorely tempted to do a post on ticket scalping for a while – I’m sure I have a bunch of links I sent myself in an email somewhere. I’ll definitely do it at some point – but it probably won’t be terribly exciting 😛

Want to do a post on barter and first degree price discrimination at some point – discussing why barter may be more “efficient” until the opportunity cost of time gets to high. I’ll try to do that this week. I’ve got two posts scheduled tomorrow anyways so I’ll probably think about it tomorrow night 😛

A post about ticket scalping would be entertaining. I have had several hearty arguments with coworkers explaining that scalping is natural and the onus of “protecting the fans” rests with the sellers of the tickets.

I am not really sure what you are asking here. If you are just writing one way of describing price stickiness then that is cool 🙂

It appears that you are stating that when a firm chooses prices over time and subject to uncertainty then in the face of any small shift in demand they are unlikely to actually change the price they set. This will only work in the sense of a unrequited change in relative prices (relative to the counter-factual) as long as there is some sort of transaction cost (as otherwise they would re-optimise whenever they face a statistically significant change in the distribution of their error term). This is where “menu costs” come in for the New Keynesian literature.

It is important to note that, empirically, menu costs haven’t been found to be significant in many industries (namely supermarkets) as a result, the nominal price stickiness is often confined to specific industries, which in turn makes the relative price issue a difficult one to conceptualise in an aggregate model. Who knows, this is where true general equilibrium theory may make a come-back 🙂

On the issue of price rigidity. Any firm, like Apple, plans an execution of transactions in its market over time. Its effort yields a price for the longer term value of the company. The price is estimated by industry accountants, who keep both their estimate of the fortunes in the software market, plus an estimate of the error of their estimate.

So, the resultant rate and value of market transactions yield a most likelihood estimate of discounted firm value. We know much from linear estimation theory, like the bounds on price volatility, over time, to yield an estimate of the error.

Regarding price rigidity, the firm has a very fixed idea of the rate*value of transactions it is willing to risk over time to yield a specific certainty in longer term firm value. He cannot go beyond that because his measurement error is still to great, and the next increment of transactions, taken again from linear estimation theory, would be N*logN rate of increase for each N of precision “bits”. You pay a power law price to get the next increment of market precision. So, the firm (and market) has to restructure to restore excess capital.

The model is that things important in the economy are viewed by intelligent people through the lens of an ad hoc Kalman filter, decisions are optimal to reduce costs, and both the differential manifold and measurements (transactions) must converge.