sorry, my keyboard just blew up…had to plug in a new one.

But as I was saying, I find it tremendously funny that economists think they can use math in their field.

Not to say that a competent understanding of simple arithmetic wouldn’t be useful…like when calculating government budgets, and government statistics could use some dumbing down, and structured finance would work better if 20% down mortgages to people who have had their incomes verified by the lender was what we are buying to get an inflation matching yield.

But I’m perfectly happy with Professor Roubini assimilating large amounts of information and then giving us his “gut feel”.

Also, you can’t get the timing right on macro predictions when you don’t know how many trillions in external financing you can get !

Paulson might have just gotten lucky. Getting the timing exactly right is hard. But yes, he called it right, and Nouriel was early.

I know this well. I used to argue that RGE shouldn’t send out an email every week about nouriel’s latest prediction that the recesion was coming.

I still think Nouriel deserves credit for two things:

a) he didn’t waver; the imbalances that he believed would drag the economy down didn’t correct, so he got more worried over time not less even tho his initial forecast was early
b) he was almost alone (and right) in arguing that the financial sector would be hard hit by the combination of falling housing prices and a slowdown, and that all the new fangled securitization technology had dispersed risk to those able to bear it and the us financial system wasn’t well capitalized. on that he has been proved right — and that is big. sure he was early. but, to be a bit crude, a lot of his critics would be bust now if not for unprecedented US government intervention to support the financial sector. and i do think the regulators should have spent less time talking about the wonders of risk dispersion and a bit more time trying to find out who exactly was holding housing market risk.

note the points i made about complacency in the post. Senior folks in big banks were criticizing roubini as a scare mongerer who didn’t understand the resilience of the us economy and us financial system. the IMF was going to reorient itself towards helping with growth rather than worrying about risks. and so on …

Thanks for your reply; my initial comment (and yes, you were responding to Tony, but also to me) was motivated by a sense that:

a) a lot of formal modeling is done without ever really looking at data. I assume X, Y and Z. I think those assumptions are relevant to the world (an often unstated assumption). Those assumptions and a bit of math yield an interesting theoretical conclusion. For example, IMF lending has no impact …

b) on the other hand, a lot of analysis of past data is done with limited amount of thinking about what might have produced the outcomes that emerge from the data.

it is for example possible to construct a national housing market index. but that doesn’t mean that the housing market in 1970 is like the housing market in 2005. I remember well that missouri banks weren’t allowed to operate in Kansas back then. The big issue bank regulators worried about was regional concentration. Texas banks had a lot of energy exposure, and suffered when prices fell in the 80s. National banks with more diversified balance sheets were the answer — but they also helped to nationalize (together with mortgate brokers) a change in the lending standards nationally that helped create the conditions for a more correlated national housing market.

another example is that the losses on subprime loans were lower than expected in the last recession, largely b/c housing prices didn’t fall during the recession to the underlying collateral remained good. and since subprime was a fairly recent innovation, there wasn’t a data series that stretched back far enough to capture a housing market recession. net result: I think some risk models concluded that the risk on subprime had been over-estimated, and that it was a better bet than people thought …

but then behavior started to change based on that assumption, and the conditions that produced the lower than expected losses dissipated.

my guess is that we do not fundamentally disagree — using data to make judgments about financial risk and reward is hard.

I hear you, your points are well made.
Good day.

Thanks, we seen less far apart than I thought. My grudge is mainly with people who use models opportunistically, and with people who think economics is an arena for showing off mathematical skills to potential competitors for tenured positions. I think a good economist should certainly be able to present an argument in a formal manner and where possible strive for empirical validation, requiring models with a manageable quantum of components.
Just take a look at the March 2008 issue of the earlier mentioned AER ( 2008, 98:1, 267-293). Something every successful third world politician knows (or else he would not be around anymore) thugs beat rules when they get the chance. It is interesting, not easy to read and unsurprising, except for the naive. Good economics means being able to present economic thought in a way the public will understand, because it is not a bloody bridge, it is changing, fickle and ingenious human beings who make an economy and who need to be educated to be responsible citizens.

Reading the actual IMF transcript of what happened at the presentation offers a different story:

http://www.businesscycle.com/news/press/1643/

Thank you for sharing your thinking on my ‘defending equations’ post.
I am a student of the financial markets and use equations to try to understand
how all the forces acting on markets cause it to evolve the way it evolves.
As such, I am sensitive to those that put a negative spin on
using simulation models constructed from equations to understand
financial market behavior better. Now that I have reflected on yesterday’s post
I realize more than anything it was Tony’s post that justified avoiding equations
because of an awareness that normal distributions don’t reflect lots of observed
phenomenon that tipped me over into spending the time to write and submit the
‘defending equations’ post. The fact is that the assumptions underlying ANY probability
distribution based only on data and not based on one or more behavioral equations is
to me not really telling you anything about the forces that brought that particular
distribution about. So what really bothered me about Tony’s post was that he was condoning not using equations because probability distributions derived from data don’t rely on underlying relational/behavioral equations; this to me is the key problem with how risk management is done on Wall St. Just because many
derive conclusions about risk on data alone without attempting to model any underlying structural relationships in an attempt to model behavior is to me not a reason to condone not using equations.

I think both you and Nouriel model economic problems in your own way better than most; otherwise I wouldn’t spend lots of time reading your blog posts that shed light on the most challenging economic issues of our time. I am glad you got something from the ‘defending equations’ post. Good day.

To Rien Huizer;

Rien asks, “How would you structure uncertainty in a prescriptive model for say, Sudan, in a state of civil war, or Afghanistan? There is no way to model the strategies of the actors you think may be relevant, there is no way to test theories. In other words you might end up with a signal to noise ratio of close to zero after years of work.”

A fair question; I’d have to understand the institutions that exert power in the region, their goals, their
time horizons for getting results to the pressures that are making them act the way they are acting. I would have to understand what people make up these institutions, how they came to power, how they maintain their power, what the rules of these institutions are. They may not have a Constitution but they have cultural norms and a perspective about themselves and others that could help shed light on why they act the way they act. Is it a fight for petrodollars in various regions where various factions cannot resolve the resource allocations non-violently? Given this environment, what prescriptive actions could lessen the violence?
If I took on this problem and tried to model it using equations I would very likely as you say end up with a signal to noise ratio of zero for any modeled policy responses (prescriptive actions.). You cite a problem that needs bold assumptions, bold assumptions about the actors’ behavior and WHY they behave the way, they do. It has been said that if ancient Greeks could see the World today that they would be amazed at all the technology and would not understand it, but they would completely understand what’s going on in a session of Congress in the USA or in the dreadful situation in Darfur. To say, “There is no way to model the strategies of the actors you think may be relevant, there is no way to test theories.” underestimates your and humankind’s potential to address pressing problems. If some group of people think they can address such problems using equations and simulations that try to model the institutional and involved individuals’ behavior, then the approach and any prescriptions should be scrutinized and vetted but they should not be dismissed out of hand.