I don’t entirely agree. First, can you tell me what a priori assumptions the Austrian school makes which aren’t present in other schools?
When creating models you don’t just get a steady flow from one conclusion to the next that eventually lets you know everything about a certain reality. You get contingencies. An example would be your labour supply based on your wage. Does it slope up or down? The answer: It depends. There are two forces at work when your wage increase: The income effect and the substitution effect. These are basically your leisure-consumption preferences. If you have a preference for leisure, a wage increase will make you richer and you won’t have to work as much to fund the same level of consumption. So you’ll lower your labour supply. If you prefer consumption, your wage increase now means that an extra hour of work will give you a lot more extra consumption than it used to. So you’ll increase your labour supply. Now the question of whether or not labour supply slopes up or down depends entirely on which effect dominates. The problem is, you can’t just say a priori “rational people prefer consumption to leisure”. These aren’t things you can reason, These are subjective preferences that people decide based on emotion, not logic. So you can’t determine which effect will dominate with logic alone. If you want to make statements about the impact of certain things on the labour market, you need to measure whether or not the labour supply curve slopes up or down.
Linked to that stuff above.
No. You’re describing exactly what I said before. You’re just talking about the gains you could have made had you not shorted. The problem being, you can’t predict whether it’ll go up or down in the short run, but you can predict it’ll go down in the long run. You care about long run gains, because they are certain. Not short run gains, which are random. Your principal never gets diminished. The current value of your asset will decrease, but you don’t care. It’ll increase significantly in the long run.
As rigorous as can be without becoming a useless abstraction that can’t produce any results. It all begins with intertemporal utility/profit maximisation, but certain assumptions which make the theory unique are applied. There are the obvious ones which everybody uses like diminishing marginal utility and dominance of the substitution effect in current labour supply, but their special assumptions are high intertemporal elasticity of labour supply and random persistent shocks to TFP.
On the contrary, the risk of playing against an opponent who controls a printer of legal tender in his basement is extremely large. He can match whatever bet you make with an opposing bet of ten times your size. Plus, this system incentivizes his buddies (banks) – who get first dibs to all newly created “capital” and control most other bets in this “free” market – to bet in his direction. The risk/reward for this cabal is such that: (1) fail, everybody must bail us out lest ATM’s stop giving out cash tomorrow morning, (2) win, well we win.
Are you sure you have the staying power (the capital needed to pay for your losses as prices are being pushed against you) to match theirs? Who do you think a smart trader would be betting with/against? Think again.
Your problem is that you’ve been brainwashed at school to view the above predicament as a free market, whereas it’s pure **Gosplan **socialism. You will never get out of your Matrix and see reality for what it is unless you understand the workings of the central banking system.
Spoken like a true trader. You must be making a killing in the markets.
This seems to have become quite a lively thread! @Marginal Interest, the problem with econometric forecasting as I see its implicit assumption of constancy in relationships of past variables. This is something Mises pointed out ad nauseum given that human action is necessarily and always a complex phenomenon, in which there are no quantitative constants and nothing is held equal as would be required to replicate laboratory settings and formulation of the type of theories found in the natural sciences.
When one forecasts, you are taking an econometric model estimated with parameters that best fit past data according to some criterion (Max. Likelihood, Least Squares etc.), and then further projecting that the underlying relationships will not change and using these to project into the future. The same goes for any “predictions” regarding the consequences on one variable given a shock in another. You are always making the untenable assumption of the constancy f these relationships among these variables, and hence identically, the invariance of their conditional probabillity distributions with respect to each other. Given this I don’t find it shocking that after decades of abject failure, most econometricians don’t take forecasting seriously. What I do find amazing is that anyone could ever take forecasting seriously.
What it does at best is not much better than tracing out a best fit line or relationship to data and project it to carry further into the future. The term “structural break” is used to label when these invariances stop and you cannot avoid the conclusion past relationships assumed to be invariant are broken. They could try to “solve” this problem by taking a larger set of time series data, but then it would probably dawn on them that these price data might not necessarily be comparable (again, a point Mises made continuously…).
Also, a comparison to the issues based on problems in forecasting in meteorology is further misguided. Meteorology is informed by theories developed in the natural sciences (just like good economic history is informed by praxeology), and hence the understanding of the fact that phenomena underlying weather are nonlinear and often chaotic is informed therefrom. Chaos theory gives us good reasons why as a matter of principle the behaviour fo these systems must be such that they must diverge from our predictions given the impossibillity of removing mesurement errors. These projections are made applying the theories of the natural sciences(tested and formulated in isolation) on a macroscopic scale where many elements interact. It would hence be incorrect to make an analogy with the above described methods og econometric forecasting, maing your defense of the latter by comparison to the former utterly erroneous.
How does that risk exist? The only way it could possibly exist is if the ability to keep printing money would result in the real value of houses never coming down. That’s absurd for two reasons: 1) It assumes a grotesque amount of money illusion; 2) if the prices never come down then it’s not a bubble is it? So obviously you wouldn’t short it.
Why would that be the case? I think it makes sense that markets are to a large degree efficient consistent with the semi-strong EMH. I can’t consistently beat the market because I have no information that they don’t already have long before me. The best I can hope for, without constantly having my finger on the buy/sell buttons to react within seconds of new news becoming available, is to do only as well as the market trend.
Yes. This problem, though not entirely the same as what Mises is saying, is known as the Lucas Critique in the mainstream.
The microeconomic concepts macro models are built on are reasonably well tested, both econometrically and experimentally. Whatever the reason for forecasts (economic or meteorological) being inherently inaccurate, the point is that the failure of such forecasts does not render the field unscientific; as was suggested by the OP.
Yep. I hold a conflicting opinion, so I must have been brainwashed! I’ve been nothing but courteous and you give me this shit. You paying attention to this John James?
I don’t think what I said above has much specifically to do with the Lucas critique. You claim these microeconomic theories are well tested, are you sure they’re not just assuming what they are trying to prove? In order to “impute” utillity functions to consumers based on past purchase data you have to actually assume that their preferences were “constant.” This is untestable, howver reasonable it might seem in a given scenario intuitively(which is usually how it is justified when push comes to shove, e.g. that desire for milk doesn’t change each month).
Alternatively, similar problems are created if you try to find structural parameters from a simultaneous equations econometric model, e.g. of supply and demand. You have to input restrictions informed from economic theory in order to do this, but there is no way to actually test these restrictions themselves, leaving the frequent occurence that antagonistic and contradictory theories would simultaneously provide a good fit to the data (I guess I don’t need to say who pointed this out again…). (In fact, I’m quite glad my econometrics lecturer was candid enough to admit this too).
However reasonable these assumptions of economic theory might be by “intuition” ( a word as definite as praxeology would probably make neoclassical economists want to run for the hills), they can’t be tested or imputed from the data.
Well the problems of parameters being non-constant just due to the natural variation caused by “human complexity” is reflected in the fact that the estimators are given with a confidence interval whose width is proportional to the variance of the error term. So parameters which would change erratically in large magnitude would produce unusably large confidence intervals. So I think that problem isn’t entirely valid. However the Lucas Critique remains a big issue with forecasting, so I f igured I’d mention it.
Well many of the assumptions are unfalsifiable, like that consumers maximise their expected utility. I’m not exactly sure how putting constraints on a model derived from that can be criticised when it’s exactly praxeology… We find estimates of, say, labour supply elasticity, which if in every case of measurement happens to be negative would be good enough grounds to justify using upward sloping supply curves. Yes, the models used to form the estimates impose some theory on the data, however that’s one of the great things about peer review. There are plenty of people out there who will check the sensitivity of your results to the model specification. Don’t get me wrong, I by no means think econometrics is perfect. It faces amazingly difficult problems, especially with forecasting. But I don’t think it’s entirely useless.
I wasn’t referring to anything as trivial as that. The variance of error terms used to inform confidence intervals, is again an instance of assuming constancy of relationships in projecting forward past relationships to future data. Shocks inherently can never effect the conditional distributions in such forecasting models, as that very fact is their operative assumption.
There isn’t a problem with using insights that are ultimately praxeological (although of an often bastardised form, using untenable assumptions where they cannot apply), but in thinking that you are letting the data speak for itself, distinguish between the theories you’re utillising(which ultimately it can’t) and impute forward relationships based on it (though as you and I have already noted, nobody in their heart of hearts really seems to believe this anymore).
Perhaps I’ll explain my view on econometrics: Obviously it can’t give you truth. You can’t figure everything out just by looking at some data in relation to other data. It’s there to advance theory which has reached it’s “praxeological limits”. Logical deduction will only get you up to the point where you identify (going back to the same example) that there’s an income effect and a substitution effect and consequences depending on which dominates. You need econometrics at that point to be able to say “this effect dominates” or “this effect is insignificant” and actually proceed with your logical deductions, conditional on that observation, so that your model can have more explanatory power. The other use, of course, is in falsifying unique predictions models make. Though (and this is my biggest complaint in regard to economics) there’s not nearly enough drive to produce these predictions.
I can deduce the theory of price praxeologically (see Bohm Bawerk’s theory of marginal pairs), through which we may explain the notions of “supply” and “demand”, as consequences in explaining price determination. The complete theory allows for an can account for trades not at the equilibrium price. Application of the supply-demand “model” necessarily assumes all trade at the equilibroum point, unless dynamised using the Cobweb model. Something like the Cobweb Model makes very primitive assumptions about entrepreneurial expectations however, assuming that sellers simply react to yesterday’s prices in their production for today in an automative manner, as pointed out by John Muth (to whom I think the credit should really go for Rational Expectations). Unfortunately, the assumption of rational expectations means that you have to make even stronger assumptions regarding people’s niscience of the future to even explain something as simple as price formation and market clearing. These issues would all have been avoided if people within the profession had the abillity to think beyond the equilibrium Walrasian S-D model, or disequilibrium bifurcations of it, in which you also implicitly assume price determination to explain it given that demands are produced as solutions to a Lagrangian optimisation problem with price “already given” to each consumer as a parameter (for instance when put in the typical, Perfect Competition form).
Meanwhile, you can actually explain all instances of price formation with Bohm Bawerk’s apparatus(as well as with Wicksteed and Fetter’s variations along the same lines), as well as including the concept of expectations since all bids are based on them, but you don’t need them to be anywhere near “exact” to explain price determination. Neither do you run into the nonsensical issue produced by the Cobweb Model, in which you don’t have equilibrium convergence if demand and supply price elasticities around the equilibrium point are beyond a certain kind. These Walrasian price theories you can derive using reasoning that somehwat resembles praxeology, but my point is the errors produced are produced by the incorporation of bad assumptions and hence bad deductions based on them. They necessarily obscure the true explanation of price determination, inspite of the fact that one could gather data and pretend try to “test” and distinguish them.
With regard to income and substitution effects and attempts to distinguish them, I think I’ll pass comment, aside of the and demand constancies often assumed in such an analysis. There are other issues too, like the fact that from a set of observations you might reckon e.g. demand is upward sloping, when what you’re actually doing is finding different demands on a relatively stable “supply curve.” Given that all you know are just the hypothesised intersections of 2 curves, it still seems to me that this kind of thing could never be determined by the data, rather, a prior udnerstanding is used to inform interpretation of the data always.
Finally, your final request, I think if carried out would probably lead to the complete (and deserved) discrediting of the vast majority of the economics profession. That’s also why I don’t think it’ll happen.
I don’t know if this has been brought up before [probably has, somewhere !], but Doug French on Mises Daily recently posted a good article that goes to the heart of the debate in this thread, called" The Folly of Forecasting" . regards, onebornfree
Here is another good article I recently found on economic modeling [it is probably “old hat” to most regulars here] : “The Myth of the Model”. by Max Borders at The Freeman.
Taken together, these claims are false. An RBC model with rational expectations assumes that the economy acts as a maximizing agent and is in general equilibrium. Estimates of microeconomic magnitudes that seem plausible at the individual/industry level do not necessarily aggregate up. Therefore, If one believes that Walrasian models of general equilibrium tell us anything at all, then RBC models have no microeconomic justification except in highly implausible circumstances (such as homothetic and identical preferences and wealth among all consumers).
In other words, RBC models are merely based on faith that microeconomic processes will somehow “behave nicely” at the aggregate level, without any justification for this in the neoclassical system. At the very least, Austrian macroeconomics does not have this problem (in most variants).
Not sure what you mean in regard to “trades not at the equilibrium price”. If trade is being conducted at a non-equilibrium price (in absence of frictions), then necessarily consumers aren’t maximising their utility or producers aren’t maximising profit…
I don’t think the complaint that “price determination is implicitly assumed to explain it” is justified. The model you reference is something done in intermediate micro because it’s easy. It’s an easy way to introduce people to constrained optimisation, so it’s not going to be extremely general. The price is not “given”. For simplicity initial endowments are given. Price is determined endogenously, along with the quantity demanded, from the supply=demand equilibrium condition.
Where exactly does praxeology diverge from mainstream micro? In the case of price determination, we get demand schedules from maximising utility subject to a budget constraint, and supply schedules from maximising profit subject to a cost structure; then solve from the equilibrium condition. How does praxeology do it differently?
You’re not measuring a million individual elasticities, remember, you’re measuring an aggregate. And the theory is trying to explain the movement of aggregate variables. Obviously it’s absurd to try and predict the behaviour of millions of individual entities. Aggretation is a necessary evil in that what you lose in detailed information gets made up for in managability. So far it’s done remarkably well at explaining quantitative movements in variables ex post. Now what we need is to move away from calibration and make some ex ante predictions.
I did see a couple of papers a while ago with RBC models with many representative agents with differing preferences. If I remember correctly, it added much more tedium to the model but not much more explanatory power.
There was no risk in what you explained. You talked about bigger agents placing bets against you, which wasn’t coherent. I interpreted it as “the government will keep printing money to prop up the housing market”, and I addressed that. Was that an incorrect interpretation? If so, please explain it again.
It means the value of a portfolio sufficiently diversified to remove unsystematic risk will follow a random walk with a drift. That’s pretty obvious, right. If there’s some arbitrage opportunity, everybody will do it until it’s not there anymore. So basically if I pick some stock index, like the S&P 500, my portfolio won’t be able to consistently produce larger expected returns than a portfolio that’s just composed of that index.
I’ll paraphrase one of your buddies: Markets can be (and usually are) “wrong” much longer than you can remain solvent. Here’s an example of what happens to a hedge fund legend that tries to “fight the Fed”:
…For those unfamiliar with Robertson, then here’s what you need to know: He is the definition of a hedge fund legend. After attending the University of North Carolina, Robertson served as an officer in the US Navy and worked as a stockbroker for Kidder Peabody. He then founded and grew his (now defunct) hedge fund Tiger Management from $8 million at launch to over $22 billion in 1998 at its peak. And, as listed on our Tiger Cub biographies page, Tiger compounded a gross rate of 31.5% between 1980 and 2000. But, after losses of 4% in 1998 and 19% in 1999, Tiger shut down as the dot-com bubble expanded right in front of his eyes. He avoided what he deemed to be ‘irrational investing.’ The tech bubble would indeed be irrational investing, but his fund wouldn’t be around to see it through…
Simply put, in a socialist (Fed controlled) economy there is no “right” and “wrong” - there is only betting with or against the Gosplan. Prices don’t have to be going up forever for the correct predictors of a reversal (collapse) to get taken out to the cleaners before they are proven right. In trading/speculation, the timing, risk management, and staying power are everything. Having a printer of legal tender in one’s basement allows one to literally paint price charts on the screens 99% of the time, which is more than enough to shake out every opposing bet (into insolvency) and force most participants to toe the line. Once the one-way avalanche has been started it takes a life of its own and they don’t even have to push it anymore. And during the 1% of the time that markets get out of their control (such as in 2008) the risk of betting against them is that there would be no financial system and solvent counterparties left standing from which to collect your winnings, unless they crank up the printing presses and prove your bet wrong once again.
If you short a market at $100 thinking it should go to $20, and it proceeds to go to $200, then you would have lost all of your initial $100 principal, all your investors would have sued you (that is, the ones who wouldn’t prefer you dead), your wife would have thrown you out on the street, and your bank account would have $0 in it – even if the market fell to $20 after that, as you have correctly predicted. Do you understand this?
Who do you think is the largest buyer of US Treasuries today, and – given what I just explained above – which way would a smart market player bet?
Your questions truly baffle me. I feel bad but I will posit again – as a kind favor to you because I like you, and not as an insult – that you have been even more brainwashed at school than I previously thought.
Have you stopped to think what the major component/contributor to this “drift” is? What is the main factor behind the econometric-derived “wisdoms” such as “60-40 stocks-bonds portfolio” and “buy and hold”? I could give you a hint, but it’s better that you figure it out on your own. Let me ask you this: If all arbitrage is always and immediately eliminated, and there’s no real $100 bills left on the pavement, why does an entrepreneur still get out of bed in the morning? Because he’s stupid, and you’re smart?