Economic Forecasting: Why Does It Matter?

Forecasting is a tough business, leading some people to question the value of forecasting altogether. However, economic analysis and forecasting are important for at least two reasons:

-They immediately affect stock markets. Financial analysts and economic forecasters generate on daily basis a lot of news which can easily change a market sentiment and move the markets easily by several percentage points. As a result billions of dollars of wealth can be evaporated or sublimated within seconds. One of the most notable recent examples of such impact is Kenneth Bruce’s August 2007 prediction that Countrywide Financial might go bankrupt. This prognosis was one of the key events that triggered bursting the housing bubble.

-They have direct impact on policymakers. The Feds use (at least in theory) their figures in setting up monetary policy. The federal government bases federal budgets and decisions on taxes, government spending and transfers on their forecasts.

And that is why I think that economists are directly responsible for the current crisis.

Why not consumers?
They are not informed properly (at least not all of them), they heavily rely on economists and government, they are moved by greed, and finally they did not have any incentives to be modest and sustainable. Why should someone be modest in this frenzy of spending with easily available credit?

Why not business (e.g. predatory lenders)?
They are moved by greed and animal spirits only ("The markets are moved by animal spirits, and not by reason" J.M.Keynes). They will do whatever is possible to take advantage of the situation. And everybody knows it. Can we blame predators for being carnivorous?

Why not the government?
Well, the government and Feds share responsibility. They did not act properly. However, the major reason for that -they heavily rely on economists in their decision-making.

So, the role of economists in this uncharted sea of modern global economy is to measure depth and foresee obstacles rather than guess where we will be in five minutes with the same wind and depth. To be useful economists must become whistblowers, not heralds.

In the next article we will consider what economic forecasters specifically overlooked.

Mainstream Economic Forecasting - Too Complex To Be Predictive

The Conference Board of Canada
model includes some 1,250 equations.

Forecasting is a tough business. It is particularly true for economic forecasting. Nowadays, economists constantly miss targets by a mile.

In the previous posting I discussed problems associated with economic forecasting in general. In this posting we will talk about the major intrinsic problem of mainstream economic models - their complexity.

There are a couple of major complications associated with complex mathematical models. The first major problem are the parameters and variables themselves. With thousands of parameters and variables it is almost impossible to monitor or assess them correctly. Typically, economists try to minimize the error on key variables only. The major consequence of this problem - inability to see the whole picture. Every physicist or mathematician knows that it is easy to get lost even in the system of 3-4 equations.

However, the biggest problem is the derivation of these models. In natural sciences many of these equations are derived from the first principles (e.g. Maxwell’s equations, gravitation law) which were tested in countless experiments. Thus, these equations allow creating extremely accurate models that guide ballistic missiles and satellites.

Predictability of other natural science models which are not based on first principles (e.g. forecasting of earthquakes and weather) are much less accurate; however, they typically have enormous databanks for testing and improving their models.

Social science models, however, lack both rigorous first principles and extensive data (it looks like we have tones of economic information, however, i) its amount is not adequate to the complexity of the system, ii) this information is much less accurate and consistent than one typically used in natural science models).

In these circumstances it is possible (and what economists actually do) to develop a model by linearizing a system in some vicinity of an equilibrium state. Thus, such model may have predictive power in some narrow range of parameters. It may run pretty smoothly if the most important parameters change insignificantly and smoothly (in this case you can adjust the model to a new state using the same set of data)- e.g. economy between two recessions.

However, with a system of 1250 equations you have thousands of parameters which almost impossible to monitor or assess correctly. And due to construction of the model, if even one important parameter or variable is changed significantly you have to re-adjust the whole model, because the system can leave the previous equilibrium state and move to a new one. However, the problem is that we don’t have factual data to feed this model at a new state. Particularly, referring to the current situation we never had a credit slump, housing bubble and $147-a-barrel oil simultaneously.

A good example of economic forecasting models is the so-called Fairmodel, a publicly available (http://fairmodel.econ.yale.edu) econometric model created and maintained by Professor Ray C. Fair of Yale University. The Fairmodel is a compilation of multiple regression equations based on historical data collected from 1952 and established relationships among these variables. This model produced accurate predictions for many years, however, all recent forecasts were overly optimistic.

In summary, we can conclude that most of mainstream economic models do not have predictive power in forecasting turning points or unusual market events. What is the reason why they run smoothly under the normal circumstances but failed to predict the current recession and constantly fail to predict the correct dynamic of the recession? They were developed and trained on a completely different (and irrelevant to the current situation) dataset.

Roots of Crisis: Consumer Spending vs. Saving

The recent drop in the U.S. GDP (in 0.5% in 3rd and 6.2% in 4th quarters of 2008) is casually connected with the plunge in consumer spending. The government and Feds are trying to restore the financial system in order to rejuvenate credit and boost spending. Bouncing back of consumer spending is the vital part of the economy recovery; without it any stimulus package will not be able to put the economy back on track.

The only problem – it is impossible. Consumer spending CANNOT be restored at the pre-crisis level in any near future. On the next figure reproduced from from the Bureau of Economic Analysis website (U.S. Department of Commerce) it is easy to see that over years 90% of DPI (disposable personal income= personal income after taxes) were spent while remaining 10% were used for saving.




This situation started changing dramatically around 1993, when the share of saving started decreasing steadily. In 2007 savings reached astonishing 0.5% ($57.4bln out of $10,170.5bln) of disposable income. So, 99.5% of all money were well spent. No provisions on safety net; no retirement planning; just buying new houses, cars and home theaters.

Is it reasonable to expect that people will stick to this pattern in the current grueling economic situation? I don’t think so. It is much more logical to expect that they will return to a traditional 90/10 pattern, which means $1,000bln drop in spending. This "traditional" pattern will probably persist at least for some period of time required to restore consumer confidence and, thus, the associated drop in spending ($1,000bln annually) cannot be offset by any reasonable stimulus package.

In the next article we will consider why this regime change was overlooked by economists.