For some time, heteroskedasticity has been the code-word around my office for the more opaque concerns of econometrics. Who beyond the relatively small community of professionals who specialize in statistical inference cares about T ratios and P values and chi squares anyway? I’ve always believed that clever applications of economic theory, expressed in models, and illustrated by dominating examples, are the path to enlightenment.

Sufficiently strongly about this did I feel last week, when, running down a list of Old Salts and Not-So-Old-Salts in economics who I thought might be particularly interesting to readers, I put Clive Granger on the list and then took him off again, settling for closer-to-home Christopher Sims instead.

Clearly Granger was important to other econometricians, I thought, and to some macroeconomists who used his tools, but not to the kinds of people who read this weekly. If the Swedes got around to him, I would write the story when they did.

And Robert Engle? A lengthy and enthusiastic article last spring in The New York Times Magazine told how New York University was “beefing up its roster [in economics] to make a run at the pennant by adding a core of solid role players and a Hall of Fame pitcher [Thomas Sargent] in the prime of his career” — but failed to so much as mention Engle, even though the trophy-scholar had been expensively lured to NYU’s business school from the University of California at San Diego four years before.

So even though Granger has been working for years to get a handle on the dynamics of the deforestation of the Amazon rain forest (and Engle is a big noise on Wall Street), these are the kinds of guys who, when they win Academy Awards, receive their Oscars at a separate dinner a few weeks before the big night. Or so I thought.

Score another for the Swedes. Last week Granger and Engle were named winners of the 36th Nobel Award in Economics for the tools they developed for interpreting “time series” data — cointegration in Granger’s case, autoregressive conditional heteroskedasticity (ARCH) in Engle’s case. More than ever, it came clear what the committee that nominates the prizewinners recently has been trying to say.

It was the third time in four years that the award was given for contributions to the tool-kit of empirical economists — applied microeconomic analysis one year, experimental techniques the next, forecasting methods after that. In between, a prize for “lemons” — three economists who were among the first to successfully capture market structure with price theory by concentrating on the role of information.

The committee seems to be buttressing the case for the Nobel award itself.

It is, of course, always possible that the timing of the award — just three years after another prize, to James Heckman and Daniel McFadden, for the making of econometric tools for microeconomics — resulted from an inability to agree on some other course of action.

But coming so quickly on the heels of the earlier award, this year’s prize may be directed less at the lay public, which is always hoping to understand what is going on in economics, than at the award’s real constituency — the scientists of the Royal Swedish Academy of Sciences, mainly physical scientists, who actually vote the award.

Remember, economics was riding pretty high in popular esteem when the Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel was established in 1969 (that is its official name). The economics award is the only addition to the roster of five prizes established in 1895 by the will of Alfred Nobel — physics, chemistry, medicine or physiology, literature and peace

It was established to commemorate the 300th anniversary of the founding of the world’s first central bank — an invention by practical men that slowly evolved in the direction of successful theory, rather like, say, the study of electricity. The first few prizes established clearly what the Swedish Academy meant when it said “economics.” And those awards confirmed, at least in many minds, the authority of the prize (if not necessarily all the fine points of its emphases).

Ragnar Frisch of Norway and Jan Tinbergen of the Netherlands were honored first in 1969, as pioneers of the newfangled combination of economic theory, statistics and mathematics known as econometrics. Then in 1970 came polymath Paul Samuelson; measurement economist Simon Kuznets in 1971; theorists Kenneth Arrow and John Hicks in 1972.

In the 1970s, the prestige of economics began to slip somewhat, and with it the reputation of the prize, as the world economy entered what seemed to be a period of dangerous instability.

In 1974 the prize was shared equally by Friedrich Hayek and Gunnar Myrdal — two remarkable men of generally opposing political points of view, more nearly philosophers of economics than exemplars of the slice-it-thin and nail-it-down style of scientific advance. Yet when the Swedish Academy did reward tool-makers or successful theorists, their work was dismissed, at least in some quarters, as obvious or trivial.

When, in the late 1980s, the prize was awarded consecutively to two European thinkers (Trgyve Haavelmo and Maurice Allais) for contributions they had made more than 40 years before, some worried that the committee was losing its touch.

Then in 1993, leadership among the economists in the Swedish Academy passed to a new generation, and within a year the Nobel Award in Economics was painting a very different history of the field. In 1994 came the first prize ever for game theory. The electrifying decision to honor John Nash not only changed the way the history of economic thought was written (though it hardly wrote the final draft); it sparked an eventual hit movie as well.

Next came Robert Lucas, whose successful incorporation of expectations into dynamic models brought psychology back to economics, and served as the hinge on which all subsequent developments in macroeconomics have turned. Recognized next were James Mirrlees and William Vickrey, economists whose models of asymmetric information laid the groundwork for any number of new auction strategies. They were followed by Robert Merton and Myron Scholes, whose option-pricing formula formed the basis for vast new global markets to diminish volatility and manage risk.

Then came slightly more philosophical nods to left and right, before the return to the emphasis on tools and their uses — Amartya Sen in 1998 for his highly practical theoretical work on ameliorating poverty, Robert Mundell in 1999 for the ideas that eventually informed the decision to create a common European currency.

Against this background, then, what about cointegration and autoregressive conditional heteroskedasticity? One sign of the renewed ascendancy of economics is the fact The Financial Times, The Wall Street, The New York times and The Washington Post had skilled and knowledgeable beat reporters available to write to the story.

The Journal’s Jon Hilsenrath, for example, got Harvard economist James Stock to illustrate the cointegration technique of analyzing relationships between variables this way: “…(F)or hundreds of years, Mars has been moving closer to Earth. During the same period, national income in the U.S. has been rising. A simple statistical regression model might show that as Mars gets closer to Earth, incomes rise. The Engle-Granger test [of causation] helps to prove statistically that there is no connection between these movements, while there might be connections in other movements, such as incomes and consumer spending, or long-term interest rates and short-term interest rates.”

Part of the significance of the cointegration technique, economists say, is that it originated in economics and now is being adopted by scientists in other fields, including weather forecasting. It is a claim that the citation doesn’t make.

In his Marshall Lectures 1998, Granger dwelt on the central problem of the evaluation of econometric models — a problem that, as he noted, has received almost no attention from economists themselves.

“Econometricians and empirical modelers in economic fields spend a great deal of effort in constructing their models. Appropriate data are gathered, alternative specifications considered, a good dose of economic theory inserted and the model is carefully estimated. The final model is then ready to be presented to the public like some exotic dish in an expensive restaurant. Just looking at the model, the natural question arises — is it any good?”

Any realistic evaluation must take account of the use to which the model is put, said Granger — not just to the quality of its inputs, but to the quality of its output, meaning its usefulness to the decision-maker. Does it really matter? Of course it does, he continued. Take the question of the deforestation rate in the Amazon region of Brazil.

Suppose this were framed as a matter of the supply and demand for wood. An elegant model could be specified and estimated. It would fit the data well. But pity the policy-maker who depended on it, since the driving force behind deforestation is not the demand for wood but rather the demand for land on which the trees grow.

And heteroskedasticity? True, the word shares a Greek root with skedaddle — skedannumi, meaning to scatter. But rather than describing the tendency of a mixed group of individuals to leave a single location all at once, heteroskedasticity refers to unequal variance among regression errors.

Think that variance doesn’t matter in everyday life? Consider the asymmetry of costs, says Granger — the difference between being ten minutes early for a flight vs. being ten minutes late. In Engle’s hands, computer-driven models of share-price volatility permitted financial economists to relax the traditional assumption that volatility was constant — with highly rewarding results.

Embodying more realistic assumptions (that volatility was related to its own past pattern, for example), ARCH models have provided guidance for risk managers of all sorts — none more than the Bank for International Settlements’ Committee on Banking Supervision, the Nobel citation pointed out, whose capital requirements for banks have themselves been calculated with ARCH models since 1996.

* * *

For all the novelty of what happened in California last week, it was not much different from what happened last year — for the fourth time, no less — in Massachusetts. Voters in a state in which the Democratic Party had become overwhelmingly dominant in the legislature chose to elect a Republican governor (three candidates have won a total of four terms in Massachusetts since 1990), hoping that divided government will prove an adequate defense against the tendency to runaway spending and self-dealing.

Indeed, for all his cartoonish ways and loathsome behavior, Arnold Schwarzenegger shares certain important characteristics with Massachusetts Gov. Mitt Romney. He was an accomplished and wealthy practitioner in one of the leading industries of his state — film, as opposed to venture capital. He is an outsider to the local culture — an Austrian immigrant instead of a Mormon.

He is a liberal on most social issues (though Romney has hopes of bring back the death penalty to Massachusetts). And he is well-advised, at least so far — the Hoover Institution’s George Shultz and moneyman Warren Buffet in California, Fidelity Management executive Robert Pozen and Conservation Law Foundation founder Douglas Foy in Massachusetts (local celebrities for a smaller state).

There are two key differences that may affect the outcome. Romney was raised in a political family. (His father George was governor of Michigan and a presidential aspirant.) Schwarzenegger merely married into one (the Kennedys). He may lack some of the fundamental instincts that are vital to politicians’ success.

Moreover, the fiscal situation in California is immeasurably worse than in Massachusetts. As Kent Smetters of the Wharton School (and former deputy assistant secretary for economic policy at the U.S. Treasury Department) pointed out in the Financial Times Friday, soon-to-be former-Gov. Gray Davis and the Democratic legislature resorted to a remarkable array of accounting tricks to produce a budget that was “only” $8 billion in the red.

Almost $20 billion in current spending has been shifted to next year through a variety of gimmicks, Smetters says, including a $10.7 billion bond issue to be paid off by “existing resources” that don’t exist.

But then that’s presumably why California voters elected the Republican movie star in the first place. He is a beneficiary, at least for the moment, of the fiscal rip tide that soon will be the dominant feature of American politics.

Perhaps the new governor will follow Smetters’ suggestion and make his top priority the creation of sound and transparent accounts for the state, which, after all, constitutes an economy the size of that of Germany. Good luck Arnold Schwarzenegger!