Posted inCulture

Ron Paul’s Moving, Truthful, Necessary Last Speech

Ron Paul, patriot
Editor’s note. It appears Paul has an unfortunate sordid past, and that he attracts more than his share of lunatics, and that he has the penchant to exaggerate and to see spurious boogeymen. He is, therefore, like we all are, a flawed man. But even accepting the worst of that does not change the truth in this speech. The length of the necessary reading will tax many. A video of the speech is here. A PDF is here.

This may well be the last time I speak on the House Floor. At the end of the year I’ll leave Congress after 23 years in office over a 36 year period. My goals in 1976 were the same as they are today: promote peace and prosperity by a strict adherence to the principles of individual liberty.

It was my opinion, that the course the U.S. embarked on in the latter part of the 20th Century would bring us a major financial crisis and engulf us in a foreign policy that would overextend us and undermine our national security.

To achieve the goals I sought, government would have had to shrink in size and scope, reduce spending, change the monetary system, and reject the unsustainable costs of policing the world and expanding the American Empire.

The problems seemed to be overwhelming and impossible to solve, yet from my view point, just following the constraints placed on the federal government by the Constitution would have been a good place to start.

In many ways, according to conventional wisdom, my off-and-on career in Congress, from 1976 to 2012, accomplished very little. No named legislation, no named federal buildings or highways—thank goodness. In spite of my efforts, the government has grown exponentially, taxes remain excessive, and the prolific increase of incomprehensible regulations continues. Wars are constant and pursued without Congressional declaration, deficits rise to the sky, poverty is rampant and dependency on the federal government is now worse than any time in our history.

All this with minimal concerns for the deficits and unfunded liabilities that common sense tells us cannot go on much longer. A grand, but never mentioned, bipartisan agreement allows for the well-kept secret that keeps the spending going. One side doesn’t give up one penny on military spending, the other side doesn’t give up one penny on welfare spending, while both sides support the bailouts and subsidies for the banking and corporate elite. And the spending continues as the economy weakens and the downward spiral continues. As the government continues fiddling around, our liberties and our wealth burn in the flames of a foreign policy that makes us less safe.

The major stumbling block to real change in Washington is the total resistance to admitting that the country is broke. This has made compromising, just to agree to increase spending, inevitable since neither side has any intention of cutting spending.

The country and the Congress will remain divisive since there’s no “loot left to divvy up.”

Without this recognition the spenders in Washington will continue the march toward a fiscal cliff much bigger than the one anticipated this coming January.

I have thought a lot about why those of us who believe in liberty, as a solution, have done so poorly in convincing others of its benefits. If liberty is what we claim it is- the principle that protects all personal, social and economic decisions necessary for maximum prosperity and the best chance for peace- it should be an easy sell. Yet, history has shown that the masses have been quite receptive to the promises of authoritarians which are rarely if ever fulfilled.

If authoritarianism leads to poverty and war and less freedom for all individuals and is controlled by rich special interests, the people should be begging for liberty. There certainly was a strong enough sentiment for more freedom at the time of our founding that motivated those who were willing to fight in the revolution against the powerful British government.

During my time in Congress the appetite for liberty has been quite weak; the understanding of its significance negligible. Yet the good news is that compared to 1976 when I first came to Congress, the desire for more freedom and less government in 2012 is much greater and growing, especially in grassroots America. Tens of thousands of teenagers and college age students are, with great enthusiasm, welcoming the message of liberty.

I have a few thoughts as to why the people of a country like ours, once the freest and most prosperous, allowed the conditions to deteriorate to the degree that they have.

Freedom, private property, and enforceable voluntary contracts, generate wealth. In our early history we were very much aware of this. But in the early part of the 20th century our politicians promoted the notion that the tax and monetary systems had to change if we were to involve ourselves in excessive domestic and military spending. That is why Congress gave us the Federal Reserve and the income tax. The majority of Americans and many government officials agreed that sacrificing some liberty was necessary to carry out what some claimed to be “progressive” ideas. Pure democracy became acceptable.

They failed to recognized that what they were doing was exactly opposite of what the colonists were seeking when they broke away from the British.

Some complain that my arguments makes no sense, since great wealth and the standard of living improved for many Americans over the last 100 years, even with these new policies.

But the damage to the market economy, and the currency, has been insidious and steady. It took a long time to consume our wealth, destroy the currency and undermine productivity and get our financial obligations to a point of no return. Confidence sometimes lasts longer than deserved. Most of our wealth today depends on debt.

The wealth that we enjoyed and seemed to be endless, allowed concern for the principle of a free society to be neglected. As long as most people believed the material abundance would last forever, worrying about protecting a competitive productive economy and individual liberty seemed unnecessary.

This neglect ushered in an age of redistribution of wealth by government kowtowing to any and all special interests, except for those who just wanted to left alone. That is why today money in politics far surpasses money currently going into research and development and productive entrepreneurial efforts.

The material benefits became more important than the understanding and promoting the principles of liberty and a free market. It is good that material abundance is a result of liberty but if materialism is all that we care about, problems are guaranteed.

The crisis arrived because the illusion that wealth and prosperity would last forever has ended. Since it was based on debt and a pretense that debt can be papered over by an out-of-control fiat monetary system, it was doomed to fail. We have ended up with a system that doesn’t produce enough even to finance the debt and no fundamental understanding of why a free society is crucial to reversing these trends.

If this is not recognized, the recovery will linger for a long time. Bigger government, more spending, more debt, more poverty for the middle class, and a more intense scramble by the elite special interests will continue.

Without an intellectual awakening, the turning point will be driven by economic law. A dollar crisis will bring the current out-of-control system to its knees.

If it’s not accepted that big government, fiat money, ignoring liberty, central economic planning, welfarism, and warfarism caused our crisis we can expect a continuous and dangerous march toward corporatism and even fascism with even more loss of our liberties. Prosperity for a large middle class though will become an abstract dream.

This continuous move is no different than what we have seen in how our financial crisis of 2008 was handled. Congress first directed, with bipartisan support, bailouts for the wealthy. Then it was the Federal Reserve with its endless quantitative easing. If at first it doesn’t succeed try again; QE1, QE2, and QE3 and with no results we try QE indefinitely—that is until it too fails. There’s a cost to all of this and let me assure you delaying the payment is no longer an option. The rules of the market will extract its pound of flesh and it won’t be pretty.

The current crisis elicits a lot of pessimism. And the pessimism adds to less confidence in the future. The two feed on themselves, making our situation worse.

If the underlying cause of the crisis is not understood we cannot solve our problems. The issues of warfare, welfare, deficits, inflationism, corporatism, bailouts and authoritarianism cannot be ignored. By only expanding these policies we cannot expect good results.

Everyone claims support for freedom. But too often it’s for one’s own freedom and not for others. Too many believe that there must be limits on freedom. They argue that freedom must be directed and managed to achieve fairness and equality thus making it acceptable to curtail, through force, certain liberties.

Some decide what and whose freedoms are to be limited. These are the politicians whose goal in life is power. Their success depends on gaining support from special interests.

The great news is the answer is not to be found in more “isms.” The answers are to be found in more liberty which cost so much less. Under these circumstances spending goes down, wealth production goes up, and the quality of life improves.

Just this recognition—especially if we move in this direction—increases optimism which in itself is beneficial. The follow through with sound policies are required which must be understood and supported by the people.

But there is good evidence that the generation coming of age at the present time is supportive of moving in the direction of more liberty and self-reliance. The more this change in direction and the solutions become known, the quicker will be the return of optimism.

Our job, for those of us who believe that a different system than the one that we have had for the last 100 years, has driven us to this unsustainable crisis, is to be more convincing that there is a wonderful, uncomplicated, and moral system that provides the answers. We had a taste of it in our early history. We need not give up on the notion of advancing this cause.

It worked, but we allowed our leaders to concentrate on the material abundance that freedom generates, while ignoring freedom itself. Now we have neither, but the door is open, out of necessity, for an answer. The answer available is based on the Constitution, individual liberty and prohibiting the use of government force to provide privileges and benefits to all special interests.

After over 100 years we face a society quite different from the one that was intended by the Founders. In many ways their efforts to protect future generations with the Constitution from this danger has failed. Skeptics, at the time the Constitution was written in 1787, warned us of today’s possible outcome. The insidious nature of the erosion of our liberties and the reassurance our great abundance gave us, allowed the process to evolve into the dangerous period in which we now live.

Today we face a dependency on government largesse for almost every need. Our liberties are restricted and government operates outside the rule of law, protecting and rewarding those who buy or coerce government into satisfying their demands. Here are a few examples:

Posted inStatistics

Two differences in perception between global cooling and global warming

As is well known by now, a passel of climatologists in the 1970s, including such personalities as Stephen “It’s OK to Exaggerate To Get People To Believe” Schneider, tried to get the world excited about the possibility, and the dire consequences, of global cooling.

From the 1940s to near the end of the 1970s, the global mean temperature did indeed trend downwards. Using this data as a start, and from the argument that any change in climate is bad, and anything that is bad must be somebody’s fault, Schneider and others began to warn that an ice age was imminent, and that it was mainly our fault.

The causes of this global cooling were said to be due to two main things: orbital forcing and an increase in particulate matter—aerosols—in the atmosphere. The orbital forcing—a fancy term meaning changes in the earth’s distance and orientation to the sun, and the consequent alterations in the amount of solar energy we get as a result of these changes—was, as I hope is plain, nobody’s fault, and because of that, it excited very little interest.

But the second cause had some meat behind it; because, do you see, aerosols can be made by people. Drive your car, manufacture oil, smelt some iron, even breath and you are adding aerosols to the atmosphere. Some of these particles, if they diffuse to the right part of the atmosphere, will reflect direct sunshine back into space, depriving us of its beneficial warming effects. Other aerosols will gather water around them and form clouds, which both reflect direct radiation and capture outgoing radiation—clouds both cool and warm, and the overall effect was largely unknown. Aerosols don’t hang around in the air forever. Since they are heavy, over time they will fall or wash out. It’s also hard to do too much to reduce the man-made aerosol burden of the atmosphere; except the obvious and easy things, like install cleaner smoke stacks.

Pause during the 1980s when nothing much happened to the climate.

Posted inStatistics

AMS conference report: day 3

More on hurricanes today. Jim Elsner, with co-author Tom Jagger, both from Florida State University started off by warning against using naive statistical methods on count data, such as hurricanes.…
Posted inStatistics

Example of how easy it is to mislead yourself: stepwise regression

I am, of course, a statistician. So perhaps it will seem unusual to you when I say I wish there were fewer statistics done. And by that I mean that I’d like to see less statistical modeling done. I am happy to have more data collected, but am far less sanguine about the proliferation of studies based on statistical methods.

There are lots of reasons for this, which I will detail from time to time, but one of the main ones is how easy it is to mislead yourself, particularly if you use statistical procedures in a cookbook fashion. It takes more than a recipe to make an eatable cake.

Among the worst offenders are methods like data mining, sometimes called knowledge discovery, neural networks, and other methods that “automatically” find “significant” relationships between sets of data. In theory, there is nothing wrong with any of these methods. They are not, by themselves, evil. But they become pernicious when used without a true understanding of the data and the possible causal relationships that exist.

However, these methods are in continuous use and are highly touted. An oft-quoted success of data mining was the time a grocery store noticed that unaccompanied men who bought diapers also bought beer. A relationship between data which, we are told, would have gone unnoticed were it not for “powerful computer models.”

I don’t want to appear too negative: these methods can work and they are often used wisely. They can uncover previously unsuspected relationships that can be confirmed or disconfirmed upon collecting new data. Things only go sour when this second step, verifying the relationships with independent data, is ignored. Unfortunately, the temptation to forgo the all-important second step is usually overwhelming. Pressures such as cost of collecting new data, the desire to publish quickly, an inflated sense of certainty, and so on, all contribute to this prematurity.

Stepwise

Stepwise regression is a procedure to find the “best” model to predict y given a set of x’s. The y might be the item most likely bought (like beer) given a set of possible explanatory variables x, like x1 sex, x2 total amount spent, x3 diapers purchased or not, and on and on. The y might instead be total amount spent at a mall, or the probability of defaulting on a loan, or any other response you want to predict. The possibilities for the explanatory variables, the x’s, are limited only to your imagination and ability to collect data.

A regression takes the y and tried to find a multi-dimensional straight line fit between itself and the x’s (e.g., a two-dimensional straight line is a plane). Not all of the x’s will be “statistically significant1“; those that are not are eliminated from the final equation. We only want to keep those x’s that are helpful in explaining y. In order to do that, we need to have some measure of model “goodness”. The best measure of model goodness is one which measures how well that model does predicting independent data, which is data that in no way was used to fit the model. But obviously, we do not always have such data at hand, so we need another measure. One that is often picked is the Akaike Information Criterion (AIC), which measures how well the model fits the data that was used to fit the model.

Confusing? You don’t actually need to know anything about the AIC other than that lower numbers are better. Besides, the computer does the work for you, so you never have to actually learn about the AIC. What happens is that many combinations of x’s are tried, one by one, an AIC is computed for that combination, and the combination that has the lowest AIC becomes the “best” model. For example, combination 1 might contain (x2, x17, x22), while combination 2 might contain (x1, x3). When the number of x’s is large, the number of possible combinations is huge, so some sort of automatic process is needed to find the best model.

A summary: all your data is fed into a computer, and you want to model a response based on a large number of possible explanatory variables. The computer sorts through all the possible combinations of these explanatory variables, rates them by a model goodness criterion, and picks the one that is best. What could go wrong?

To show you how easy it is to mislead yourself with stepwise procedures, I did the following simulation. I generated 100 observations for y’s and 50 x’s (each of 100 observations of course). All of the observations were just made up numbers, each giving no information about the other. There are no relationships between the x’s and the y2. The computer, then, should tell me that the best model is no model at all.

But here is what it found: the stepwise procedure gave me a best combination model with 7 out of the original 50 x’s. But only 4 of those x’s met the usually criterion for being kept in a model (explained below), so my final model is this one:

explan. p-value Pr(beta x| data)>0
x7 0.0053 0.991
x21 0.046 0.976
x27 0.00045 0.996
x43 0.0063 0.996

In classical statistics, an explanatory variable is kept in the model if it has a p-value< 0.05. In Bayesian statistics, an explanatory variable is kept in the model when the probability of that variable (well, of its coefficient being non-zero) is larger than, say, 0.90. Don't worry if you don't understand what any of that means---just know this: this model would pass any test, classical or modern, as being good. The model even had an adjusted R2 of 0.26, which is considered excellent in many fields (like marketing or sociology; R2 is a number between 0 and 1, higher numbers are better).

Nobody, or very very few, would notice that this model is completely made up. The reason is that, in real life, each of these x’s would have a name attached to it. If, for example, y was the amount spent on travel in a year, then some x’s might be x7=”married or not”, x21=”number of kids”, and so on. It is just too easy to concoct a reasonable story after the fact to say, “Of course, x7 should be in the model: after all, married people take vacations differently than do single people.” You might even then go on to publish a paper in the Journal of Hospitality Trends showing “statistically significant” relationships between being married and travel model spent.

And you would be believed.

I wouldn’t believe you, however, until you showed me how your model performed on a set of new data, say from next year’s travel figures. But this is so rarely done that I have yet to run across an example of it. When was the last time anybody read an article in a sociological, psychological, etc., journal in which truly independent data is used to show how a previously built model performed well or failed? If any of my readers have seen this, please drop me a note: you will have made the equivalent of a cryptozoological find.

Incidentally, generating these spurious models is effortless. I didn’t go through 100s of simulations to find one that looked especially misleading. I did just one simulation. Using this stepwise procedure practically guarantees that you will find a “statistically significant” yet spurious model.

1I will explain this unfortunate term later.
2I first did a “univariate analysis” and only fed into the stepwise routine those x’s which singly had p-values < 0.1. This is done to ease the computational burden of checking all models by first eliminating those x's which are unlikely to be "important." This is also a distressingly common procedure.