Correlation is a basic and widely-used statistical method.

Correlation be defined as either 1) the dependency between variables (e.g., Pearson’s correlation) or 2) or the association between rank order of two variables (Spearman rank correlation and Kendall’s tau), such that as X increases Y also increases or vice versa.

The most common form of correlation is known as Pearson’s correlation. Expressed as r, Pearson’s correlation looks at the linear associations between variables and are derived by dividing the covariance of two variables by each of their standard deviations:

r = cov(X,Y)/ sdXsdY

The range of a Pearson’s correlation is from -1 to 1, where -1 represents a perfect negative linear association and 1 is a perfect positive linear association. A value of 0 represents no linear association. The closer that r is to either extreme (-1 or 1), the stronger the correlation.

Original image by Wikipedia user Jtneill

Although two variables that are independent (and thus are not associated) will have a correlation of r = 0, a correlation of r = 0 does not necessarily mean that there is no association between the variables. Rather, this means that there is no linear association between X and Y. There may in fact be a quadratic relation, or an exponential relation, or some other data pattern that is informative however not linear.

With the S&P 500 and Nasdaq hitting year to date highs, it should not surprise currency traders that the dollar has extended its gains. Since the beginning of the month, the dollar appreciated more than 5 percent against the Japanese Yen and over 4 percent against the euro, Australian dollar and Swiss Franc. The only currency that has been stronger than the greenback is the Canadian dollar and even then the loonie’s gains have been marginal. Today, the loonie remains the only currency to strengthen against the dollar. The correlation between the foreign exchange and equity markets continue to dominate trading but it is important to realize that this is a new development on a quiet trading week with unusually low volume and market participants.

Understanding the Correlation Between Currencies and Equities
The correlation that everyone is focused on is the positive correlation between the dollar and stocks. There is an expectation that the U.S. dollar and stocks will rise in tandem on the belief that investors in both markets are banking on an accelerating U.S. recovery. It also means that the dollar is keying off U.S. fundamentals and not risk appetite as stronger U.S. data bolsters the confidence and attractiveness of dollar denominated assets. Although this may seem logical to many investors, it has not been the case for most of the year. In fact the illogical behavior of the dollar selling off on good data and rising on bad has dominated trading. The following table illustrates how the correlation between the S&P 500 and currencies has changed. Between last Thursday and today, the S&P 500 has had a 96 percent negative correlation with the EUR/USD and a near perfect correlation with USD/JPY. In other words, since last Thursday, a rally in U.S. equities has coincided with a sell-off in the EUR/USD and a rally in USD/JPY. However this is a new development because over the entire month, the correlation between stocks and currencies has been very weak. If we take a step back and look at the correlation between currencies and equities over the past 6 months, we can see that previously, the EUR/USD rose alongside equities. It remains to be seen whether this new correlation can hold and if it does, it would break a relationship that has lasted for most of the year.

Economic Data Preview and Review
Meanwhile the stronger existing home sales report completely offset the market’s reaction to the disappointing GDP report. After last month’s solid number, many people believed that the pace of improvement in the housing market would slow and even though it did, the 7.4 percent growth was extremely impressive. The number of units sold in the month of November hit 6.54 million, the highest since Feb 2007. This suggests that we could see similar strength in tomorrow’s new home sales report. Personal income, personal spending and revisions to the December University of Michigan consumer sentiment report are due for release tomorrow. Stronger numbers are expected all around. As for growth, the third release of GDP revealed that the U.S. economy expanded by only 2.2 percent in the third quarter, a far cry from the initial estimate of 3.5 percent. The details of the report pointed to weaker growth in personal consumption, gross private investment and government consumption. Personal consumption was revised from 2.9 to 2.8 percent while PCE was revised from 0.5 to 0.4 percent.

EUR/USD: CLOSING IN ON SUPPORT

It has been a bad month for anyone long euros. The currency has been on a one way downtrend with virtually no recovery. However, relief may be in sight with the EUR/USD closing in on a very important support level. The 1.4185/90 level represents the September low as well as the 200-day SMA and the second standard deviation Bollinger Band. Part of the reason why the Eurozone has not rebounded is because economic data has been light and the reports that we have been released did not help the euro. For example, German consumer confidence for January fell from 3.6 to 3.3. This week is a very quiet week in the Eurozone with no meaningful economic reports. The only economic releases on the calendar tomorrow are German import prices and French consumer spending. Meanwhile, the rebound in EUR/CHF has been far from impressive. Having hit a high of 1.4989 intraday, the currency pair is ending the NY session closer to its low. Thanks to the prior stabilization of the Swiss Franc, exports increased 1.6 percent in the month of November. However along with a 0.6 percent rise in imports, the trade surplus actually fell from 2.44B to 2.14B.

GBP/USD: UK ECONOMY REMAINS IN RECESSION

The British pound continued to weaken against the U.S. dollar, breaking its 1.60 support level in the process. Although GDP growth was revised upwards from -0.3 to -0.2 percent, the positive sentiment from the report was offset by the reality that the U.K. was the only major country that failed to grow in the third quarter. To the disappointment of policymakers, the country remains mired in recession. Unfortunately based solely upon the October trade numbers and the fourth quarter retail sales reports that we have seen so far, growth may have remained negative in Q4. The outlook for growth is a critical component of the Bank of England’s monetary policy decisions. Tomorrow, we will receive the minutes from this month’s central bank meeting which will indicate how many members favored keeping the Quantitative Easing Program unchanged. If the vote was relatively tight with a good number of monetary policy members voting for additional easing, the pound could come under further selling pressure. However if the vote was unanimous or if the tone of the minutes is slightly hawkish, the pound could easily rise back about 1.60. Aside from the GDP numbers, the current account balance was also released this morning with the deficit expanding marginally in the third quarter.

NZD/USD: GDP MISSES

Broad dollar weakness has pushed the Australian and New Zealand dollars lower but the Canadian dollar continued to buck the trend, rising for the third trading day in a row. Disappointments in both Australian and New Zealand economic data contributed to the underperformance of the Asian currencies. The New Zealand economy expanded by 0.2 percent in the fourth quarter, falling short of the market’s 0.4 percent forecast. For Australia, the Conference Board Leading index turned negative in October, signaling that the recovery in the Australian economy may be slowing. There was no economic data released from Canada but the rise in crude prices and the prospect of stronger GDP numbers tomorrow is helping to lift the CAD. The latest retail sales report indicates that consumer spending remains strong while the trade balance returned to surplus in October. According to Canadian Finance Minister Flaherty, there are no signs of a housing bubble at this time but if a bubble were to develop, he can tighten standards and make mortgages tougher to obtain.

USD/JPY: SIX DAYS OF PERSISTENT STRENGTH

For the sixth trading day in row, the U.S. dollar appreciated against the Japanese Yen, hitting a one month high in the process. In fact, the strength of the currency pair has pulled all of the other Japanese Yen crosses higher. Don’t forget that the reason why currency pairs such as the AUD/JPY are known as crosses is because their rate is dependent upon the value of USD/JPY and the AUD/USD. Japanese economic data also disappointed, which may have added pressure on the Yen. The latest report indicates that supermarket sales have been negative for12 months in a row while small business confidence dropped for the third consecutive month. Unlike large businesses, small companies have not benefitted from the global recovery. The divergence between the Tankan survey which measures the confidence of large businesses and Shoko Chukin report which surveys small businesses reflect Japan’s fundamental problems which is that stronger profitability in the corporate is not filtering into the rest of the economy. According to a Reuters poll, the sentiment amongst Japanese consumers has also fallen for the fourth consecutive month. There are no economic reports due from Japan tomorrow. The latest comment from Bank of Japan Governor Shirakawa that policy guided by short term price moves would destabilize the economy suggests that they have no plans to change monetary policy in term and economic fundamentals certainly support that.

USD/CAD: Currency in Play for Next 24 Hours

he currency in play for the upcoming next hours is USD/CAD. Canadian Monthly GDP data is on tap for tomorrow at 13:30GMT or 8:30AM EST. At the same time, the U.S. will announce Core PCE as well as Personal Spending and Personal income numbers. Shortly thereafter, the U. of Michigan Consumer Confidence and New Home Sales reports will be released at 15:00GMT or 10:00AM EST. Over the past 3 months, USD/CAD has been trapped in a range. The currency pair currently roams within the Range Trading Zone which we determine using Bollinger bands. A lack of trend and decrease in volatility has created an asymmetrical triangle that signals that a breakout is imminent. The upper boundary of the triangle acts as the key resistance level, particularly since it coincides with the 100-day SMA at 1.0680. Meanwhile support lingers at the 1st Standard Deviation which coincides with the bottom boundary of the same formation and the psychologically important 1.05 level.

As stated in a previous post on the same subject, when a country achieves a certain level of economic growth – or, more precisely, rising levels of GDP per capita because economic growth as such can be the result of rising population levels – it is assumed that this reflects a higher average standard of living for its citizens. Economic growth is therefore seen as an important tool in the struggle against poverty (if you wonder why poverty is a human rights issue, go here). If a country is richer in general, the population will also be richer on average. On average meaning that GDP growth isn’t necessarily equally distributed over every member of the population. That is why GDP growth isn’t sufficient proof of poverty reduction. Separate measurements of poverty and inequality are necessary.  

So in theory, you can have GDP growth and increasing levels of poverty, on the condition that GDP growth is concentrated in the hands of a few. However, that’s generally not the case. GDP growth benefits to some extent many of the poor as well as the wealthy, which is shown by the strong correlation between poverty reduction and levels of GDP growth (always per capita of course). It’s no coincidence that a country such as China, which has seen strong GDP growth over the last decades, is also a country that has managed to reduce poverty levels substantially.  

Unfortunately, growth isn’t a silver bullet. Poverty is a complex problem, requiring many types of solutions. Promoting economic growth will do a lot of the work, but something more is required. In a new paper, Martin Ravaillon gives the example of China, Brazil and India. The levels of poverty reduction in these three countries doesn’t mirror the levels of economic growth. Although half of the world’s poor live in these three countries, in the last 25 years China has reduced its poverty level from 84% of the population in 1981 to just 16% in 2005 (see chart below). China is exceptional, but Brazil also did well, cutting its rate in half over the same period (8% of Brazilians still live on less than $1.25 a day). Regarding India, there are some problems with its statistics, but whichever statistic you use, there’s a clear reduction.  

poverty reduction in China, India and Brazil

(source)

Ravaillon points out that the intensity of poverty reduction was higher in Brazil than in India and China, despite lower GDP growth rates.  

Per unit of growth, Brazil reduced its proportional poverty rate five times more than China or India did. How did it do so well? The main explanation has to do with inequality. This (as measured by the Gini index, also marked on the chart) has fallen sharply in Brazil since 1993, while it has soared in China and risen in India. Greater inequality dampens the poverty-reducing effect of growth. (source)  

Which is rather obvious: higher levels of income equality means a better distribution of the benefits of growth. So the “pro-growth strategy” against poverty is important but not enough, and should be combined with Brazilian type anti-inequality measures (focus on education, healthcare and redistribution).

Author : HADY, CHRISTIAN

Salah satu faktor yang menjadi bahan pertimbangan dalam pengajuan kredit adalah laporan keuangan yang telah diaudit oleh suatu kantor akuntan publik. Oleh karena itu penting bagi auditor untuk menjaga kualitasnya, agar tidak merugikan pihak yang memakai informasi laporan keuangan tersebut. Penelitian ini bertujuan untuk mengetahui hubungan antara persepsi analis kredit terhadap pelaksanaan audit yang berkualitas dari kantor akuntan publik dengan keputusan pemberian kredit. Penelitian ini dilakukan dengan cara menyebarkan kuesioner kepada para analis kredit dari bank yang ada di surabaya. Data diolah dengan menggunakan program SPSS versi 10 sehingga dapat mengetahui seberapa besar hubungan antara persepsi analis kredit terhadap pelaksanaan audit yang berkualitas dari kantor akuntan publik dengan keputusan pemberian kredit. Hasil dari penelitian ini menunjukkan ada hubungan antara persepsi analis kredit terhadap pelaksanaan audit yang berkualitas dari kantor akuntan publik dengan keputusan pemberian kredit. Tetapi dalam pembuktiannya terdapat satu hipotesis yang tidak memiliki hubungan dengan keputusan pemberian kredit, yaitu variabel persepsi analis kredit terhadap pemahaman yang memadai atas struktur pengendalian internal dari akuntan.

Keyword : spss, correlation, auditing standart

Sumber : http://repository.petra.ac.id/2302/

Author : MARGIE, ERIDYA

Ketidakpuasan dalam metode akuntansi tradisional dalam menilai kinerja perusahaan telah melahirkan beberapa metode bam yang modem, salah satu diantaranya adalah metode EVA. Terdapat suatu perdebatan hangat di kalangan praktisi dunia bisnis mengenai manakah metode yang berkorelasi terbesar dengan saham perusahaan, apakah itu dengan metode yang modem atau dengan metode akuntansi tradisional. Perusahaan-perusahaan dalam industri food and beverages menjadi pilihan karena pangan mempakan salah satu kebutuhan pokok manusia, yang menjadikan perusahaan-perusahaan dalam industri ini akan tems bertahan di masa krisis ekonomi sekalipun.

Keyword : eva, tradisional accounting method, correlation, stock, food and beverages

Sumber : http://repository.petra.ac.id/1882/

Conservative plans to incentivize marriage with a £20 a week tax break for married couples are ridiculous.

Firstly, it seems bizarre for proponents of marriage to make what they see as a sacred union more of a callous financial choice. Second, it is totally crazy given the economic mess we’re in that an incentive that would cost upward of £3.2 billion (the figure £3.2 billion ruminated in the summer, although The Spectator now cites a House of Lords debate which suggests it’d cost £4.9 billion a year). Spending so much is all the more foolish when the rewards marriage supposedly reaps in the form of kids’ better educational performance and decreased likelihood of taking drugs are a) in the future, and b) very difficult to calculate anyway. This leads me to my final point that the proposal is founded on the idiotic assumption that a wedding band on a finger makes a relationship good, and that it is this that causes children of said relationship to not do drugs, do better in school, not become dependent on benefits and be an all-round stalwart citizen.

Peter Hoskin for The Spectator is sceptical of the effect of  “deploying fiscal incentives to force something which should largely be a private decision, based on sappier motives such as love.” He later asks, in response to an avid marriage incentiviser, how many people would consider marrying, or not divorce as readily, for £20 a week and suggests the costly plan may only have a very slight social effect. Yet I think it’d be wrong to dimiss the effects that financial considerations can have on the decision to marry.

“I married for money” reads the headline of Eleanor Mills’ column in The Sunday Times today. She makes a good point. People do marry for money; not necessarilly for financial gain, but for considerations like pension entitlements. They also do not marry for financial concerns;  today the cost of “buying a house or having a child is the priority, with marriage seen as an aspirational extra, a great excuse to throw a party, not a necessity.”

Indeed, a couple can live together now as if they were married, without being married. They can be committed to each other, have children and be mortgaged to the hilt. Eleanor Mills claims getting married eventually made her feel more secure and settled. I will not suggest marriage does not have this affect on people. Yet there is no way of proving the hypothetical of how she may have felt several years on with her partner had they not married, the quality of their relationship and the quality of their relationship with their children.  It’d be safe to say that apart from a few legal benefits, the state of being married does very little to change the relationship of a couple than when they were not married but living together.

Supporters of marriage incentives can look to statistics which suggest marriage makes people better, but I can assure you the cause of the social problems they highlight are more complicate than whether mummy and daddy were married. One particularly irksome set of stats brought up to lambast lone parents, is a piece of research by YouGov which found:

children brought up in lone parent families are:

75% more likely to fail at school
70% more likely to be a drug addict
50% more likely to have an alcohol problem
35% more likely to experience unemployment / welfare dependency

This is what is used as ammunition in the arguement against lone parents and for marriage incentives. But correlation is not causation! Lone parents do not cause their children to fail at school, it is far more likely to be the case that a majority of lone parents are relatively less educated themselves and live in areas where the quality of schooling is poor. It is these variables that bear more on educational performance. It is also likely that not performing well at school and leaving without qualifications have a significant impact on the likelihood of becoming a lone parent. As multiple TV documentaries on teenage pregnancy attest, lone parenthood is a conscious, assertive decision that some girls make in the wake of very limited prospects elsewhere. See also the Joseph Rowntree report on ‘Planned’ Teenage Pregnancy.

What is also wong is equating children with unmarried parents with lone parents. Stats on lone parents do not then make such a compelling argument for marriage. They may instead make a more solid case for couples to raise children. And indeed, the added emotional support a partner can have for a parent should not be underestimated. But there are also many types of “lone parent”, from fortysometing singletons with adopted kids and widows, not all are the feckless kind who inhabit the set of Jeremy Kyle and marriage incentivists’ imaginations. Look in the differences in these lone parents, take into consideration poverty and the quality of schooling and housing, and I think you’d have a better idea of the cause of negative child outcomes.

Taking this into consideration, it should become clear that marriage is not a panacea. Marriage per se is not what matters, but strong relationships and financially stability – and these are the very factors that tend to precede marriage.

Why don’t we encourage this? Make it easier for couples to set up home, earn a decent living, and for young people to leave school with a sense of selfworth and job prospects. We’d be better off all round. Much better than providing a tax break to a dastardly philanderer, as Harriet Harman put it, on his third marriage whilst wife number one and two bringing up his kids are left out of pocket.

(This is a follow-up from a previous post).

There aren’t many questions in political science that are more important than this one: which are the factors that determine whether a country becomes or doesn’t become a democracy, and that determine the degree to which a country is democratic. There are two reasons why this question is important:

• Democracy is an important good. I gave some arguments for this statement here. Hence it’s important to know what facilitates or hinders the realization of this good.
• Countries act on this statement in their foreign policy. For example, part of the rationale for invading Iraq was the conviction held by the U.S. administration of the time that promoting democracy in Iraq was both an intrinsic good and in the interest of the U.S. (see here as well).

I gave a short and non-exhaustive list of possible factors promoting/undermining the development/survival of democracy here. In the current post I want to focus on two of them: education levels and income or prosperity levels.

1. Education

education and democracy correlation

(source)

This graph compares the Polity IV Democracy Index scores for the countries of the world (average scores during the 1960-2000 period), with the average years of schooling of the adult population in 1960. And there’s obviously a correlation, and the quote below gives an indication about the direction of correlation:

The chart above shows the 77 percent correlation between education levels in 1960 (measured by the average years of schooling in a country as estimated by Robert Barro and Jong-Wha Lee), and the subsequent 40-year average of the Polity IV democracy index. That democracy index runs from zero to 10, where countries with index values less than three don’t look remotely democratic and countries with index values of about seven are reasonably well-functioning democracies.

One way to read the graph is that there are basically no countries with very low levels of education that have managed to be democratic over the long term, and almost every country with a high level of education has remained a stable democracy.

Thomas Jefferson

Thomas Jefferson wrote that “if a nation expects to be ignorant and free, in a state of civilization, it expects what never was and never will be.” In 1960, 36 nations had less than 1.74 years of schooling (which happens to be the level that Afghanistan has today). Of those 36 countries, only two — India and Botswana — managed to have average democracy scores above 4.2.

Out of the 19 countries in this sample with more than 5.3 years of schooling (the current level in Iran) in 1960, 17 have average democracy scores above 7.9. Fifteen of these have been perfectly democratic, at least by the standards of Polity IV. Only Poland and Hungary were dictatorships, and one can certainly argue that those places would have been democracies in 1960s if it were not for Soviet troops.

But in the middle ranges of education, between two and five years on average, almost anything goes. Some places, like Costa Rica and Italy, have been extremely democratic, while others, like Kuwait and Paraguay, have not. Iraq falls into this category today, which suggests a fair amount of uncertainty about that country’s political future.

Why do I think that the chain of causality runs from education to democracy rather than the reverse? Democracy in 1960 is essentially uncorrelated with subsequent growth in the levels of education. Education in 1960, on the other hand, does an extremely good job of predicting increases in democracy.

Why is there a connection between human capital and freedom? Giacomo Ponzetto, Andrei Shleifer and I have argued that the connection reflects the ability of educated people to organize and fight collaboratively. Dictators provide strong incentives for the ruling clique; democracies provide more modest benefits for everyone else. For democracy to beat dictatorship, the dispersed population needs to have the skills and motivation to work collaboratively to defeat dictatorial coups and executive aggrandizement.

Education teaches skills, like reading and writing, that enable people to work collaboratively. At younger grades, teachers spend a lot of time teaching children how to get along. In the United States, education is strongly linked to civic engagement and membership in social groups. The ability to work together enables the defense of democracy. Edward L. Glaeser (source)

2. Income

There’s an interesting paper here examining the causal relation between democracy and income. The authors find that

the level of national income provides the most important factor explaining inter-country variations in the degree of democracy with the consequence that low income is the most important barrier to democracy.

They first present the correlation between income and democracy, using not the Polity IV index but the Gastil/Freedom House index (see also here):

democracy and income correlation

(source)

The authors have two reasons to believe that the causal link goes from income to democracy rather than the other way around:

• Initial income in 1971 correlates with average democracy scores during the 1972-2005 period. This approach is similar to the one above in the case of education and democracy.
• And – simultaneously – there doesn’t seem to be a very strong causal link going from democracy to income. Barro has concluded that the degree of democracy is only a minor variable explaining income levels. So there is only a weak causal link going from democracy to income (see also here*). This means that the strong correlation shown in the graph above must be explained by a causal link going from income to democracy.

Why do higher levels of income promote the development of democracy? I gave an overview of the reasons here but some of the more important ones are:

• Higher education levels in a population means a higher probability of contestation. Following the Maslow hierarchy of needs it’s natural to expect the appearance of political needs once more basic needs have been secured.
• More income means more complex production. This in turn means that governments find it harder to impose central control over their economies.

Obviously, income is just one of many factors determining the development of democracy. It’s an important one, but clearly not sufficient. The graph above shows the Muslim countries separately. As you can see, all non-Muslim countries with high income levels are in the “high level of democracy” range. Affluent Muslim countries, however, aren’t. This indicates that affluence in itself promotes but doesn’t determine the development of democracy. Other factors are also in play. Culture and religion are perhaps some of them. It’s often argued that Islam is incompatible with democracy, or at least slows down the development of or transition to democracy. I’ll come back to this controversial topic another time.

* One can argue that the link would be stronger if democracies would be of better quality, see here.

The Pragmatic Capitalist blog has posted a chart that shows the high degree of correlation between the inverse of initial jobless claims and the S&P 500:

This is a highly interesting chart, as it shows just how important jobs (or the lack of jobs) have been to the market during this recession.

The correlation here is astonishing; frankly far higher then I would have expected given the recent reaction to jobs reports that the market has basically shrugged off.

I would expect this correlation to continue, as consumer confidence and jobs are the last remaining economic pedestal on which the bears can hang their hat, and if these initial jobless claims can continue their decline, we should see further upside in the market.

- AH

Disclosure: No stocks mentioned, but long the market.

Visualization is considered to be one of the valuable tools in data mining. Visual analysis helps to understand data with minimum effort. Using graphs/ 3D plots to visualize data is more effective way of understanding massive sets of data. Ofcourse, we have other ways of exploring data like for example using statistical functions namely mean which depicts the average value, standard deviation / variance which depicts the spread of data and correlation which depicts the relationship between attributes. Some of the classical visualization techniques include

(i) Histograms (ii) Scatter plots (iii) Pie charts

              A frequency histogram displays the distribution of values for attributes by dividing the possible values into bins and showing the number of objects/records falling in each bin. Scatter plot is a great way to visualize paired numeric attributes. For example, you have two attributes height and weight. Scatter plot can visually represent the correlation of height wrt weight. It may indicate facts like “As the height increases, weight also increases” or “As the height increases, weight decreases”. Data mining techniques use scatter plots to identify redundant attributes which can be dropped from analysis.
             Newer visualization techniques are evolving with growing business needs and need to minimize efforts for decision making. Scientists use visual analysis to explore previously unknown patterns in their research/simulation data. Hence visualization has gained wide acceptance across all spheres of life. Some visualization techniques which I really admire about are the geo-spatial visualization and word clouds.

• Geo Spatial Data: Consider the average energy consumption per person data for various regions of the world. In this scenario, it is more meaningful to visualize the energy data against the geography to get some quick facts about data. The figure below shows the visualization. The bigger the circles and the darker the shade, indicates that energy consumption is high. On contrary, lighter shade with smaller circles indicate relatively low energy consumption.
  

  geo spatial Data Analysis : Energy consumption data

• Word Cloud Analysis: I am sure, you must have seen this visualization when you visited any site like torrentz, pdf-geni, rapidsearch,etc. A word cloud indicates the frequency of word usage as a function of font/ color of the text. Bigger the font and darker the shade, the more frequent it appears in a given data set. The figure below shows the word cloud analysis of Lincoln’s speech. As seen from the visual, Lincoln more frequently used words like people, government, constitution, etc as these are relatively bigger in font. The figure on the right shows the word cloud analysis for this blog. There are some online tools to get the word cloud constructed for you blog/document – http://tagcrowd.com/

Word Cloud Visualization

Word cloud analysis of this blog

There is a classical example where visualization technique helped identify the reason for cholera outbreak in London. On visualizing the chorela affected houses, it was evident that those people nearer to a pond had developed cholera and those away from this pond had lesser probability. On inspection of the water samples collected from the pond, it showed contamination. Hence using visualization a major problem was solved.
                      In a recent article published in leading magazine, i had learnt that expert systems(AI) are visually repesenting the probable reason for health problem by highlighting the affected organ(s) in 3 dimensions with ability to drill down to the microscopic levels( based on medical test data). All these facts, prove visualization to be effective tools.
                   Please add your suggestions(if any) in the comments section. If you find this blog interesting, please subcribe to this blog.

This post tries to unsettle some of the methods used in economics today – Regression analysis and Granger causality. Apart from this objective, the post also tries to understand the meaning (rather, meanings) of causality. Do we economists mistake correlation for causality? Can we have a single method for capturing causality?

Causation is defined in the following ways:

-the action of causing or producing.

-the relation of cause to effect; causality

-anything that produces an effect; cause [Dictionary.com]

And the definition of Correlation is:

-mutual relation of two or more things, parts, etc. [Dictionary.com]

I once asked a professor who had offered to give a lecture for our Advanced Economic Theory class this: “Sir, is it possible to establish causation conclusively?” He replied “That is simple. There are these tests- Granger test, Sims test, Sargent test, McClave-Hsiao test, Haugh-Pierce test, etc.” And he wrote the names of these so-called ’scientific’ tests on our black board.

What we often forget is that, there is no single and simple understanding of causation. There are various kinds of causality like epistemic, conceptual, factual, counterfactual etc. Causality in economics also are of different natures- poverty is causing unemployment, increased demand for oil has resulted in oil price rise, supply constraints are hiking up the price, etc. For instance, conceptually we know that poverty causes unemployment (vicious cycle of poverty) and that increased demand causes a concomitant price rise. In economics, it is important to have an account of both conceptual as well as factual causality.

For causality to be present between two variables A and B, it is necessary for them to be related in some way. This relationship among them can be of a linear nature or a non-linear one. If it is linear in nature, then it is called correlation. [Note that regression analysis (OLS) is based on correlation and is linear in nature.] But, correlation alone does not imply causation. Hence, all those who think that causation and correlation are the same make an inductive leap – from correlation to that of causation.

As R G D Allen writes in his Statistics for Economics: “This statistical concept of correlation is quite neutral as regards causation. One of the variables may be ’caused’ by the other, but this can only be known from other than statistical considerations.” Usually, causal hypotheses are derived from economic theory, because ‘data does not speak on its own’. We need to pass data through theory in order to make sense of it.

However, economic theory (like any other theory) contains a lot of assumptions, mostly unrealistic. What happens to causality then? Causality, then is dependent on these assumptions. Hence, drawing inferences from such theoretical models for practical purposes should be undertaken with caution.

It is this caution that seems to be missing amongst econometricians. This will be evident after looking into the workings of Granger causality. Granger causality analyses probabilistic causality. However, this per se is not a limitation of the test. For, in social sciences, it is exceedingly difficult to talk about deterministic causes in real-world scenario- especially, in a macroeconomic environment.

Granger causality
If X and Y are probabilistically dependent and X precedes Y, then X causes Y.

And, in actual testing, Y is regressed on X (t) and also on X (t-1). If the latter regression is found to be more significant, then X is said to Granger-cause Y. In actual practice, there are economists who forget the prefix. That is, again, some sort of correlation analysis is carried out between Y and X (t-1). In any case, the concept of causation is a problem-ridden one. And as economists who contribute to policy-making, one ought to be on their toes all the time.

Time for a more lighthearted post in this blog series. Suppose you find a correlation between two phenomena. And you’re tempted to conclude that there’s a causal relation as well. The problem is that this causal relation – if it exists at all – can go either way. It’s a common mistake – or fraud – to choose one direction of causation and forget that the real causal link can go the other way, or both ways at the same time.

An example. We often think that people who like violent video games are more likely to show violent behavior because they are incited by the games to copy the violence that’s featured in these games. But can it not be that people who are more prone to violence are more fond of violent video games? (See also here). We choose a direction of causation that fits with our pre-existing beliefs.

Another widely shared belief is that uninformed and uneducated voters will destroy democracy, or at least diminish its value (see here, here and here). No one seems to ask the question whether it’s not a diminished form of democracy that renders citizens apathetic and uninformed. Maybe a full or deep democracy can encourage citizens to participate and become more knowledgeable through participation.

A classic example is the correlation between education levels and GDP (see also here). Do countries with higher education levels experience more economic growth because of the education levels of their citizens? Or is it that richer countries can afford to spend more on education and hence have better educated citizens? Probably both.

Another cartoon that expresses the same risk:

(source, click to enlarge)

More posts in this blog series.

A level of class I may never achieve.

This has been the longest I have gone without a new post (3 days) and from it I have discovered two correlation.

First, the longer it has been since I have taken photo the less inclined I am towards posting.

Second, a post a day schedule is not what brings people to this site (all 40 of them a day).

I am guessing that I won’t start posting in earnest again until the quarter ends and perhaps after the holidays. This quarter has taken a lot time when combined with my RA position.

I still think a smaller camera would encourage me to take more photos out and about.

I’m back to blogging, but only for a bit. Next week is a bit hectic. But I HAD to post on this.

When I was working in Bangalore, I was in a team full of young people just a few years older than me. They were much more experienced, and I, the only fresher, moved from module to module learning what each bit did. And it always, always happened that just as I moved to a new thing, the person handling that module would get their wedding fixed and would go off for a month, leaving me to work on that module.

There were the usual jokes about my being some hex or something, but it was mostly attributed to the common age group of all my mentors, and a week of holidaying following the end of each iteration, following which my colleagues would go home, and be subjected to a week of bride/groom-seeing.

And now the saga continues.

The person I am working with is an alumnus of University X. And has a wife who is expecting.
The person I might be working with, completely totally unrelated to the previously mentioned person, and of a completely different age group, is an alumnus of University X, though in a totally different mostly unrelated discipline. And has a wife who is expecting.

PS: I know such coincidences are everywhere, that if you look hard enough, you can find the shape of a holy grail on the Boston, Detroit, Washington DC, Chennai, Surathkal, Bangalore maps, so don’t make that point in the comments.

There is no shortage of cash rich investors in Asia even amidst the current troubles in Dubai. Indeed, sentiment in the gemstones market is particularly upbeat, with a rare five-carat pink diamond selling for a record HK$84.24 million in Hong Kong. Perhaps this is a good reflection of abundant liquidity and of course wealth in Asia and in particular China, with talk that mainland Chinese investors were strong participants in the diamond auction.

It’s not just diamonds that are selling for record prices; gold hit a fresh high above $1,200 and once again at least part of this is attributable to the appetite of Asian central banks as well as demand from China as the country tries to increase its gold reserves. The rise in gold prices has coincided with a bullish announcement from the world’s top gold producer that it has completely eliminated its market hedges earlier than forecast due to the positive outlook on prices and waning supply.

The correlation between gold prices and the USD remains very strong at -0.88 over the last 3-months, with firmer gold prices, implying further USD weakness. In fact, the gold / USD correlation has been consistently strong over the past few months and is showing little sign of diminishing.

Over the past 6-months the correlation has been -0.91 and over the past 1-month it was -0.75.  Assuming that anything above 0.70 can be considered statistically significant, the relationship shows that USD weakness has been well correlated with gold strength and that despite talk of a breakdown in the relationship it appears to remain solid. 

As long as the bullish trend in gold continues, the pressure on the USD will remain in place.  Adding to this pressure is the fact that risk is back on for now. Markets took the news of a fall in the ISM manufacturing index and in particular the drop in the employment component in its stride even though it supports the view of a weaker than consensus drop in payrolls in November when it is published on Friday.

There are still plenty of reasons to be cautious in the weeks ahead and although we appear to be back in a “risk on” environment markets are likely to gyrate between “risk on” and “risk off” over coming weeks. At least for now, the USD looks to remain under pressure but if risk aversion creeps back up as I suspect it may then the USD will see a bit more resilience into year end. 

Moreover, central banks globally are reaching the limits of their tolerance of USD weakness and will be tested once again, with EUR/USD back above 1.5000, EUR/CHF moving back below 1.5100 and the USD/JPY set to re-test 85.00 following the relatively benign measures announced by the BoJ in which the Bank did little to stem deflationary pressure or weaken the JPY.

Hello Friends here we come up with our another write up on “SMC Gyan Series”.

Topic is ECONOMIC INDICATORS… “Leading the World”.

Here, we would go through the Brief of like what are Economic Events & Indicators and important sources of data provider for calculating & determining economic indicators.

 

ECONOMIC INDICATORS… “Leading the World”

..

Economic Events & Indicators are statistics that precede an economic event.

The goal is to track the economy & derive a forecast for future performance.

Economic indicators have tremendous potential to generate volume and to move prices of commodities futures as well as the financial markets including Forex.

Tools of Construction: This would include separate sections of statistical methods including

- Calculating indices and re-basing them,

- Differences between arithmetic and geometric averages,

- Standard deviations,

- Regression analysis,

- Correlation and causation,

- Margins of error in statistics calculations and

- What this means for interpretation, subsequent revisions and why they happen.

Economic indicators include various indices, earnings reports, and economic summaries.

Examples : unemployment rate,  housing starts,  Consumer Price Index (a measure for inflation),  Consumer Leverage Ratio,  industrial production,  bankruptcies,  Gross Domestic Product,  broadband internet penetration,  retail sales,  stock market prices,  money supply changes etc;

The important sources of data provider for calculating & determining economic indicators are like:

- Bureau of Labor Statistics,

- Census of Construction Industries,

- Bureau of Economic Analysis &

- Reserve Bank.

The value of the indicator data is considered important if it presents new information, or is instrumental to drawing conclusions which couldn’t be drawn under other reports or data.

Each indicator is marked with “H”-”M”-”L” (High-Medium-Low), according to its level of importance, as commonly considered.

Next Blog we would try to know about the classified categories of Economic indicators in details and what is Time Era.

Stay Tuned for more and more on this

However For More latest Industry,Stock Market and Economy News Updates, Click Here

In a bit of fun and interesting timing it turns out I’ll be going to flocon in New Orleans this January.

Since I’ve spent the past 2-3 years doing business risk and security architecture, national sector level strategy, policy, etc….but now find myself getting into the technical details of building a CERT (ICS-CERT, specifically)…it’s suddenly time to get more up to speed on flows and how people are using them these days (Especially since I’d previously spent most of my time with firewalls and IDS data and not netflow / SiLK stuff).

My work on and release of pkviz this past weekend has helped a bit to get me re-focused on data analysis and playing with correlation tools and methodologies, but I’m still finding it odd going back to my earlier technology-centric security role  – which I’d thought I’d given up.  My head space has to be completely different than it was and I have to work around what some have called my fatalistic belief that technical security measures and analysis are doomed to fail in the face of our complete lack of interest in doing business risk architectures.

What scares me a little, though, is when I’ve been talking to people and doing research lately, it seems the state of the art of IDS, Flows, SEMS, SIEMS, network data analysis, etc. hasn’t changed all that much in the past few years. More vendors have sold more products, but they still do the same (questionable) things it seems. What gives? Am I off base?

Still, I’m pretty excited to get back into this type of thing and about the con. Who’s going to be there?

Has anyone noticed how Darwinists were the ones who were mainly promoting and worrying over global warming, and that it was the opposite in the case of Creationists?:

And now, it’s recently been found that the some of the top global warming scientists, all Darwinists, had been lying about there being a global warming trend, and that Earth had actually been cooling, possibly about to enter another ice age even (on the ice age site is a link to a book being sold which goes along with the false claim that Earth is billions of years old, and that animals suddenly evolved new useful features or into other animals because of magnetic reversals, but obviously magnets don’t cause people to grow wings or tails).

One notorious psuedo-scientist, a trolling idiot, claimed that it wasn’t true that Darwinists were the ones always promoting global warming when a notorious plagiarist atheist and wannabe-wise woman noticed the same thing months ago, claiming, “Not true” because oil companies were denying global warming. So a few oil companies are the majority of Darwinists? There are so many millions of Darwinists oil companies that other atheists not apart of any oil companies or any company at all are just a small minority? And how can oil companies deliberately lying (lying in their own minds) count as people who sincerely deny the truth, if, again, they “knew” they were lying? How absurd and stupid atheists are, especially Darwinist ones.

On top of that, Mr. “Not True” Atheist was lying about all oil companies denying global warming, though in my judgment in his ignorance being that he busies himself trolling Christian creationists, desperately and frantically posting mere claims without evidence to defend anti-Christian doctrine, like he did in this case, rather than carefully researching.

Here’s another passage, 2 years older than Mr. Not True’s lie, which refutes it,

“James Mulva says he’s recognizing an inconvenient truth. The chairman and chief executive of ConocoPhillips (COP), the nation’s third-largest oil company, acknowledged this month that fossil fuels—his company’s core product—are permanently warming the Earth. …

On Apr. 10, ConocoPhillips pledged $22 million to help Iowa State University develop fuels out of corn and switchgrass. Six days later the company unveiled a joint venture with Tyson Foods (TSN) to produce diesel fuel out of animal fat. Mulva says that venture will operate on a break-even basis and only then as a result of a $1-a-gallon federal subsidy. The 175 million gallons per year of production is a drop in the bucket compared to the 375 million gallons of gasoline Americans consume every day. …”

 

And just a few words later, months before the Prison Planet article, this Businessweek article then says,

“To die-hard environmentalists there is something self-serving in oil companies jumping on the green wagon. Are they truly trying to save the planet or merely attempting to ward off environmental legislation that could cost them money and reduce gasoline consumption? “We appreciate their corporate citizenship, but is there something we’re not seeing here?” asks Tyson Slocum…” (not the same Tyson of Tyson’s Foods)

There’s a maddening grammatical error in the top part of that article by the way.

So, Psuedo Christian Science Monitorers, who seven months ago mocked Christians and those who believed in Intelligent Design scientists over their disbelief of global warmin, lumping them in with astrologers, and who continues to mock them, clearly, the jokers, are you, the evil type: mockers. Not April fools, you liberal fools.

3009841    ความสัมพันธ์พยาธิวิทยาและคลินิก 2    Patho-Clinical Correlation II

นำเสนอและอภิปรายผลการตรวจทางพยาธิวิทยา ความสัมพันธ์กับลักษณะทางคลินิกและภาพทางรังสี และสรุปผลการตายของผู้ป่วย

(Presenttion of and discussion on pathological results, correlation to clinical findings and radiologic imaging in the autopsy cases.)

(3009841 จุฬาลงกรณ์มหาวิทยาลัย)

3009870    ความสัมพันธ์เปรียบเทียบพยาธิ-รังสีวิทยา    Radio-Pathological Correlation

ฝึกทักษะการวินิจฉัยทางพยาธิสภาพที่เห็นด้วยตาเปล่าและด้วยกล้องจุลทรรศน์ โดยคำนึงถึงความสัมพันธ์ระหว่างพยาธิสภาพกับภาพการตรวจวินิจฉัยด้านรังสีวิทยาในโรคที่พบบ่อยในประเทศไทย

(Practical skill diagnosis of gross and microscopic pathology with correlation of pathology and diagnostic imaging of common diseases in Thailand.)

(3009870 จุฬาลงกรณ์มหาวิทยาลัย)

3009884    ความสัมพันธ์พยาธิวิทยาและประสาทวิทยา 2    Neuro-Pathological Correlation II

อภิปรายความสัมพันธ์ระหว่างผลการตรวจทางพยาธิวิทยากับลักษณะทางคลินิกของผู้ป่วยโรคทางระบบประสาท เพื่อสรุปผลการวินิจฉัยทางพยาธิวิทยา

(Discussion on the correlation of pathological examination with clinical finding in the neurological patients and conclusion of the pathological diagnosis.)

(3009884 จุฬาลงกรณ์มหาวิทยาลัย)

Whew. I can relax.

For the past 2-3 months, I’ve been working on my first real Objective-C project (my iphone app is still going, it just took a back seat to this): An application that will read tcpdump output and animate the packets over time using their inherent byte / packet structure

And now…it’s up and in beta-ish quality. (Meaning it works, though some error checking and minor features arent quite where I want them.)

You can download it here for free: http://sintixerr.wordpress.com/pkviz-packet-visualizer-and-animator/

See it in motion here:

This project was important to me and has been a long time coming. I’ve wanted to write a packet visualizer since I first started working with data viz 5 or so years ago at NetSec and was using Advizor. That tool cost thousands of dollars per seat, didnt really animate (at least the way I needed), and only parsed CSV or databases. The free tools – like GnuPlot, just weren’t up to the task at all.

I also wanted something that could plot out data in interesting, pretty ways for some art projects I have in mind.

So, I originally started this time around on a quest to write a short python parser for tcpdump ascii hex output to put into <some generic viz tool> just to get started…but somehow I ended up writing a full-fledged visualizer (my first GUI project ever, I might add!). The learning process was a blast – I feel like I’m a much better coder for it – and I’ll be able to extend/expand on this to use for other art and security projects that are on my plate or are coming up.

I’m pretty excited about it. To see this finished through after years of whining to myself about it, procrastinating, and genuinely not having enough time, is pretty awesome. I’ve even already created a couple of cool shots that I’m happy to call “art” (granted, there is some photoshop processing here, but they’re both true to their originals!):

Anyway, Mac Users, check out the tool and let me know what you think!

(CFD.net.au – Contract for Difference, Share, Forex, ETFs, Commodities Traders) –

Today the pair has been trading in a narrow range between 0.9020 and 0.9050, on consolidation mode after reaching 14-month high at 0.9090 in the yesterday’s American session.

According to TJ Marta, analyst at The Overnight Express, the

AUD/USD is Slipping

: “AUD/USD (0.9035) is down slight …

<!–TITOL:

Forex: AUD/USD hits 0.9080 as fresh intra-day highFITITOL–>

Today the pair has been trading in a narrow range between 0.9020 and 0.9050, on consolidation mode after reaching 14-month high at 0.9090 in the yesterday’s American session.

According to TJ Marta, analyst at The Overnight Express, the

AUD/USD is Slipping

: “AUD/USD (0.9035) is down slightly overnight after making a new high since Aug’08 yesterday. Technical resistance for AUD/USD exists at 0.9090 (Oct 8 high) and 0.9275 (May ’08). Support lies at 0.8570 (Oct 2 low), 0.8545 (Sep 14 low) and 0.8478 (Aug high). AUD/USD has correlated most strongly with equities (S&P 500, positive) and commodities (CRB, positive). The correlation with gold (positive) is slipping.”

Source: Forex: AUD/USD hits 0.9080 as fresh intra-day high

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