professors

Economic Outlook 2015 – I

Well, it’s that time – end of one calendar year and, soon, the beginning of another, and that means major banks and financial institutions are releasing their big picture “economic outlooks” for 2015.

Here are two well worth watching.

Jan Hatzius of Goldman Sachs provides an interesting, short discussion of the US economic outlook for 2015.

Huw Pills, also of Goldman Sachs, gives a nuanced discussion of Europe’s more vulnerable economic position for 2015.

For other regions, see Outlook 2015.

Barron’s Outlook 2015: Stick With the Bull focuses on stocks and is based on a survey of investment advisors; its outlook is decidedly upbeat.

Born in March 2009, today’s bull market is the fourth longest in history—and it isn’t about to end, despite last week’s shellacking. That’s the word from Wall Street’s top strategists, who expect the Standard & Poor’s 500 stock index to rise 10% in 2015. A gain of that magnitude surely would merit applause, coming atop an 8% rally year to date, not to mention 2013’s 30% advance. Almost six years in, the old bull still seems sprightly….

U.S. stocks are neither cheap nor expensive, based on the market’s current price/earnings ratio of 15.8 times future four-quarter earnings. Few strategists expect the multiple to expand much in the coming year.

“In isolation, U.S. stocks are on the expensive side,” says Jeffrey Knight, head of global asset allocation at Columbia Management. But measured against other financial assets—whether emerging-market equities or developed-market bonds—U.S. shares look strong, he adds.

And, in researching this article, I found Janet Yellen’s Dashboard available from the Brookings Institution website.

A lot of what happens in 2015 has to do with whether, when, and then how much the Fed raises interest rates.

I’m aiming to be as inclusive as I can in putting up these videos of the various celebrity forecasters and their outlook for 2015, so stay tuned.

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2014 in Review – I

I’ve been going over past posts, projecting forward my coming topics. I thought I would share some of the best and some of the topics I want to develop.

Recommendations From Early in 2014

I would recommend Forecasting in Data-Limited Situations – A New Day. There, I illustrate the power of bagging to “bring up” the influence of weakly significant predictors with a regression example. This is fairly profound. Weakly significant predictors need not be weak predictors in an absolute sense, providing you can bag the sample to hone in on their values.

There also are several posts on asset bubbles.

Asset Bubbles contains an intriguing chart which proposes a way to “standardize” asset bubbles, highlighting their different phases.

BubbleAnatomy

The data are from the Hong Kong Hang Seng Index, oil prices to refiners (combined), and the NASDAQ 100 Index. I arrange the series so their peak prices – the peak of the bubble – coincide, despite the fact that the peaks occurred at different times (October 2007, August 2008, March 2000, respectively). Including approximately 5 years of prior values of each time series, and scaling the vertical dimensions so the peaks equal 100 percent, suggesting three distinct phases. These might be called the ramp-up, faster-than-exponential growth, and faster-than-exponential decline. Clearly, I am influenced by Didier Sornette in choice of these names.

I’ve also posted several times on climate change, but I think, hands down, the most amazing single item is this clip from “Chasing Ice” showing calving of a Greenland glacier with shards of ice three times taller than the skyscrapers in Lower Manhattan.

See also Possibilities for Abrupt Climate Change.

I’ve been told that Forecasting and Data Analysis – Principal Component Regression is a helpful introduction. Principal component regression is one of the several ways one can approach the problem of “many predictors.”

In terms of slide presentations, the Business Insider presentation on the “Digital Future” is outstanding, commented on in The Future of Digital – I.

Threads I Want to Build On

There are threads from early in the year I want to follow up in Crime Prediction. Just how are these systems continuing to perform?

Another topic I want to build on is in Using Math to Cure Cancer. I’d like to find a sensitive discussion of how MD’s respond to predictive analytics sometime. It seems to me that US physicians are sometimes way behind the curve on what could be possible, if we could merge medical databases and bring some machine learning to bear on diagnosis and treatment.

I am intrigued by the issues in Causal Discovery. You can get the idea from this chart. Here, B → A but A does not cause B – Why?

casualpic

I tried to write an informed post on power laws. The holy grail here is, as Xavier Gabaix says, robust, detail-independent economic laws.

Federal Reserve Policies

Federal Reserve policies are of vital importance to business forecasting. In the past two or three years, I’ve come to understand the Federal Reserve Balance sheet better, available from Treasury Department reports. What stands out is this chart, which anyone surfing finance articles on the net has seen time and again.

FedMBandQEgraph

This shows the total of the “monetary base” dating from the beginning of 2006. The red shaded areas of the graph indicate the time windows in which the various “Quantitative Easing” (QE) policies have been in effect – now three QE’s, QE1, QE2, and QE3.

Obviously, something is going on.

I had fun with this chart in a post called Rhino and Tapers in the Room – Janet Yellen’s Menagerie.

OK, folks, for this intermission, you might want to take a look at Malcolm Gladwell on the 10,000 Hour Rule


So what happens if you immerse yourself in all aspects of the forecasting field?

Coming – how posts in Business Forecast Blog pretty much establish that rational expectations is a concept way past its sell date.

Guy contemplating with wine at top from dreamstime.

 

Links – Beginning of the Holiday Season

Economy and Trade

Asia and Global Production Networks—Implications for Trade, Incomes and Economic Vulnerability Important new book –

The publication has two broad themes. The first is national economies’ heightened exposure to adverse shocks (natural disasters, political disputes, recessions) elsewhere in the world as a result of greater integration and interdependence. The second theme is focused on the evolution of global value chains at the firm level and how this will affect competitiveness in Asia. It also traces the past and future development of production sharing in Asia.

Chapter 1 features the following dynamite graphic – (click to enlarge)

GVC2009

The Return of Currency Wars

Nouriel Roubini –

Central banks in China, South Korea, Taiwan, Singapore, and Thailand, fearful of losing competitiveness relative to Japan, are easing their own monetary policies – or will soon ease more. The European Central Bank and the central banks of Switzerland, Sweden, Norway, and a few Central European countries are likely to embrace quantitative easing or use other unconventional policies to prevent their currencies from appreciating.

All of this will lead to a strengthening of the US dollar, as growth in the United States is picking up and the Federal Reserve has signaled that it will begin raising interest rates next year. But, if global growth remains weak and the dollar becomes too strong, even the Fed may decide to raise interest rates later and more slowly to avoid excessive dollar appreciation.

The cause of the latest currency turmoil is clear: In an environment of private and public deleveraging from high debts, monetary policy has become the only available tool to boost demand and growth. Fiscal austerity has exacerbated the impact of deleveraging by exerting a direct and indirect drag on growth. Lower public spending reduces aggregate demand, while declining transfers and higher taxes reduce disposable income and thus private consumption.

Financial Markets

The 15 Most Valuable Startups in the World

Uber is among the top, raising $2.5 billion in direct investment funds since 2009. Airbnb, Dropbox, and many others.

The Stock Market Bull Who Got 2014 Right Just Published This Fantastic Presentation I especially like the “Mayan Temple” effect, viz

MayanTemple

Why Gold & Oil Are Trading So Differently supply and demand – worth watching to keep primed on the key issues.

Technology

10 Astonishing Technologies On The Horizon – Some of these are pretty far-out, like teleportation which is now just gleam in the eye of quantum physicists, but some in the list are in prototype – like flying cars. Read more at Digital Journal entry on Business Insider.

  1. Flexible and bendable smartphones
  2. Smart jewelry
  3. “Invisible” computers
  4. Virtual shopping
  5. Teleportation
  6. Interplanetary Internet
  7. Flying cars
  8. Grow human organs
  9. Prosthetic eyes
  10. Electronic tattoos

Albert Einstein’s Entire Collection of Papers, Letters is Now Online

Princeton University Press makes this available.

AEinstein

Practice Your French Comprehension

Olivier Grisel, Software Engineer, Inria – broad overview of machine learning technologies. Helps me that the slides are in English.

Xmaspic

Forecasting Holiday Retail Sales

Holiday retail sales are a really “spikey” time series, illustrated by the following graph (click to enlarge).

HolidayRetailSales

These are monthly data from FRED and are not seasonally adjusted.

Following the National Retail Federation (NRF) convention, I define holiday retail sales to exclude retail sales by automobile dealers, gasoline stations and restaurants. The graph above includes all months of the year, but we can again follow the NRF convention and define “sales from the Holiday period” as being November and December sales.

Current Forecasts

The National Retail Federation (NRF) issues its forecast for the Holiday sales period in late October.

This year, it seems they were a tad optimistic, opting for

..sales in November and December (excluding autos, gas and restaurant sales) to increase a healthy 4.1 percent to $616.9 billion, higher than 2013’s actual 3.1 percent increase during that same time frame.

As the news release for this forecast observed, this would make the Holiday Season 2014 the first time in many years to see more than 4 percent growth – comparing to the year previous holiday periods.

The NRF is still holding to its bet (See https://nrf.com/news/retail-sales-increase-06-percent-november-line-nrf-holiday-forecast), noting that November 2014 sales come in around 3.2 percent over the total for November in 2013.

This means that December sales have to grow by about 4.8 percent on a month-over-year-previous-month basis to meet the overall, two month 4.1 percent growth.

You don’t get to this number by applying univariate automatic forecasting software. Forecast Pro, for example, suggests overall year-over-year growth this holiday season will be more like 3.3 percent, or a little lower than the 2013 growth of 3.7 percent.

Clearly, the argument for higher growth is the extra cash in consumer pockets from lower gas prices, as well as the strengthening employment outlook.

The 4.1 percent growth, incidentally, is within the 97.5 percent confidence interval for the Forecast Pro forecast, shown in the following chart.

FPHolidaySales

This forecast follows from a Box-Jenkins model with the parameters –

ARIMA(1, 1, 3)*(0, 1, 2)

In other words, Forecast Pro differences the “Holiday Sales” Retail Series and finds moving average and autoregressive terms, as well as seasonality. For a crib on ARIMA modeling and the above notation, a Duke University site is good.

I guess we will see which is right – the NRF or Forecast Pro forecast.

Components of US Retail Sales

The following graphic shows the composition of total US retail sales, and the relative sizes of the main components.

USRETAILPIE 

Retail and food service sales totaled around $5 trillion in 2012. Taking out motor vehicle and parts dealers, gas stations, and food services and drinking places considerably reduces the size of the relevant Holiday retail time series.

Forecasting Issues and Opportunities

I have not yet done the exercise, but it would be interesting to forecast the individual series in the above pie chart, and compare the sum of those forecasts with a forecast of the total.

For example, if some of the component series are best forecast with exponential smoothing, while others are best forecast with Box-Jenkins time series models, aggregation could be interesting.

Of course, in 2007-09, application of univariate methods would have performed poorly. What we cry out for here is a multivariate model, perhaps based on the Kalman filter, which specifies leading indicators. That way, we could get one or two month ahead forecasts without having to forecast the drivers or explanatory variables.

In any case, barring unforeseen catastrophes, this Holiday Season should show comfortable growth for retailers, especially online retail (more on that in a subsequent post.)

Heading picture from New York Times

Big Data and Fracking

Texas’ Barnett Shale, shown below, is the focus of recent Big Data analytics conducted by the Texas Bureau of Economic Geology.

BarnettShale

The results provide, among other things, forecasts of when natural gas production from this field will peak – suggesting at current prices that peak production may already have been reached.

The Barnett Shale study examines production data from all individual wells drilled 1995-2010 in this shale play in the Fort Worth basin – altogether more than 15,000 wells.

Well-by-well analysis leads to segmentation of natural gas and liquid production potential in 10 productivity tiers, which are then used to forecast future production.

Decline curves, such as the following, are developed for each of these productivity tiers. The per-well production decline curves were found to be inversely proportional to the square root of time for the first 8-10 years of well life, followed by exponential decline as what the geologists call “interfracture interference” began to affect production.

TierDCurves

A write-up of the Barnett Shale study by its lead researchers is available to the public in two parts at the following URL’s:

http://www.beg.utexas.edu/info/docs/OGJ_SFSGAS_pt1.pdf

http://www.beg.utexas.edu/info/docs/OGJ_SFSGAS_pt2.pdf

Econometric analysis of well production, based on porosity and a range of other geologic and well parameters is contained in a followup report Panel Analysis of Well Production History in the Barnett Shale conducted under the auspices of Rice University.

Natural Gas Production Forecasts

Among the most amazing conclusions for me are the predictions regarding total natural gas production at various prices, shown below.

Barnetshalecurvelater

This results from a forecast of field development (drilling) which involved a period of backcasting 2011-2012 to calibrate the BEG economic and production forecast models.

Essentially, it this low price regime continues through 2015, there is a high likelihood we will see declining production in the Barnett field as a whole.

Of course, there are other major fields – the Bakken, the Marcellus, the Eagle-Ford, and a host of smaller, newer fields.

But the Barnett Shale study provides good parameters for estimating EUR (estimate ultimate recovery) in these other fields, as well as time profiles of production at various prices.

Forecasting Shale Oil/Gas Decline Rates

Forecasting and data analytics increasingly are recognized as valued partners in nonconventional oil and gas production.

Fracking and US Oil/Gas Production

“Video Friday” here presented a YouTube with Brian Ellis – a Michigan University engineer – discussing hydraulic fracturing and horizontal drilling (“fracking”).

USannualoilprod

Fracking produced the hockey stick at the end of this series.

These new technologies also are responsible for a bonanza of natural gas, so much that it often has nowhere to go – given the limited pipeline infrastructure and LNG processing facilities.

shalegasprod

Rapid Decline Curves for Fracking Oil and Gas

In contrast to conventional wells, hydraulic fracturing and horizontal drilling (“fracking”) produces oil and gas wells with rapid decline curves.

Here’s an illustration from the Penn State Department of Energy and Mineral Engineering site,

Pennstatedeclinecurve

The two legends at the bottom refer to EUR’s– estimated ultimate recovery times (click to enlarge).

Conventional oil fields typically have decline rates on the order of 5 percent per year.

Shale oil and gas wells, on the other hand, may produce 50 percent or more of their total EUR in their first year of operation.

There are physical science fundamentals behind this, explained, for example, in

Decline and depletion rates of oil production: a comprehensive investigation

You can talk, for example, of shale production being characterized by a Transient Flow Period followed by Boundary Dominated Flow (BDF).

And these rapid decline rates have received a lot of recent attention in the media:

Could The ‘Shale Oil Miracle’ Be Just A Pipe Dream?

Wells That Fizzle Are a ‘Potential Show Stopper’ for the Shale Boom

Is the U.S. Shale Boom Going Bust?

Forecasting and Data Analytics

One forecasting problem in this context, therefore, is simply to take histories from wells and forecast their EUR’s.

Increasingly, software solutions are applying automatic fitting methods to well data to derive decline curves and other shale oil and gas field parameters.

Here is an interesting product called Value Navigator.

This whole subject is developing rapidly, and huge changes in the US industry are expected, if oil and gas prices continue below $60 a barrel and $4 MMBtu.

The forecasting problem may shift from well and oil field optimization to evaluation of the wider consequences of recent funding of the shale oil and gas boom. But, again, the analytics are available to do this, to a large extent, and I want to post up some of what I have discovered in this regard.

fracking

Video Friday – Fracking

Here is Brian Ellis from Michigan University Engineering with a look at the technology of hydraulic fracturing (fracking) and horizontal drilling – the innovations that recently pushed US oil production near the 10 million barrel per day mark.

I’m putting this up, rather than other, often excellent film clips showing people lighting water from their kitchen taps because the scale of shale oil and gas production has become so large. There really is a huge tradeoff between current employment and business activity and long term environmental effects.

Price, rather than environmental concerns, are likely to be the crucial factor in any scaleback.

At the same time, there is the possibility of further technical advance in the US shale oil and gas technologies, advances which may push extraction prices lower, giving the industry a longer lease during what may be a year or more of lower oil prices.

Fracking and its possible dynamics are critical to a lot of business activity and, thus, forecasting in the US.

foxconn-121016

China – Trade Colossus or Assembly Site?

There is a fascinating paper – How the iPhone Widens the United States Trade Deficit with the People’s Republic of China. In this Asian Bank Development Institute (ADBI) white paper, Yuqing Ying and his coauthor document the value chain for an Apple iPhone:

IPhone

The source for this breakout, incidentally, is a “teardown” performed by the IT market research company iSupply, still accessible at -https://technology.ihs.com/389273/iphone-3g-s-carries-17896-bom-and-manufacturing-cost-isuppli-teardown-reveals. In other words, iSupply physically took apart an iPhone to identify the manufacturers of the components.

The Paradox

After estimating that, in

2009 iPhones contributed US$1.9 billion to the trade deficit, equivalent to about 0.8% of the total US trade deficit with the PRC,

the authors go on to point out that

..most of the export value and the deficit due to the iPhone are attributed to imported parts and components from third countries and have nothing to do with the PRC. Chinese workers simply put all these parts and components together and contribute only US$6.5 to each iPhone, about 3.6% of the total manufacturing cost (e.g., the shipping price). The traditional way of measuring trade credits all of the US$178.96 to the PRC when an iPhone is shipped to the US, thus exaggerating the export volume as well as the imbalance. Decomposing the value added along the value chain of iPhone manufacturing suggests that, of the US$2.0 billion worth of iPhones exported from the PRC, 96.4% in fact amounts to transfers from Germany (US$326 million), Japan (US$670 million), Korea (US$259 million), the US (US$108 million), and other countries (US$ 542 million). All of these countries are involved in the iPhone production chain.

Yuqing Xing builds on the paradox in his more recent China’s High-Tech Exports: The Myth and Reality published in 2014 in MIT’s Asian Economic Papers.

Prevailing trade statistics are inconsistent with trade based on global supply chains and mistakenly credit entire values of assembled high-tech products to China. China’s real contribution to the reported 82 percent high-tech exports is labor not technology. High-tech products, mainly made of imported parts and components, should be called “Assembled High-tech.” To accurately measure high-tech exports, the value-added approach should be utilized with detailed analysis on the value chains distributions across countries. Furthermore, if assembly is the only source of value-added by Chinese workers, in terms of technological contribution these assembled high-tech exports are indifferent to labor-intensive products, and so they should be excluded from the high-tech classification.

MNEs, in particular Taiwanese IT firms in China, have performed an important role in the rapid expansion of high-tech exports. The trend of production fragmentation and outsourcing activities of MNEs in information and communication technology has benefitted China significantly, because of its huge labor endowment. The small share of indigenous firms in high-tech exports implies that China has yet to become a real competitor of the United States, EU, and Japan. That China is the number one high-tech exporter is thus a myth rather than a reality.

Ying and Yang

This perspective – that it is really “value-added” that we should focus on, rather than the total dollar volume of trade coming in or going out of a country – is interesting, but I can’t help but think there is a disconnect when you consider actual Chinese foreign exchange reserves, shown below (source – http://www.stats.gov.cn/tjsj/ndsj/2013/indexeh.htm).

ChinaFER

So currently China holds nearly 3.5 trillion dollars in foreign exchange reserves – most of which, but not all, is comprised of US dollars.

This is a huge amount of money, on the order of five percent of total global GDP.

How could China have accumulated this merely by being an assembly site for high tech and other products (see Five Facts about Value-Added Exports and Implications for Macroeconomics and Trade Research)? How can this be attributable just to mistakes in counting the origin of the many components in goods coming from China? Don’t those products have to come in and be counted as imports?

There is a mystery here, which it would be good to resolve.

Assembly photo at top from Apple Insider

Chinesebureacrat

China Passes US in Terms of Purchasing Power Parity

The International Monetary Fund (IMF) announced recently that Chinese GDP passed that of the United States – in terms of purchasing power parity (PPP).

Business Insider charts the relative sizes of the Chinese and US economies in terms of total global output, where, again, production is measured in terms of purchasing power output.

ChinaUS

According to the World Bank,

Purchasing power parity conversion factor is the number of units of a country’s currency required to buy the same amount of goods and services in the domestic market as a U.S. dollar would buy in the United States.

In a less serious vein, the Economist magazine maintains the Big Mac Index. This is informative, however, inasmuch as MacDonalds outlets range across the globe.

In July of this year, the Economist lists the US price of a Big Mac hamburger as $4.80.

China is among the cheapest places to buy a Big Mac, as shown in this table from Economist data.

BIGMAClist

The China Big Mac Index, therefore, is 0.57, suggesting Chinese yuan purchase almost twice the actual goods and services in China, as their dollar exchange rate would suggest.

Or to do this calculation based on the current exchange rate, 1 US dollar buys 6.41 Chinese 1 yuan.

So, if the local price has not changed, 16.9 yuan buy a Big Mac, indicating that a Big Mac now has a dollar price of $2.63. Then, if today’s Big Mac still costs $4.80, the renmimbi buys 4.8/2.63 or 1.83 times as much as its market exchange rate indicates. Hence, according to a Big Mac type index evaluation, the renmimbi is undervalued.

This is a pretty good calculation, according to the World Bank, which lists the conversion factor as 0.7.

Of course, there are four to five times as many residents of the People’s Republic of China, as there are US residents. Per capita Chinese incomes, accordingly, are four to five times lower, even in terms of purchasing power parity.

And in terms of market exchange values, the IMF estimates 2014 Chinese GDP at 10,355 billion dollars, compared with $17,416 billion for the US.

The rise of Chinese production has been truly spectacular, as this chart of Chinese GDP shows, based on official Chinese statistics.

ChinaGDPgraph

There are a lot of other remarkable charts that can be pulled together about China, and I am planning several future posts along these lines.

See you this coming week!

Chinese official courtesy of Wikipedia

SaudiOilMinister

Followup on OPEC and the Price of Oil

Well, readers here may have noticed, Business Forecast Blog correctly predicted the OPEC decision about reducing oil production at their Thanksgiving Thursday (November 27) meeting in Vienna.

USA Today reports,

VIENNA — Crude prices plunged Thursday after the powerful Organization of Petroleum Exporting Countries said it wouldn’t cut production levels to stem the collapse in oil prices that have fallen 40% since June.

Saudi Arabia’s oil minister Ali Al-Naimi delivered the news as he left a nearly five-hour meeting of the cartel’s 12 oil ministers here.

Our post was called The Limits of OPEC and was studded with passages of deep foresight, such as

I’m kind of a contrarian here. I think the sound and fury about this Vienna meeting on Thanksgiving may signify very little in terms of oil prices – unless global (and especially Chinese) economic growth picks up. As the dominant OPEC producer, Saudi Arabia may have market power, but, otherwise, there is little evidence OPEC functions as a cartel. It’s hard to see, also, that the Saudi’s would unilaterally reduce their output only to see higher oil prices support US frackers continuing to increase their production levels at current rates.

The immediate response to the much-anticipated OPEC meeting was a plunge in the spot price of West Texas Intermediate (WTI) to below $70 a barrel.

WTINov28

Brent, the other pricing standard, fared a little better, but dropped significantly,

BrentNov28

Both charts are courtesy of the Financial Times of London.

The Reuters article on the OPEC decision – Saudis block OPEC output cut, sending oil price plunging – is full of talk that letting prices drift lower, perhaps down to $60-65 a barrel, is motivated by a desire to wing higher-cost US producers, and also, maybe, to squeeze Russia and Iran – other players who are out of favor with Saudi Arabia and other Gulf oil states.

Forecasting Issues and Techniques

Advice – get the data, get the facts. Survey Bloomberg and other media by relevant news story and topic, but whenever possible, go to the source.

For example, lower oil prices may mean Saudi Arabia and some other Gulf oil states have to rely more on accumulated foreign exchange to pay their bills, since their lavish life-styles probably adjusted to higher prices (even though raw production costs may be as low as $25 a barrel). Just how big are these currency reserves, and can we watch them being drawn down? There is another OPEC meeting apparently scheduled for June 2015

Lead picture of Saudi Oil Minister from Yahoo.

Sales and new product forecasting in data-limited (real world) contexts