Category Archives: gasoline price forecasts

gasoline price forecasts, impacts of offshoring, macroeconomic forecasting

What Up? The Trump Years – I

From the standpoint of business forecasting, Donald Trump is important. His challenge to various conventional wisdoms and apparently settled matters raises questions about where things will go in 2017 and beyond. Furthermore, his style of governance is unknown, since as a businessman and minor celebrity, Trump has literally no government experience. He is an Outsider to the political scene, arriving with a portfolio of ideas like mass deportations of illegal immigrants, a massive wall between the United States and its southern neighbor, Mexico, bringing manufacturing jobs back, no further gun controls, and more rigorous screening of immigrants from the Middle East and Muslim countries.

So, with Donald Trump’s Inauguration January 20, a lot seems up in the air. But behind the hoopla, fundamental economic processes and trends are underway. What types of forecasts, therefore, seem reasonable, defensible?

Some Thoughts on Economics

Let’s start with economics.

Donald Trump could be the President who returns inflation and higher interest rates to the equation.

Offshoring and outsourcing have been factors in creating industrial wastelands and hollowed out production in the US – areas where you can drive for miles through abandoned buildings and decaying business centers. At the same time, offshoring and outsourcing bring low-cost electronics and other products to US consumers.

It is a Faustian bargain. If you were a wage-earner with a high school education (or less), supporting a family working in the “fast food sector” or convenience store, maybe holding two jobs to patch together enough income for bills – what you got was a $500 big screen TV and all sorts of gadgets for your kids. You could buy a cheap computer, and cheap clothing, too. Credit cards are available, although less so after 2008.

Oh yeah, another place you could work is in a Big Box store, whose long aisles and vanishing sakes clerks serve as the terminus of global supply chains coursing through ports on the West and East coasts. These are the ports where box-car size containers from China and elsewhere are unloaded, and put on rail cars or moved by truck to stores where consumers can purchase the goods packed in these containers largely on credit.

Is it even possible to slow, stop, or reverse this dynamic?

Let’s see, the plan for bringing manufacturing back to the United States involves deregulation of business, making doing business in the US more profitable. One idea that has been floated is that deregulation would provide incentives for US business to repatriate all that money they are holding overseas back to the US, where it could be invested in America.

Before taking office, President-elect Trump earned points with his supporters by “jawboning” US and foreign companies to keep jobs here, threatening taxes or fees for re-importing stuff to the US from newly relocated operations.

But most of the returning manufacturing would be highly capital intensive (think”robots”) so that only a few more jobs could be garnered from this re-investment in America, right?

Well, before dismissing the idea, note that some of these new jobs would be good-paying, probably requiring higher skills to run more automated production processes.

But this is a different game – producing in the United States, discouraging companies to move operations abroad to lower cost environments, placing taxes, fees, or tariffs on goods manufactured abroad coming into the US. This also involves higher prices.

There is another thread, though, to do with the impact of “deregulation” on the US oil and gas industry, aka “fracking.”

When American ingenuity developed hydraulic fracturing technology (“fracking”) to tap lower yield oil and gas reserves in areas of Texas, South Dakota, and elsewhere, US oil and gas production surged almost to the point of self-sufficiency. But then the Saudi’s lowered the boom, and oil prices dropped, making the higher cost US wells unprofitable, and slowing their expansion.

Before that happened, however, it was apparent fracking in the West and the older oil fields of the Eastern US energized activity up the supply chain, drawing forth significant manufacturing of pipes and equipment. The proverbial boom towns cropped up in the Dakotas and Texas, where hours of work could be long, and pay was good.

Clearly, as oil prices rise again with various global geopolitical instabilities, the US oil and gas industry can rise again, create large numbers of jobs and, also, significant environmental degradation – unless done with high standards for controlling wastes and methane emissions.

But Mr. Trump nominated the former Oklahoma Attorney General to head the US Environmental Protection Agency (EPA) – an agency which Mr. Trump vowed at one point in the campaign to eliminate and which his nominee Scott Pruitt fought tooth and nail in the courts.

So, concern with the environment to the winds, there is a case for a Trump “boom” in 2017 and 2018 – if global oil prices can stay above the breakeven point for US oil and gas production.

Another thread or storyline ties in here  – deportations and stronger controls over illegal immigration.

Again, we have to consider how things are actually made, and we see that, as Anthony Bourdain has noted, many of the restaurant jobs in New York City and other big cities  – in the kitchen especially – are filled by new migrants, many not here legally.

Also, scores of construction jobs in the Rocky Mountain West are filled by Hispanic workers.

Pressure on these working populations to produce their papers can only lead to higher wages and costs, which will be passed along to consumers.

And don’t forget President Trump’s promise to restore US military preparedness. As “cost-plus” contracts, US defense production acts as a conduit for price increases, and may be overpriced (the alternative being to let potential enemies manufacture US weapons).

So what this thought experiment suggests is that, initially, jobs in the Trump era may be boosted by captive or returning manufacturing operations and resumption of the US oil and gas boom – but be accompanied by higher prices. Higher prices also are thematic to limiting the labor pool in US industries, and the cost-overruns are endemic to US defense production.

This is not the end of the economics story, obviously.

The next thing to consider is the US Federal Reserve Bank, which, under Chairman Yellen and other members of the Board of Governors is itching to increase interest rates, as the US economy recovers.

Witnessing a surge of employment from fracking jobs plus a smatter of repatriation of US manufacturing, and the associated higher prices involved with all of this, the Fed should have plenty of excuse to bring interest rates back to historic levels.

Will this truncate the Trump boom?

And what about international response to these developments in the United States?

energy forecasting, gasoline price forecasts, geopolitical risk

Texas Manufacturing Shows Steep Declines

The Dallas Federal Reserve Bank highlights the impact of continuing declines in oil prices in their latest monthly Texas Manufacturing Outlook Survey:

Texas factory activity fell sharply in January, according to business executives responding to the Texas Manufacturing Outlook Survey. The production index—a key measure of state manufacturing conditions—dropped 23 points, from 12.7 to -10.2, suggesting output declined this month after growing throughout fourth quarter 2015.

Other indexes of current manufacturing activity also indicated contraction in January. The survey’s demand measures—the new orders index and the growth rate of orders index—led the falloff in production with negative readings last month, and these indexes pushed further negative in January. The new orders index edged down to -9.2, and the growth rate of orders index fell to -17.5, its lowest level in a year. The capacity utilization index fell 15 points from 8.1 to -7, and the shipments index also posted a double-digit decline into negative territory, coming in at -11.

Perceptions of broader business conditions weakened markedly in January. The general business activity and company outlook indexes fell to their lowest readings since April 2009, when Texas was in recession. The general business activity index fell 13 points to -34.6, and the company outlook index slipped to -19.5.

Here is a chart showing the Texas monthly manufacturing index.

TexasManuIndex

The logical follow-on question is raised by James Hamilton – Can lower oil prices cause a recession?

Hamilton cites an NBER (National Bureau of Economic Research) paper – Geographic Dispersion of Economic Shocks: Evidence from the Fracking Revolution – which estimates jobs from fracking (hydraulic fracturing of oil deposits) resulted in more than 700,000 US jobs 2008-2009, resulting in an 0.5 percent decrease in the unemployment rate during that dire time.

Obviously, the whole thing works in reverse, too.

Eight states with a high concentration of energy-related jobs – including Texas and North Dakota – have experienced major impacts in terms of employment and tax revenues. See “Plunging oil prices: a boost for the U.S. economy, a jolt for Texas”.

Another question is how long can US-based producers hold out financially, as the price of crude continues to spiral down? See Half of U.S. Fracking Industry Could Go Bankrupt as Oil Prices Continue to Fall.

I’ve seen some talk that problems in the oil patch may play a role analogous to sub-prime mortgages during the last economic contraction.

In terms of geopolitics, there is evidence the Saudi’s, who dominate OPEC, triggered the price decline by refusing to limit production from their fields.

business trends, energy forecasting, gasoline price forecasts, global business forecasts

Oil and Gas Prices – a “Golden Swan”?

Crude oil prices plummeted last week, moving toward $80/Bbl for West Texas Intermediate (WTI) – the spot pricing standard commodity.

CrudeOilSpotPrice

OPEC – the Organization of Petroleum Exporting Counties – is a key to trajectory of oil prices, accounting for about 40 percent of global oil output.

Media reports that the Saudi Arabian Kingdom, which is the largest producer in OPEC, is advising that it will not cut oil production at the current time. The US Energy Information Agency (EIA) has a graph on its website underlining the importance of Saudi production to global oil prices.

Saudiproductionoilprice

Officially, there is very little in the media to pin down the current Saudi policy, although, off-the-record, Saudi representatives apparently have indicated they could allow crude prices to drift between $80 and $90 a barrel for a couple of years. This could impact higher cost producers, such as Iran and burgeoning North American shale oil production.

At the same time, several OPEC members, such as Venezuela and Libya, have called for cuts in output to manage crude prices going forward. And a field jointly maintained by Saudi Arabia and Kuwait just has been shut down, ostensibly for environmental upgrades.

OPEC’s upcoming November 27 meeting in Vienna, Austria should be momentous.

US Oil Production

Currently, US oil production is running at 8.7 million barrels a day, a million barrels a day higher than in a comparable period of 2013, and the highest level since 1986.

The question of the hour is whether US production can continue to increase with significantly lower oil prices.

Many analysts echo the New York Times, which recently compared throttling back US petroleum activity to slowing a freight train.

Most companies make their investment decisions well in advance and need months to slow exploration because of contracts with service companies. And if they do decide to cut back some drilling, they will pick the least prospective fields first as they continue developing the richest prospects.

At the same time, the most recent data suggest US rig activity is starting to slip.

Economic Drivers

It’s all too easy to engage in arm-waving, when discussing energy supplies and prices and their relationship to the global economy.

Of course, we have supply and demand, as one basis. Supplies have been increasing, in part because of new technologies in US production and Libyan production coming back on line.

Demands have not been increasing, on the other hand, as rapidly as in the past. This reflects slowing growth in China and continuing energy conservation.

One imponderable is the influence of speculators on oil prices. Was there a “bubble” before 2009, for example, and could speculators drive oil prices significantly lower in coming months?

Another factor that is hard to assess is whether 2015 will see a recession in major parts of the global economy.

The US Federal Reserve has been planning on eliminating Quantitative Easing (QE) – its program of long-term bond purchases – and increasing the federal funds rate from its present level of virtually zero. Many believe that these actions will materially slow US and global economic growth. Coupled with the current deflationary environment in Europe, there have been increasing signs that factors could converge to trigger a recession sometime in 2015.

However, low energy prices usually are not part of the prelude for a recession, although they can develop after the recession takes hold.

Instead, prices at the pump in the US could fall below $3.00 a gallon, providing several hundred dollars extra in discretionary income over the course of a year. This, just prior to the Christmas shopping season.

So – if US oil production continues to increase and prices at the pump fall below $3.00, there will be jobs and cheap gas, a combination likely to forstall a downturn, at least in the US for the time being.

Top image courtesy of GameDocs

Big Data, gasoline price forecasts, geopolitical risk, predictive analytics

When the Going Gets Tough, the Tough Get Going

Great phrase, but what does it mean? Well, maybe it has something to do with the fact that a lot of economic and political news seem to be entering kind of “end game.” But, it’s now the “lazy days of summer,” and there is a temptation to sit back and just watch it whiz by.

What are the options?

One is to go more analytical. I’ve recently updated my knowledge base on some esoteric topics –mathematically and analytically interesting – such as kernel ridge regression and dynamic principal components. I’ve previously mentioned these, and there are more instances of analysis to consider. What about them? Are they worth the enormous complexity and computational detail?

Another is to embrace the humming, buzzing confusion and consider “geopolitical risk.” The theme might be the price of oil and impacts, perhaps, of continuing and higher oil prices.

Or the proliferation of open warfare.

Rarely in recent decades have we seen outright armed conflict in Europe, as appears to be on-going in the Ukraine.

And I cannot make much sense of developments in the Mid-East, with some shadowy group called Isis scooping up vast amounts of battlefield armaments abandoned by collapsing Iraqi units.

Or how to understand Israeli bombardment of UN schools in Gaza, and continuing attacks on Israel with drones by Hamas. What is the extent and impact of increasing geopolitical risk?

There also is the issue of plague – most immediately ebola in Africa. A few days ago, I spent the better part of a day in the Boston Airport, and, to pass the time, read the latest Dan Brown book about a diabolical scheme to release an aerosol epidemic of sorts. In any case, ebola is in a way a token of a range of threats that stand just outside the likely. For example, there is the problem of the evolution of immune strains of bacteria, with widespread prescription and use.

There also is the ever-bloating financial bubble that has emerged in the US and elsewhere, as a result of various tactics of central and other banks in reaction to the Great Recession, and behavior of investors.

Finally, there are longer range scientific and technological possibilities. From my standpoint, we are making a hash of things generally. But efforts at political reform, by themselves, usually fall short, unless paralleled by fundamental new possibilities in production or human organization. And the promise of radical innovation for the betterment of things has never seemed brighter.

I will be exploring some of these topics and options in coming posts this week and in coming weeks.

And I think by now I have discovered a personal truth through writing – one that resonates with other experiences of mine professionally and personally. And that is sometimes it is just when the way to going further seems hard to make out that concentration of thought and energies may lead to new insight.

accuracy of forecasts, analyzing seasonal effects, energy forecasting, gasoline price forecasts, long term forecasting, univariate forecast modeling approaches

Analyzing Complex Seasonal Patterns

When time series data are available in frequencies higher than quarterly or monthly, many forecasting programs hit a wall in analyzing seasonal effects.

Researchers from the Australian Monash University published an interesting paper in the Journal of the American Statistical Association (JASA), along with an R program, to handle this situation – what can be called “complex seasonality.”

I’ve updated and modified one of their computations – using weekly, instead of daily, data on US conventional gasoline prices – and find the whole thing pretty intriguing.

tbatschart

If you look at the color codes in the legend below the chart, it’s a little easier to read and understand.

Here’s what I did.

I grabbed the conventional weekly US gasoline prices from FRED. These prices are for “regular” – the plain vanilla choice at the pump. I established a start date of the first week in 2000, after looking the earlier data over. Then, I used tbats(.) in the Hyndman R Forecast package which readers familiar with this site know can be downloaded for use in the open source matrix programming language R.

Then, I established an end date for a time series I call newGP of the first week in 2012, forecasting ahead with the results of applying tbats(.) to the historic data from 2000:1 to 2012:1 where the second number refers to weeks which run from 1 to 52. Note that some data scrubbing is needed to shoehorn the gas price data into 52 weeks on a consistent basis. I averaged “week 53” with the nearest acceptable week (either 52 or 1 in the next year), and then got rid of the week 53’s.

The forecast for 104 weeks is shown by the solid red line in the chart above.

This actually looks promising, as if it might encode some useful information for, say, US transportation agencies.

A draft of the JASA paper is available as a PDF download. It’s called Forecasting time series with complex seasonal patterns using exponential smoothing and in addition to daily US gas prices, analyzes daily electricity demand in Turkey and bank call center data.

I’m only going part of the way to analyzing the gas price data, since I have not taken on daily data yet. But the seasonal pattern identified by tbats(.) from the weekly data is interesting and is shown below.

tbatsgasprice

The weekly frequency may enable us to “get inside” a mid-year wobble in the pattern with some precision. Judging from the out-of-sample performance of the model, this “wobble” can in some cases be accentuated and be quite significant.

Trignometric series fit to the higher frequency data extract the seasonal patterns in tbats(.), which also features other advanced features, such as a capability for estimating ARMA (autoregressive moving average) models for the residuals.

I’m not fully optimizing the estimation, but these results are sufficiently strong to encourage exploring the toggles and switches on the routine.

Another routine which works at this level of aggregation is the stlf(.) routine. This is uses STL decomposition described in some detail in Chapter 36 Patterns Discovery Based on Time-Series Decomposition in a collection of essays on data mining.

Thoughts

Good forecasting software elicits sort of addictive behavior, when initial applications of routines seem promising. How much better can the out-of-sample forecasts be made with optimization of the features of the routine? How well does the routine do when you look at several past periods? There is even the possibility of extracting further information from the residuals through bootstrapping or bagging at some point. I think there is no other way than exhaustive exploration.

The payoff to the forecaster is the amazement of his or her managers, when features of a forecast turn out to be spot-on, prescient, or what have you – and this does happen with good software. An alternative, for example, to the Hyndman R Forecast package is the program STAMP I also am exploring. STAMP has been around for many years with a version running – get this – on DOS, which appears to have had more features than the current Windows incarnation. In any case, I remember getting a “gee whiz” reaction from the executive of a regional bus district once, relating to ridership forecasts. So it’s fun to wring every possible pattern from the data.