Tag Archives: macroeconomic forecasts

Big Data, causes of deflation, Deep Questions, macroeconomic forecasting, real estate forecasts

Links – late August 2014

August 23, 2014 Clive Jones

Economics Articles, Some Theoretical, Some Applied

Who’s afraid of inflation? Not Fed Chair Janet Yellen At Jackson Hole, Yellen speech on labor market conditions states that 2 percent inflation is not a hard ceiling for the Fed.

Economist’s View notes a new paper which argues that deflation is simply unnecessary, because the conditions for a “helicopter drop” of money (Milton Friedman’s metaphor) are widely met.

Three conditions must be satisfied for helicopter money always to boost aggregate demand. First, there must be benefits from holding fiat base money other than its pecuniary rate of return. Second, fiat base money is irredeemable – viewed as an asset by the holder but not as a liability by the issuer. Third, the price of money is positive. Given these three conditions, there always exists – even in a permanent liquidity trap – a combined monetary and fiscal policy action that boosts private demand – in principle without limit. Deflation, ‘lowflation’ and secular stagnation are therefore unnecessary. They are policy choices.

Stiglitz: Austerity ‘Dismal Failure,’ New Approach Needed

US housing market loses momentum

Fannie Mae economists have downgraded their expectations for the U.S. housing market in the second half of this year, even though they are more optimistic about the prospects for overall economic growth.

How Detroit’s Water Crisis Is Part Of A Much Bigger Problem

“Have we truly become a society to where we’ll go and build wells and stuff in third world countries but we’ll say to hell with our own right here up under our nose, our next door neighbors, the children that our children play with?”

Economic harassment and the Ferguson crisis

According to .. [ArchCity Defenders] recent report .. the Ferguson court is a “chronic offender” in legal and economic harassment of its residents….. the municipality collects some $2.6 million a year in fines and court fees, typically from small-scale infractions like traffic violations…the second-largest source of income for that small, fiscally-strapped municipality….

And racial profiling appears to be the rule. In Ferguson, “86% of vehicle stops involved a black motorist, although blacks make up just 67% of the population,” the report states. “After being stopped in Ferguson, blacks are almost twice as likely as whites to be searched (12.1% vs. 7.9%) and twice as likely to be arrested.” But those searches result in the discovery of contraband at a much lower rate than searches of whites.

Once the process begins, the system begins to resemble the no-exit debtors’ prisons of yore. “Clients reported being jailed for the inability to pay fines, losing jobs and housing as a result of the incarceration, being refused access to the Courts if they were with their children or other family members….

“By disproportionately stopping, charging, and fining the poor and minorities, by closing the Courts to the public, and by incarcerating people for the failure to pay fines, these policies unintentionally push the poor further into poverty, prevent the homeless from accessing the housing, treatment, and jobs they so desperately need to regain stability in their lives, and violate the Constitution.” And they increase suspicion and disrespect for the system.

… the Ferguson court processed the equivalent of three warrants and $312 in fines per household in 2013.

Science

Astronauts find living organisms clinging to the International Space Station, and aren’t sure how they got there

A Mathematical Proof That The Universe Could Have Formed Spontaneously From Nothing

What caused the Big Bang itself? For many years, cosmologists have relied on the idea that the universe formed spontaneously, that the Big Bang was the result of quantum fluctuations in which the Universe came into existence from nothing.

Big Data Trends In 2014 (infographic – click to enlarge)

business trends, impacts of offshoring, macroeconomic forecasting

Walmart and China – Icons of the Age

August 8, 2014 Clive Jones

Here’s a puzzle. Why has the rate of new business creation declined fairly consistently for the past 20 or so years? This contrasts with the idea the US is an entrepreneurial nation, that it is the heartland of the “free market” and place where new jobs are created by myriads of startups.

This pattern is well-established, and is validated by several sources, including a recent Brookings report and Letter of the Chicago Federal Reserve Bank.

There are a cluster of causes, but two words are iconic – Walmart and China.

The Big Box store is a major factor behind the decline in small business startups, with Walmart being the leading superstore. And, for the most part, Big Box stores act as conduits for Chinese-made consumer goods.

Superstore Community Impact

In 2012, Walmart employed about 1 percent of the American workforce in its nearly 5000 stores, and clocked sales of $444 billion in the $17 trillion US economy overall. Certainly significant.

The story is that a Walmart comes to town and Mainstreet shutters up, becomes a ghost town. Gone are dozens of small proprietors and, many say, the sense of community. Instead we have endless aisles of cheap products from China – aisles where clerical assistance is scanty and the clerks often seem kind of lost in the vastness of it all.

Well, elements of this story are well-documented.

For example, there is a great website maintained by the Kennedy School at Harvard which updates the impact studies, even linking the coming of Walmart to an increase in obsesity somehow (lower wages, cheaper deep fat fried food?).

The first Walmart, Target, and K-Mart stores opened in 1962 with a focus on deep discounts and suburban locations.

In the five decades since, the American retail landscape and built environment have been profoundly altered. At the end of 2013, Wal-Mart had 4,700 stores in the United States and Puerto Rico, while Target operated nearly 1,800 locations and Kmart just over 1,200. Then there are smaller chains — still huge by any measure — as well as “category killers” and all the diverse residents of the shopping-mall ecosystem.

Selling a Cheaper Mousetrap: Wal-Mart’s Effect on Retail Prices shows that Wal-Mart’s price impact is large and can be beneficial to consumers.

The analysis combines data on the opening dates of all US Wal-Mart stores with average city-level retail prices of several narrowly-defined commonly-purchased goods over the period 1982-2002. I focus on 10 specific items likely to be sold at Wal-Mart stores and analyze their price dynamics in 165 US cities before and after Wal-Mart entry. I find price declines of 1.5%-3% for many products in the short run, with the largest price effects occurring for aspirin, laundry detergent, toothpaste and shampoo. Long-run price declines tend to be much larger, and in some specifications range from 7-13%. These effects are driven mostly by relatively small cities, which have high ratios of retail establishments to population.

The redoubtable Jerry Hausman of MIT argues that the Bureau of Labor Statistics (BLS) Consumer Price Index (CPI) calculations should be adjusted downward, when full account of Walmart and other superstores’ impact on retail is folded into the calculation.

The jobs impact has been widely studied and is difficult to assess in a controlled framework, but one thing is certain – pay is generally lower (partly since there are no more owner/operators). See also A Downward Push.

One question is whether this is a race to the bottom. But the antidote, should this be true, is not my immediate focus. Rather I want to try to trace the connections between the Big Box stores and other collateral and linked developments.

Big Box Stores as Channels For Chinese Goods

There’s a scrappy website that sends people to check where the goods in Target, K-Mart and Walmart come from – concluding that the vast majority of goods are made in China.

Again, Walmart is iconic.

The Wal-Mart effect claims that Wal-Mart was responsible for $27 billion in U.S. imports from China in 2006 and 11% of the growth of the total U.S. trade deficit with China between 2001 and 2006

Cargo containers are the innovation which makes this possible, and there is no clearer evidence for the supply sources for the Big Box stores than their record of imports with cargo containers.

This chart shows the number of standard-sized containers going to the top ten container importers.

So the supply-chain extends from Guandong Province to Kansas City, using cargo containers which are first shipped on the ocean, then loaded onto rail cars or trucks and moved to distribution centers, then put out on shelves in the Big Box stores.

China and US Manufacturing

It is no secret that Chinese tools, Chinese-made textiles, and Chinese-assembled electronics generally are cheaper than equivalent goods made in the United States. And it is not just China. There are other low wage supply areas, such as the Malquidores along the border with Mexico and in Guadalahara, as well as contract manufacturers in Vietnam and Eastern Europe.

But, again, China-made consumer goods are iconic, and they have displaced US-made products across a swath of markets.

The Economic Policy Institute (EPI) estimated in 2007 that Chinese-made goods for Walmart alone displaced 200,000 US jobs.

Altogether over the period 2001 to 2006 the US trade deficit with China is estimated to have eliminated 1.8 million US jobs.

Interestingly, this estimate, developed by a left-leaning research institute in the middle of the last decade, is now more-or-less echoed by mainstream economics – or at least by recent research published in the American Economic Review.

The China Syndrome: Local Labor Market Effects of Import Competition in the United States

We find that local labor markets that are exposed to rising low-income-country imports due to China’s rising competitiveness experience increased unemployment, decreased labor-force participation, and increased use of disability and other transfer benefits, as well as lower wages.

Just to be clear, here is a chart showing the dynamics of US manufacturing employment over the last 60 or so years.

US employment in manufacturing has fallen to the level it was in the late 1940’s.

Additionally, about 4 million of the 12 million persons currently employed in manufacturing are administrative and support personnel.

The US Trade Deficit

While we are viewing a collage of graphs on the US condition, let’s not forget this memorable chart of the US trade deficit.

Interestingly, the US balance of trade went south in synch with the activities of the World Trade Organization and agreements on opening trade around the world.

Of course, Walmart is not exclusively or even entirely responsible for these developments, but is part of the story.

Summing Up

Well, I began with the puzzle of the decline in new business formation in the US, focusing on Walmart and then Chinese products. Along the way, I highlight other possible connections, such as decline of US manufacturing employment and lower wages as well as prices.

Any exploration along these lines is bound to seem anecdotal, but at least there are numbers and charts to speak for themselves, to an extent.

On the one hand, we have new shopping centers with Big Box stores springing up all over the place, limiting opportunities for smaller businesses in the community. On the other hand, there is decline of US manufacturing, and loss of jobs accessible to persons without college education or special skills, jobs that can pay about double or triple what standard fast food sector jobs pay.

These twin trends clearly drive polarization of the US economy and society, mitigated to a degree by the lower cost of consumer goods supplied under this new system.

Looking ahead, can the magic of cheap imports can go on indefinitely? There are bottlenecks of workers with certain skills now emerging in China, and wages are rising there.

Possibly, contract manufacturers and joint ventures can move on to other locales, like Vietnam or Bangladesh.

But at some point, geopolitical risk and problems of infrastructure may slow outsourcing.

One thing – the rejoicing about “in-sourcing” – factories coming back state-side – seems premature, almost an example of wishful thinking. There are few numbers to back up the happy talk.

In the meanwhile, the slack thinking of American elites to the effect that it’s not necessary and may even be counterproductive to really educate a workforce that will predominately occupy low-paying service sectors jobs may come back to haunt us.

Investments in infrastructure are lagging. Federal spending on medical research has been cut back. Expenditures on R&D are flat to declining in many fields.

Practically the only “deal” available to many younger people without pedigrees or family resources is the US military. And production of defense goods is one area of US manufacturing where high performance activity continues – although offshore contracts there could, in my opinion, sap or even sabotage the ultimate performance of the equipment and material.

I’m working on companion pieces in coming weeks. I want to develop a big picture of the US economy and society at this moment, although I will continue to focus on business forecasting per se in the posts.

asset bubbles, celebrity forecasters, geopolitical risk, global business forecasts

Video Friday – the Outlook for the Rest of the Year

July 31, 2014 Clive Jones

Here is the latest Wells Fargo economic outlook video, featuring John Silvia – one of the top forecasters, according to Bloomberg.

Then, there is David Stockman, reminding us all about geopolitical and financial risks just at the time the Malaysian airliners got shot out of the sky.

Stockman, former Reagan Budget Director and Wall Street operator, has really become what commentators generally call an “iconoclast.”

And, I’m sorry, but I find it most useful to draw opinions from across a wide range. “Triangulation” is my best method to arrive at a perspective on the future.

accuracy of forecasts, business cycle, macroeconomic forecasting

The Business Cycle

July 29, 2014 Clive Jones

The National Bureau of Economic Research (NBER) has a standing committee which designates the start and finish of recessions, or more precisely, the dates of the peaks and troughs of the US business cycle.

And the NBER site maintains a complete record of the US business cycle, dating back to the middle 1800’s, as shown in the following tables.

Periods of contraction, from peak to trough, are typically shorter than periods of expansion – or the movement from previous trough to the next peak.

Since World War II, the average length of the business cycle, variously measured from trough to trough or from peak to peak, is more than 5 years.

Focusing on the current situation, we are interested in the length of time from the previous peak of the business cycle in December 2007 to the next peak. The longest peak to peak period was over the prosperity of the 1990’s, and lasted more than 10 years (128 months).

So, it would be unusual if the peak of this current business cycle were much later than 2017-2018.

In terms of predicting turning points, matters are complicated by the fact that, unlike many European countries, the NBER does not define a recession in terms of two consecutive quarters of decline in real GDP.

Rather, a recession is a significant decline in economic activity spread across the economy, lasting more than a few months, normally visible in real GDP, real income, employment, industrial production, and wholesale-retail sales.

But just predicting the onset of two consecutive quarters of decline in real GDP is challenging. Indeed, the record of macroeconomic forecasting is very poor in this regard.

Part of the problem with the concept of a “cycle” in this context is the irregularity of the fluctuations derived by standard filters and methods.

Harvey, for example, applies low band and pass Butterworth filters to US total investment and other macroeconomic series, deriving, at one pont, an investment “cycle” that looks like this.

So almost everything that makes a cycle useful in prediction is missing from this investment cycle. Thus, one cannot conclude that a turning point will occur, when the amplitude of the cycle is reached, since the amplitudes of these quasi-cycles vary considerably. Similarly, the “period” of the cycle is by no means fixed, but is basically stochastic, with a certain variance sometimes expressed as a “hyperparameter.” Only a certain quality of smoothness presents itself, and, of course, is a result of the filtering parameters that are applied.

In my opinion, industry cycles make a certain amount of sense, for particular industries, over particular spans of time. What I mean is that identification of such industry cycles improves predictability of the underlying series – be it sales or inventories or what have you.

The business cycle, on the other hand, is something of a metaphor, or maybe just an evocative phrase.

True, there are periods of economic contraction and periods of expansion.

But the extraction of macroeconomic cycles often does not improve predictability, because the fluctuations so identified are highly irregular from a number of different viewpoints.

I’ve sort of confirmed this is a quantitative sense by applying various cycle-extraction softwares to US real GDP to see whether any product or approach gave a hint that the Great Recession which began in 2008 would (a) occur, and (b) be as dramatic as it was. So far, no go.

And, of course, Ng points out that the Great Recession was fundamentally different than, say, recessions in the 1960’s sand 1970’s in that it was a balance sheet recession.

business cycle, cycles, Hodrick Prescott filter, macroeconomic forecasting

The Consumer Durable Inventory Cycle – Canary in the Coal Mine?

July 28, 2014 Clive Jones

I’m continuing this week with posts about cycles and, inevitably, need to address one very popular method of extracting cycles from time series data – the Hodrick-Prescott (HP) filter.

Recently, I’ve been exploring inventory cycles, hoping to post something coherent.

I think I hit paydirt, as they say in gold mining circles.

Here is the cycle component extracted from consumer durable inventories (not seasonally adjusted) from the Census manufacturing with a Hodrick-Prescott filter. I use a Matlab implementation here called hpfilter.

In terms of mechanics, the HP filter extracts the trend and cyclical component from a time series by minimizing an expression, as described by Wikipedia –

What’s particularly interesting to me is that the peak of the two cycles in the diagram are spot-on the points at which the business cycle goes into recession – in 2001 and 2008.

Not only that, but the current consumer durable inventory cycle is credibly peaking right now and, based on these patterns, should go into a downward movement soon.

Of course, amplitudes of these cycles are a little iffy.

But the existence of a consumer durable cycle configured along these lines is consistent with the literature on inventory cycles, which emphasizes stockout-avoidance and relatively long pro-cyclical swings in inventories.

accuracy of forecasts, Big Data, forecasting research, macroeconomic forecasting

Seasonal Adjustment – A Swirl of Controversies

July 16, 2014 Clive Jones

My reading on procedures followed by the Bureau of Labor Statistics (BLS) and the Bureau of Economic Analysis (BLS) suggests some key US macroeconomic data series are in a profound state of disarray. Never-ending budget cuts to these “non-essential” agencies, since probably the time of Bill Clinton, have taken their toll.

For example, for some years now it has been impossible for independent analysts to verify or replicate real GDP and many other numbers issued by the BEA, since, only SA (seasonally adjusted) series are released, originally supposedly as an “economy measure.” Since estimates of real GDP growth by quarter are charged with political significance in an Election Year, this is a potential problem. And the problem is immediate, since the media naturally will interpret a weak 2^nd quarter growth – less than, say, 2.9 percent – as a sign the economy has slipped into recession.

Evidence of Political Pressure on Government Statistical Agencies

John Williams has some fame with his site Shadow Government Statistics. But apart from extreme stances from time to time (“hyperinflation”), he does document the politicization of the BLS Consumer Price Index (CPI).

In a recent white paper called No. 515—PUBLIC COMMENT ON INFLATION MEASUREMENT AND THE CHAINED-CPI (C-CPI), Williams cites Katharine Abraham, former commissioner of the Bureau of Labor Statistics, when she notes,

“Back in the early winter of 1995, Federal Reserve Board Chairman Alan Greenspan testified before the Congress that he thought the CPI substantially overstated the rate of growth in the cost of living. His testimony generated a considerable amount of discussion. Soon afterwards, Speaker of the House Newt Gingrich, at a town meeting in Kennesaw, Georgia, was asked about the CPI and responded by saying, ‘We have a handful of bureaucrats who, all professional economists agree, have an error in their calculations. If they can’t get it right in the next 30 days or so, we zero them out, we transfer the responsibility to either the Federal Reserve or the Treasury and tell them to get it right.’”[v]

Abraham is quoted in newspaper articles as remembering sitting in Republican House Speaker Newt Gingrich’s office:

“ ‘He said to me, If you could see your way clear to doing these things, we might have more money for BLS programs.’ ” [vi]

The “things” in question were to move to quality adjustments for the basket of commodities used to calculate the CPI. The analogue today, of course, is the chained-CPI measure which many suggest is being promoted to slow cost-of-living adjustments in Social Security payments.

Of course, the “real” part in real GDP is linked with the CPI inflation outlook though a process supervised by the BEA.

Seasonal Adjustment Procedures for GDP

Here is a short video by Johnathan H. Wright, a young economist whose Unseasonal Seasonals? is featured in a recent issue of the Brookings Papers on Economic Activity.

Wright’s research is interesting to forecasters, because he concludes that algorithms for seasonally adjusting GDP should be selected based on their predictive performance.

Wright favors state-space models, rather than the moving-average techniques associated with the X-12 seasonal filters that date back to the 1980’s and even the 1960’s.

Given BLS methods of seasonal adjustment, seasonal and cyclical elements are confounded in the SA nonfarm payrolls series, due to sharp drops in employment concentrated in the November 2008 to March 2009 time window.

The upshot – initially this effect pushed reported seasonally adjusted nonfarm payrolls up in the first half of the year and down in the second half of the year, by slightly more than 100,000 in both cases…

One of his prime exhibits compares SA and NSA nonfarm payrolls, showing that,

The regular within-year variation in employment is comparable in magnitude to the effects of the 1990–1991 and 2001 recessions. In monthly change, the average absolute difference between the SA and NSA number is 660,000, which dwarfs the normal month-over-month variation in the SA data.

The basic procedure for this data and most releases since 2008-2009 follows what Wright calls the X-12 process.

The X-12 process focuses on certain types of centered moving averages with a fixed weights, based on distance from the central value.

A critical part of the X-12 process involves estimating the seasonal factors by taking weighted moving averages of data in the same period of different years. This is done by taking a symmetric n-term moving average of m-term averages, which is referred to as an n × m seasonal filter. For example, for n = m = 3, the weights are 1/3 on the year in question, 2/9 on the years before and after, and 1/9 on the two years before and after.16 The filter can be a 3 × 1, 3 × 3, 3 × 5, 3 × 9, 3 × 15, or stable filter. The stable filter averages the data in the same period of all available years. The default settings of the X-12…involve using a 3 × 3, 3 × 5, or 3 × 9 seasonal filter, depending on [various criteria]

Obviously, a problem arises at the beginning and at the end of the time series data. A work-around is to use an ARIMA model to extend the time series back and forward in time sufficiently to calculate these centered moving averages.

Wright shows these arbitrary weights and time windows lead to volatile seasonal adjustments, and that, predictively, the BEA and BLS would be better served with a state-space model based on the Kalman filter.

Loopy seasonal adjustment leads to controversy that airs on the web – such as this piece by Zero Hedge from 2012 which highlights the “ficititious” aspect of seasonal adjustments of highly tangible series, such as the number of persons employed –

What is very notable is that in January, absent BLS smoothing calculation, which are nowhere in the labor force, but solely in the mind of a few BLS employees, the real economy lost 2,689,000 jobs, while net of the adjustment, it actually gained 243,000 jobs: a delta of 2,932,000 jobs based solely on statistical assumptions in an excel spreadsheet!

To their credit, Census now documents an X-13ARIMA-SEATS Seasonal Adjustment Program with software incorporating elements of the SEATS procedure originally developed at the Bank of Spain and influenced by the state space models of Andrew Harvey.

Maybe Wright is getting some traction.

What Is The Point of Seasonal Adjustment?

You can’t beat the characterization, apparently from the German Bundesbank, of the purpose and objective of “seasonal adjustment.”

..seasonal adjustment transforms the world we live in into a world where no seasonal and working-day effects occur. In a seasonally adjusted world the temperature is exactly the same in winter as in the summer, there are no holidays, Christmas is abolished, people work every day in the week with the same intensity (no break over the weekend)..

I guess the notion is that, again, if we seasonally adjust and see a change in direction of a time series, why then it probably is a change in trend, rather than from special uses of a certain period.

But I think most of the professional forecasting community is beyond just taking their cue from a single number. It would be better to have the raw or not seasonally adjusted (NSA) series available with every press release, so analysts can apply their own models.

macroeconomic forecasting, national income shares and trends, wealth and income distribution

The Class Struggle

July 3, 2014 Clive Jones

This chart is about what kind of world we live in. It’s drawn from the official source of the US national income accounts – the Bureau of Economic Analysis (BEA).

The chart shows the shares of national income going to compensation of employees and to corporate profits of domestic industries (with inventory valuation and capital consumption adjustments).

Note the vertical axes. On the left, there is the axis for the share for employee compensation – the blue line – which varies from 53-59 percent. The share for profits, which is on the order of 5-10 percent, is on the right vertical axis.

There is a high negative correlation between these two series, approximately -0.85.

Also, the scale of the changes in the shares of each are roughly of the same size, although not exactly.

Finally, the turning points in corporate profits and employee compensation line up in almost every case.

It’s important to note that employee compensation and profits do not simply sum to 100 percent; there are other categories of national income, and these have lower correlations with employee compensation.

There is much lower correlation between employee compensation and the sum of interest plus rents – both key components of property income.

There is also less (negative) correlation between proprietors income, which is about the same size as the corporate profit share, and employee compensation (-0.55). Presumeably, this is because proprietors income includes more sole proprietorships and family businesses; also, because wages for these companies may be lower than the corporate sector.

Of course, corporate profits have gone ballistic since 2008-2009, outpacing the increase in proprietors income.

So what this looks like is that increases in corporate profits come out of the share paid to employees somehow. Shades of Karl Marx!

In titling a post like this, I proceed cautiously, thinking some of my mentors in economics years back – Ray Marshall, A.G. Hart, and, briefly, W.W. Rostow to name a few.

Rostow used to talk of a Social Compact forged between labor and business after World War II. Fewer strikes and more automatic wage increases. That clearly has ended.

accuracy of forecasts, business cycle, business trends, macroeconomic forecasting

Prospects for the 2nd Quarter 2014 and the Rest of the Year

July 1, 2014 Clive Jones

Well, it’s the first day of the 3^rd quarter 2014, and time to make an assessment of what happened in Q2 and also what is likely to transpire the rest of the year.

The Big Write-Down

Of course, the 1^st quarter 2014 numbers were surprisingly negative – and almost no one saw that coming. Last Wednesday (June 25) the Bureau of Economic Analysis (BEA) revised last estimates of 1^st quarter real GDP down a -2.9 percent decrease on a quarter-by-quarter basis.

The Accelerating Growth Meme

Somehow media pundits and the usual ranks of celebrity forecasters seem heavily invested in the “accelerating growth” meme in 2014.

Thus, in mid-June Mark Zandi of Moody’s tries to back up Moody’s Analytics U.S. Macro Forecast calling for accelerating growth the rest of the year, writing,

The economy’s strength is increasingly evident in the job market. Payroll employment rose to a new high in May as the U.S. finally replaced all of the 8.7 million jobs lost during the recession, and job growth has accelerated above 200,000 per month since the start of the year. The pace of job creation is almost double that needed to reduce unemployment, even with typical labor force gains. More of the new positions are also better paying than was the case earlier in the recovery.

After the BEA released its write-down numbers June 25, the Canadian Globe and Mail put a happy face on everything, writing that The US Economy is Back on Track since,

Hiring, retail sales, new-home construction and consumer confidence all rebounded smartly this spring. A separate government report Wednesday showed inventories for non-defense durable goods jumped 1 per cent in May after a 0.4-per-cent increase the previous month.

Forecasts for the Year Being Cut-Back

On the other hand, the International Monetary Fund (IMF) cut its forecast for US growth,

In its annual review of the U.S. economy, the IMF cut its forecast for U.S. economic growth this year by 0.8 percentage point to 2%, citing a harsh winter, a struggling housing market and weak international demand for the country’s products.

Some Specifics

The first thing to understand in this context is that employment is usually a lagging indicator of the business cycle. Ahead of the Curve makes this point dramatically with the following chart.

The chart shows employment change and growth lag changes in the business cycle. Thus, note that the green line peaks after growth in personal consumption expenditures in almost every case, where these growth rates are calculated on a year-over-year basis.

So Zandi’s defense of the Moody’s Analytics accelerating growth forecast for the rest of 2014 has to be taken with a grain of salt.

It really depends on other things – whether for example, retail sales are moving forward, what’s happening in the housing market (to new-home construction and other variables), also to inventories and durable goods spending. Also have exports rebounded, and imports (a subtraction from GDP) been reined in?

Retail Sales

If there is going to be accelerating economic growth, consumer demand, which certainly includes retail sales, has to improve dramatically.

However, the picture is mixed with significant rebound in sales in April, but lower-than-expected retail sales growth in May.

Bloomberg’s June take on this is in an article Cooling Sales Curb Optimism on U.S. Growth Rebound: Economy.

The US Census report estimates U.S. retail and food services sales for May, adjusted for seasonal variation and holiday and trading-day differences, but not for price changes, were $437.6 billion, an increase of 0.3 percent (±0.5)* from the previous month.

Durable Goods Spending

In the Advance Report on Durable Goods Manufacturers’ Shipments, Inventories and Orders May 2014 we learn that,

New orders for manufactured durable goods in May decreased $2.4 billion or 1.0 percent to $238.0 billion, the U.S. Census Bureau announced today.

On the other hand,

Shipments of manufactured durable goods in May, up four consecutive months, increased $0.6 billion or 0.3 percent to $238.6 billion

Of course, shipments are a lagging indicator of the business cycle.

Finally, inventories are surging –

Inventories of manufactured durable goods in May, up thirteen of the last fourteen months, increased $3.8 billion or 1.0 percent to $397.8 billion. This was at the highest level since the series was first published on a NAICS basis and followed a 0.3 percent April increase.

Inventory accumulation is a coincident indicator (in a negative sense) of the business cycle, according to NBER documents.

New Home Construction

From the Joint Release U.S. Department of Housing and Urban Development,

Privately-owned housing units authorized by building permits in May were at a seasonally adjusted annual rate of 991,000. This is 6.4 percent (±0.8%) below the revised April rate of 1,059,000 and is 1.9 percent (±1.4%) below the May 2013 estimate of 1,010,000…

Privately-owned housing starts in May were at a seasonally adjusted annual rate of 1,001,000. This is 6.5 percent (±10.2%)* below the

revised April estimate of 1,071,000, but is 9.4 percent (±11.0%)* above the May 2013 rate of 915,000.

Single-family housing starts in May were at a rate of 625,000; this is 5.9 percent (±12.7%)* below the revised April figure of 664,000.

No sign of a rebound in new home construction in these numbers.

Exports and Imports

The latest BEA report estimates,

April exports were $0.3 billion less than March exports of $193.7 billion. April imports were $2.7 billion more than March imports of $237.8 billion…

Here is a several month perspective.

Essentially, the BEA trade numbers suggest the trade balance deteriorated March to April with a sharp uptick in imports and a slight drop in exports.

Summary

Well, it’s not a clear picture. The economy is teetering on the edge of a downturn, which it may still escape.

Clearly, real growth in Q2 has to be at least 2.9 percent in order to counterbalance the drop in Q1, or else the first half of 2014 will show a net decrease.

CNN offers this with an accompanying video –

Goldman Sachs economists trimmed second quarter tracking GDP to 3.5 percent from 4.1 percent, and Barclays economists said tracking GDP for the second quarter fell to 2.9 percent from 4 percent. At a pace below 3 percent, the economy could show contraction for the first half due to the steep first quarter decline of 2.9 percent.

top picture http://www.bbc.com/news/magazine-24045598

accuracy of forecasts, business trends, forecasting turning points, global business forecasts, predicting recessions

Surprising Revision of First Quarter GDP

June 29, 2014 Clive Jones

I showed a relative this blog a couple of days ago, and, wanting “something spicy,” I pulled up The Record of Failure to Predict Recessions is Virtually Unblemished. The lead picture, as for this post, is Peter Sellers in his role as “Chauncey Gardiner” in Being There. Sellers played a simpleton mistaken for a savant, who would say things that everyone thought was brilliant, such as “There will be growth in the Spring.”

Well, last Wednesday, the US Bureau of Economic Analysis released a third revision of its estimate of the 1^st quarter 2014 real GDP growth – down from an initial estimate of a positive .1 percent to -2.9 percent growth at an annual rate.

The BEA News Release says,

Real gross domestic product — the output of goods and services produced by labor and property located in the United States — decreased at an annual rate of 2.9 percent in the first quarter of 2014 according to the “third” estimate released by the Bureau of Economic Analysis….

The decrease in real GDP in the first quarter primarily reflected negative contributions from private inventory investment, exports, state and local government spending, nonresidential fixed investment, and residential fixed investment that were partly offset by a positive contribution from PCE. Imports, which are a subtraction in the calculation of GDP, increased.

Looking at this graph of quarterly real GDP growth rates for the past several years, it’s clear that a -2.9 percent quarter-over-quarter change is a significant size.

Again, macroeconomic forecasters were caught off guard.

In February of this year, the Survey of Professional Forecasters released its 1^st Quarter 2014 consensus forecasts with numbers like –

Some SPF participants do predict 2014 overall will be a year of recession, as the following chart shows, but they are a tiny minority.

A downward revision of almost 3 percentage points on the part of the BEA and almost 5 percent change for the median SPF forecast is poor performance indeed.

One hears things sped up in Q2, but on what basis I do not really know – and I am thinking of tracking key markets in future posts, such as housing, consumer spending, and so forth.

My feeling is that the quandary of the Fed – its desperate need to wind down asset purchases and restore interest rates to historic levels –creates an environment for a kind of “happy talk.”

Here’s some history on the real GDP.

accuracy of forecasts, Big Data, boosting, macroeconomic forecasting, time series forecasting, yield curve

Leading Indicators

June 2, 2014 Clive Jones

One value the forecasting community can provide is to report on the predictive power of various leading indicators for key economic and business series.

The Conference Board Leading Indicators

The Conference Board, a private, nonprofit organization with business membership, develops and publishes leading indicator indexes (LEI) for major national economies. Their involvement began in 1995, when they took over maintaining Business Cycle Indicators (BCI) from the US Department of Commerce.

For the United States, the index of leading indicators is based on ten variables: average weekly hours, manufacturing, average weekly initial claims for unemployment insurance, manufacturers’ new orders, consumer goods and materials, vendor performance, slower deliveries diffusion index,manufacturers’ new orders, nondefense capital goods, building permits, new private housing units, stock prices, 500 common stocks, money supply, interest rate spread, and an index of consumer expectations.

The Conference Board, of course, also maintains coincident and lagging indicators of the business cycle.

This list has been imprinted on the financial and business media mind, and is a convenient go-to, when a commentator wants to talk about what’s coming in the markets. And it used to be that a rule of thumb that three consecutive declines in the Index of Leading Indicators over three months signals a coming recession. This rule over-predicts, however, and obviously, given the track record of economists for the past several decades, these Conference Board leading indicators have questionable predictive power.

Serena Ng Research

What does work then?

Obviously, there is lots of research on this question, but, for my money, among the most comprehensive and coherent is that of Serena Ng, writing at times with various co-authors.

So in this regard, I recommend two recent papers

Boosting Recessions

Facts and Challenges from the Great Recession for Forecasting and Macroeconomic Modeling

The first paper is most recent, and is a talk presented before the Canadian Economic Association (State of the Art Lecture).

Hallmarks of a Serena Ng paper are coherent and often quite readable explanations of what you might call the Big Picture, coupled with ambitious and useful computation – usually reporting metrics of predictive accuracy.

Professor Ng and her co-researchers apparently have determined several important facts about predicting recessions and turning points in the business cycle.

For example –

Since World War II, and in particular, over the period from the 1970’s to the present, there have been different kinds of recessions. Following Ng and Wright, ..business cycles of the 1970s and early 80s are widely believed to be due to supply shocks and/or monetary policy. The three recessions since 1985, on the other hand, originate from the financial sector with the Great Recession of 2008-2009 being a full-blown balance sheet recession. A balance sheet recession involves, a sharp increase in leverage leaves the economy vulnerable to small shocks because, once asset prices begin to fall, financial institutions, firms, and households all attempt to deleverage. But with all agents trying to increase savings simultaneously, the economy loses demand, further lowering asset prices and frustrating the attempt to repair balance sheets. Financial institutions seek to deleverage, lowering the supply of credit. Households and firms seek to deleverage, lowering the demand for credit.
Examining a monthly panel of 132 macroeconomic and financial time series for the period 1960-2011, Ng and her co-researchers find that .. the predictor set with systematic and important predictive power consists of only 10 or so variables. It is reassuring that most variables in the list are already known to be useful, though some less obvious variables are also identified. The main finding is that there is substantial time variation in the size and composition of the relevant predictor set, and even the predictive power of term and risky spreads are recession specific. The full sample estimates and rolling regressions give confidence to the 5yr spread, the Aaa and CP spreads (relative to the Fed funds rate) as the best predictors of recessions.

So, the yield curve, a old favorite when it comes to forecasting recessions or turning points in the business cycle, performs less well in the contemporary context – although other (limited) research suggests that indicators combining facts about the yield curve with other metrics might be helpful.

And this exercise shows that the predictor set for various business cycles changes over time, although there are a few predictors that stand out. Again,

there are fewer than ten important predictors and the identity of these variables change with the forecast horizon. There is a distinct difference in the size and composition of the relevant predictor set before and after mid-1980. Rolling window estimation reveals that the importance of the term and default spreads are recession specific. The Aaa spread is the most robust predictor of recessions three and six months ahead, while the risky bond and 5yr spreads are important for twelve months ahead predictions. Certain employment variables have predictive power for the two most recent recessions when the interest rate spreads were uninformative. Warning signals for the post 1990 recessions have been sporadic and easy to miss.

Let me throw in my two bits here, before going on in subsequent posts to consider turning points in stock markets and in more micro-focused or industry time series.

At the end of “Boosting Recessions” Professor Ng suggests that higher frequency data may be a promising area for research in this field.

My guess is that is true, and that, more and more, Big Data and data analytics from machine learning will be applied to larger and more diverse sets of macroeconomics and business data, at various frequencies.

This is tough stuff, because more information is available today than in, say, the 1970’s or 1980’s. But I think we know what type of recession is coming – it is some type of bursting of the various global bubbles in stock markets, real estate, and possibly sovereign debt. So maybe more recent data will be highly relevant.

Business Forecasting