Category Archives: macroeconomic forecasting

interface between technology and employment, macroeconomic forecasting, robotics

Automation, Robotics -Trends and Impacts

Trying to figure out the employment impacts of automation, computerization, and robotics is challenging, to say the least.

There are clear facts, such as the apparent permanent loss of jobs in US manufacturing since the early 1990’s.

MANEMP

But it would be short-sighted to conclude these jobs have been lost to increased use of computers and robots in production.

That’s because, for one thing, you might compare a chart like the above with statistics on Chinese manufacturing.

Chinesemanemp

Now you can make a case – if you focus on the urban Chinese manufacturing employment – that these two charts are more or less mirror images of one another in recent years. That is urban manufacturing employment in China, according the US BLS, increased about 4 mllion 2002-2009, while US manufacturing employment dropped by about that amount over the same period.

Of course, there are other off-shore manufacturing sites of importance, such as the maquiladoras along the US border with Mexico.

But what brings robotics into focus for me is that significant automation and robotics are being installed in factories in China.

Terry Guo, head of Foxconn – the huge Chinese contract manufacturer making the I-phone and many other leading electronics products – has called for installation of a million industrial robots in Foxconn factories over the next few years.

In fact, Foxconn apparently is quietly partnering with Google to help bring its vision of robotics to life.

Decoupling of Productivity and Employment?

Erik Brynjolfsson at MIT is an expert on the productivity implications of information technology (IT).

About a year ago, the MIT Technology Review ran an article How Technology Is Destroying Jobs featuring the perspective developed recently by Brynjolfsson that there is increasingly a disconnect between productivity growth and jobs in the US.

The article featured two infographics – one of which I reproduce here.

info

There have been highly focused studies of the effects of computerization on specific industries.

Research published just before the recent economic crisis did an in-depth regarding automation or computerization in a “valve industry,” arriving at three, focused findings.

First, plants that adopt new IT-enhanced equipment also shift their business strategies by producing more customized valve products. Second, new IT investments improve the efficiency of all stages of the production process by reducing setup times, run times, and inspection times. The reductions in setup times are theoretically important because they make it less costly to switch production from one product to another and support the change in business strategy to more customized production. Third, adoption of new IT-enhanced capital equipment coincides with increases in the skill requirements of machine operators, notably technical and problem-solving skills, and with the adoption of new human resource practices to support these skills

This is the positive side of the picture.

No more drudgery on assembly lines with highly repetitive tasks. Factory workers are being upgraded to computer operatives.

More to follow.

accuracy of forecasts, developed country forecast, Federal Reserve policies and impacts, macroeconomic forecasting

US Growth Stalls

The US Bureau of Economic Analysis (BEA) announced today that,

Real gross domestic product — the output of goods and services produced by labor and property located in the United States — increased at an annual rate of 0.1 percent in the first quarter (that is, from the fourth quarter of 2013 to the first quarter of 2014), according to the “advance” estimate released by the Bureau of Economic Analysis.  In the fourth quarter, real GDP increased 2.6 percent.

This flatline growth number is in stark contrast to the median forecast of 83 economists surveyed by Bloomberg, which called for a 1.2 percent increase for the first quarter.

Bloomberg writes in a confusingly titled report – Dow Hits Record as Fed Trims Stimulus as Economy Improves

The pullback in growth came as snow blanketed much of the eastern half of the country, keeping shoppers from stores, preventing builders from breaking ground and raising costs for companies including United Parcel Service Inc. Another report today showing a surge in regional manufacturing this month adds to data on retail sales, production and employment that signal a rebound is under way as temperatures warm.

Here’s is the BEA table of real GDP, along with the advanced estimates for the first quarter 2014 (click to enlarge).

usgdp

The large negative slump in investment in equipment (-5.5) indicates to me something more is going on than bad weather.

Indeed, Econbrowser notes that,

Both business fixed investment and new home construction fell in the quarter, which would be ominous developments if they’re repeated through the rest of this year. And a big drop in exports reminds us that America is not immune to weakness elsewhere in the world.

Even the 2% growth in consumption spending is not all that encouraging. As Bricklin Dwyer of BNP Paribas noted, 1.1% of that consumption growth– more than half– was attributed to higher household expenditures on health care.

What May Be Happening

I think there is some amount of “happy talk” about the US economy linked to the urgency about reducing Fed bond purchases. So just think of what might happen if the federal funds rate is still at the zero bound when another recession hits. What tools would the Fed have left? Somehow the Fed has to position itself rather quickly for the inevitable swing of the business cycle.

I have wondered, therefore, whether some of the pronouncements recently from the Fed did not have a unrealistic slant.

So, as the Fed unwinds quantitative easing (QE), dropping bond (mortgage-backed securities) purchases to zero, surely there will be further impacts on the housing markets.

Also, China is not there this time to take up the slack.

And it is always good to remember that new employment numbers are basically a lagging indicator of the business cycle.

Let’s hope for a better second and third quarter, and that this flatline growth for the first quarter is a blip.

inflation forecasts, macroeconomic forecasting, multivariate modeling, univariate forecast modeling approaches

Inflation/Deflation – 3

Business forecasters often do not directly forecast inflation, but usually are consumers of inflation forecasts from specialized research organizations.

But there is a level of literacy that is good to achieve on the subject – something a quick study of recent, authoritative sources can convey.

A good place to start is the following chart of US Consumer Price Index (CPI) and the GDP price index, both expressed in terms of year-over-year (yoy) percentage changes. The source is the St. Louis Federal Reserve FRED data site.

InflationFRED

The immediate post-WW II period and the 1970;s and 1980’s saw surging inflation. Since somewhere in the 1980’s and certainly after the early 1990’s, inflation has been on a downward trend.

Some Stylized Facts About Forecasting Inflation

James Stock and Mark Watson wrote an influential NBER (National Bureau of Economic Research) paper in 2006 titled Why Has US Inflation Become Harder to Forecast.

These authors point out that the rate of price inflation in the United States has become both harder and easier to forecast, depending on one’s point of view.

On the one hand, inflation (along with many other macroeconomic time series) is much less volatile than it was in the 1970s or early 1980s, and the root mean squared error of naïve inflation forecasts has declined sharply since the mid-1980s. In this sense, inflation has become easier to forecast: the risk of inflation forecasts, as measured by mean squared forecast errors (MSFE), has fallen.

On the other hand, multivariate forecasting models inspired by economic theory – such as the Phillips curve –lose ground to univariate forecasting models after the middle 1980’s or early 1990’s. The Phillips curve, of course, postulates a tradeoff between inflation and economic activity and is typically parameterized in inflationary expectations and the gap between potential and actual GDP.

A more recent paper Forecasting Inflation evaluates sixteen inflation forecast models and some judgmental projections. Root mean square prediction errors (RMSE’s) are calculated in quasi-realtime recursive out-of-sample data – basically what I would call “backcasts.” In other words, the historic data is divided into training and test samples. The models are estimated on the various possible training samples (involving, in this case, consecutive data) and forecasts from these estimated models are matched against the out-of-sample or test data.

The study suggests four principles. 

  1. Subjective forecasts do the best
  2. Good forecasts must account for a slowly varying local mean.
  3. The Nowcast is important and typically utilizes different techniques than standard forecasting
  4.  Heavy shrinkage in the use of information improves inflation forecasts

Interestingly, this study finds that judgmental forecasts (private sector surveys and the Greenbook) are remarkably hard to beat. Otherwise, most of the forecasting models fail to consistently trump a “naïve forecast” which is the average inflation rate over four previous periods.

What This Means

I’m willing to offer interpretations of these findings in terms of (a) the resilience of random walk models, and (b) the eclipse of unionized labor in the US.

So forecasting inflation as an average of several previous values suggests the underlying stochastic process is some type of random walk. Thus, the optimal forecast for a simple random walk is the most currently observed value. The optimal forecast for a random walk with noise is an exponentially weighted average of the past values of the series.

The random walk is a recurring theme in many macroeconomic forecasting contexts. It’s hard to beat.

As far as the Phillips curve goes, it’s not clear to me that the same types of tradeoffs between inflation and unemployment exist in the contemporary US economy, as did, say, in the 1950’s or 1960’s. The difference, I would guess, is the lower membership in and weaker power of unions. After the 1980’s, things began to change significantly on the labor front. Companies exacted concessions from unions, holding out the risk that the manufacturing operation might be moved abroad to a lower wage area, for instance. And manufacturing employment, the core of the old union power, fell precipitously.

As far as the potency of subjective forecasts – I’ll let Faust and Wright handle that. While these researchers find what they call subjective forecasts beat almost all the formal modeling approaches, I’ve seen other evaluations calling into question whether any inflation forecast beats a random walk approach consistently. I’ll have to dig out the references to make this stick.

causes of deflation, financial forecasting, forecasting inflation, Japanese economy, macroeconomic forecasting

Inflation/Deflation-2

The following chart, courtesy of the Bank of Japan (BOJ), shows inflation and deflation dynamics in Japan since the 1980’s.

Japandeflation

There is interesting stuff in this chart, not the least of which is the counterintuitive surge in Japanese consumer prices in the Great Recession (2008 et passim).

Note, however, that the GDP deflator fell below zero in 1994, returning to positive territory only briefly around 2009. The other index in the chart – the DCGPI  –  is a Domestic Corporate Goods Price Index calculated by Japanese statistical agencies.

The Japanese experience with deflation is relevant to deflation dynamics in the Eurozone, and has been central to the thinking and commentary of US policymakers and macroeconomic/monetary theorists, such as Benjamin Bernanke.

The conventional wisdom explains inflation and deflation with the Phillips Curve. Thus, in one variant, inflation is projected to be a function of inflationary expectations, the output gap between potential and actual GDP, and other factors – decline in export prices, demographic changes, “unlucky” historical developments, or institutional issues in the financial sector.

It makes a big difference how you model inflationary expectations in this model, as John Williams points out in a Federal Reserve Bank of San Francisco Economic Letter.

If inflationary expectations are unanchored, a severe recession can lead to a deflationary spiral.

The logic is as follows: In the early stage of recession, the emergence of slack causes the inflation rate to dip. The resulting lower inflation rate prompts people to reduce their future inflation expectations. Continued economic slack causes the inflation rate to fall still further. If the recession is severe and long enough, this process eventually will cause prices to fall and then spiral lower and lower, resulting in ever-faster deflation rates. The deflation rate stabilizes only when slack is eliminated. And inflation turns positive again only after a sustained period of tight labor markets.

This contrasts with “well anchored” inflationary expectations, where people expect the monetary authorities will step in and end deflationary episodes at some point. In technical time series terms – inflation time series exhibit longer term returns to mean values and this acts as a magnet pulling prices up in some deflationary circumstances. Janet Yellen and her husband George Ackerlof have commented on this type of dynamic in inflationary expectations.

The Industrial Side of the Picture

The BOJ Working Paper responsible for the introductory chart also considers “other factors” in the Phillips Curve explanation, presenting a fascinating table.

JapanGDPdeflatiorbreakout

The huge drop in prices of electric machinery in Japan over 1990-2009 caught my attention.

The collapse in electric machinery prices represent changed conditions in export markets with cheaper and high quality electronics manufactured in China and other areas harboring contract electronics manufacturing.

Could this be a major contributor to persisting Japanese deflation, initially triggered obviously by massive drops in Japanese real estate in the earlyi 1990’s?

An interesting paper by Haruhiko Murayama of the Kyoto Research Institute – Reality and Cause of Deflation in Japan – makes a persuasive case for just that conclusion.

Murayama argues competition from China and other lower wage electronics producers is a major factor in continuing Japanese deflation.

The greatest cause of the deflation is a lack of demand, which in turn is attributable to the fact that emerging countries such as China, South Korea and Taiwan have come to manufacture inexpensive high-quality electrical products by introducing new equipment and by taking advantage of their cheap labor. While competing with emerging countries, Japanese electrical machinery makers have been forced to lower their export prices. In addition, an influx of foreign products has reduced their domestic sales, and as a result, overall earnings and demand in Japan have declined, leading to continuous price drops.

He goes on to say that,

..Japan is the only developed country whose electric machinery makers have been struggling because of the onslaught of competition from emerging countries. General Electric of the United States, which is known as a company founded by Thomas Edison and which was previously the largest electric machinery maker in the world, has already shifted its focus to the aircraft and nuclear industries, after facing intense competition with Japanese manufacturers. Other U.S. companies such as RCA (Radio Corporation of America), Motorola and Zenith no longer exist for reasons such as because they failed or were acquired by Japanese companies. The situation is similar in Europe. Consequently, whereas electric machinery accounts for as much as 19.5% of Japan’s nominal exports, equivalent products in the United States (computers and peripherals) take up only 2.3% of the overall U.S. exports (in the October-December quarter of 2012)

This explanation corresponds more directly to my personal observation with contract electronics manufacturing and, earlier, US-based electronics manufacturing. And it seems to apply relatively well for Europe – where Chinese competition in broadening areas of production pressure many European companies – creating a sort of vacuum for future employment and economic growth.

The Kyoto analysis also gets the policy prescription about right –

..the deflation in Japan is much more pervasive than is indicated by the CPI and its cause is a steep drop in export prices of electrical machinery, the main export item for the country, which has been triggered by the increasing competition from emerging countries and which makes it impossible to offset the effects of a rise in import prices by raising export prices. The onslaught of competition from emerging countries is unlikely to wane in the future. Rather, we must accept it as inevitable that emerging countries will continue to rise one after another and attempt to overtake countries that have so far enjoyed economic prosperity.

If so, what is most important is that companies facing the competition from emerging countries recognize that point and try to create high-value products that will be favored by foreign customers. It is also urgently necessary to save energy and develop new energy sources.

Moreover, companies which have not been directly affected by the rise of emerging countries should also take action on the assumption that demand will remain stagnant and deflation will continue in Japan until the electrical machinery industry recovers [or, I would add, until alternative production centers emerges]. They should tackle fundamental challenges with a sense of crisis, including how to provide products and services that precisely meet users’ needs, expand sales channels from the global perspective and exert creativity. Policymakers must develop a price index that more accurately reflects the actual price trend and take appropriate measures in light of the abovementioned challenges.

Maybe in a way that’s what Big Data is all about.

explosive processes, inflation, macroeconomic forecasting

Hyperinflation and Asset Bubbles

According to Mizuno et al, the worst inflation in recent history occurred in Hungary after World War II. The exchange rate for the Hungarian Pengo to the US dollar rose from 100 in July 1945 to 6 x 1024 Pengos per dollar by July 1946.

Hyperinflations are triggered by inflationary expectations.  Past increases in prices influence expectations about future prices. These expectations trigger market behavior which accelerate price increases in the current period, in a positive feedback loop. Bounds on inflationary expectations are loosened by legitimacy issues relating to the state or social organization.

Hyperinflation can become important for applied forecasting in view of the possibility of smaller countries withdrawing from the Euro.

However, that is not the primary reason I want to address this topic at this point in time.

Rather, episodes of hyperinflation share broad and interesting similarities to the movement of prices in asset bubbles – like the dotcom bubble of the late 1990’s, the Hong Kong Hang Seng Stock Market Index from 1970 to 2000, and housing price bubbles in the US, Spain, Ireland, and elsewhere more recently.

Hyperinflations exhibit faster than exponential growth in prices to some point, at which time the regime shifts. This is analogous to the faster than exponential growth of asset prices in asset bubbles, and has a similar basis. Thus, in an asset bubble, the growth of prices becomes the focus of action. Noise or momentum traders become active, buying speculatively, often financing with Ponzi-like schemes. In a hyperinflation, inflation and its acceleration gets written into to the pricing equation. People stockpile and place advance orders  and, on the supply side, markup prices assuming rising factor costs.

The following is a logarithmic chart of inflation indexes in four South and Central American countries from 1970 to 1996 based on archives of the IMF World Economic Outlook (click to enlarge).

The straight line indicates an exponential growth of prices of 20 percent per year, underlining the faster than exponential growth in the other curves.

Hyper

After an initial period, each of these hyperinflation curves exhibit similar mathematical properties. Mizuno et al fit negative double exponentials of the following form to the price data.

negdexp

Sornette, Takayasu, and Zhouargue that the double exponential is “nothing but a discrete-time approximation of a general power law growth endowed with a finite-time singularity at some critical time tc.”

This enables the authors to develop an analysis which not only fits the ramping prices in each country, but also to predicts the end of the hyperinflation with varying success.

The rationale for this is simply that unleashing inflationary expectations, beyond a certain point, follows a common mathematical theme, and ends at a predictable point.

It is true that simple transformations render these hyperinflation curves very similar, as shown in the following chart.

scaledHyper

Here I scale the logs of the cumulative price growth for Bolivia, Nicaragua, and Peru, adjusting them to the same time period (22 years). Clearly, the hyperinflation becomes more predictable after several years, and the takeoff rate to collapse seems to be approximately the same.

The same type of simple transformations would appear to regularize the market bubbles in the Macrotrends chart, although I have not yet collected all the data.

In reading the literature on asset bubbles, there is a split between so-called rational bubbles, and asset bubbles triggered, in some measure, by “bounded rationality” or what economists are prone to call “irrationality.”

Examples of how “irrational” agents might proceed to fuel an asset bubble are given in a selective review of the asset bubble literature developed recently by Anna Scherbina from which I take several extracts below.

For example, there is “feedback trading” involving traders who react solely to past price movements (momentum traders?). Scherbina writes,

In response to positive news, an asset experiences a high initial return. This is noticed by a group of feedback traders who assume that the high return will continue and, therefore, buy the asset, pushing prices above fundamentals. The further price increase attracts additional feedback traders, who also buy the asset and push prices even higher, thereby attracting subsequent feedback traders, and so on. The price will keep rising as long as more capital is being invested. Once the rate of new capital inflow slows down, so does the rate of price growth; at this point, capital might start flowing out, causing the bubble to deflate.

Other mechanisms are biased self-attribution and the representativeness heuristic. In biased self-attribution,

..people to take into account signals that confirm their beliefs and dismiss as noise signals that contradict their beliefs…. Investors form their initial beliefs by receiving a noisy private signal about the value of a security.. for example, by researching the security. Subsequently, investors receive a noisy public signal…..[can be]  assumed to be almost pure noise and therefore should be ignored. However, since investors suffer from biased self-attribution, they grow overconfident in their belief after the public signal confirms their private information and further revise their valuation in the direction of their private signal. When the public signal contradicts the investors’ private information, it is appropriately ignored and the price remains unchanged. Therefore, public signals, in expectation, lead to price movements in the same direction as the initial price response to the private signal. These subsequent price moves are not justified by fundamentals and represent a bubble. The bubble starts to deflate after the accumulated public signals force investors to eventually grow less confident in their private signal.

Scherbina describes the representativeness heuristic as follows.

 The fourth model combines two behavioral phenomena, the representativeness heuristic and the conservatism bias. Both phenomena were previously documented in psychology and represent deviations from optimal Bayesian information processing. The representativeness heuristic leads investors to put too much weight on attention-grabbing (“strong”) news, which causes overreaction. In contrast, conservatism bias captures investors’ tendency to be too slow to revise their models, such that they underweight relevant but non-attention-grabbing (routine) evidence, which causes underreaction… In this setting, a positive bubble will arise purely by chance, for example, if a series of unexpected good outcomes have occurred, causing investors to over-extrapolate from the past trend. Investors make a mistake by ignoring the low unconditional probability that any company can grow or shrink for long periods of time. The mispricing will persist until an accumulation of signals forces investors to switch from the trending to the mean-reverting model of earnings.

Interesting, several of these “irrationalities” can generate negative, as well as positive bubbles.

Finally, Scherbina makes an important admission, namely that

 The behavioral view of bubbles finds support in experimental studies. These studies set up artificial markets with finitely-lived assets and observe that price bubbles arise frequently. The presence of bubbles is often attributed to the lack of common knowledge of rationality among traders. Traders expect bubbles to arise because they believe that other traders may be irrational. Consequently, optimistic media stories and analyst reports may help create bubbles not because investors believe these views but because the optimistic stories may indicate the existence of other investors who do, destroying the common knowledge of rationality.

I dwell on these characterizations because I think it is important to put to rest the nonsensical “perfect information, perfect foresight, infinite time horizon discounting” models which litter this literature. Behavioral economics is a fresh breeze, for sure, in this context. And behavioral economics  seems to me linked with the more muscular systems dynamics and complexity theory approaches to bubbles, epitomized by the work of Sornette and his coauthors.

Let me then leave you with the fundamental equation for the price dynamics of an asset bubble.

Sornettepricedynamics

inflation forecasts, macroeconomic forecasting

Inflation/Deflation 1

I initially envisaged a series of posts on forecasting inflation, treating inflation/deflation as phenomena more or less at arms length.

However, I began assembling posts on this topic while in the US, before traveling to Japan – where currently I am (now in Osaka).  In Japan, I’m pulled by the attractions of all the sights, the temples, the great food, and it’s cherry blossom season. Also, I just brought a tablet, leaving the laptop at home. I find that finishing a blog post nicely is hard with this Android device.

But now I have something to say – and I add some of the details of the initial post I planned on at the end of these comments.

Deflation in Japan

It’s cherry blossom season in Japan, and hotel reservations got scarce. So we had to book in clean, reasonably priced hotels that still had space – and it turns out these are sometimes on the margins of what you might call “the party district.”

I think watching the revels of young people here – uproarious and loud, all night and well into the early morning – provides a flesh-and-blood side to the general movement of prices – in this case the on-and-off deflation in Japan.

 

Check out Japan and the Exhaustion of Consumerism.

So even though I am on the 8th floor of this really nice business hotel in Osaka, the “party district” is full of young Japanese men and women (what I would call at my advanced tenure “youth”). Anyone who thinks the Japanese are reserved and repressed should put their ear to my window high above the street, listen to the revels – the howls and screams, often in unison by groups – as people get more and more booze under their belts through the evening and then through until morning. Go down at 6am and you will find couples and groups staggering around, quite drunk or loud, unruly groups of guys…..

The Original Material

As with gold and interest rates, there are several steps in coming to grips with inflation forecasts, not the least of which is the immediate prospect for price change in various global regions and inflation forecast performance over various short and longer term forecast horizons.

Generally, inflation forecasts are being adjusted downward for 2014 and 2015. The Survey of Professional Forecasters First Quarter 2014 Survey, for example, projects

…current-quarter headline CPI inflation to average 1.7 percent, lower than the last survey’s estimate of 1.8 percent. The forecasters predict current-quarter headline PCE inflation of 1.3 percent, lower than the prediction of 1.8 percent from the survey of three months ago.

The forecasters also see lower headline and core measures of CPI and PCE inflation during the next two years. Measured on a fourth-quarter over fourth-quarter basis, headline CPI inflation is expected to average 1.8 percent in 2014, down from 2.0 percent in the last survey, and 2.0 percent in 2015, down 0.2 percentage point from the previous estimate. Forecasters expect fourth-quarter over fourth-quarter headline PCE inflation to average 1.6 percent in 2014, down from 1.9 percent in the last survey, and 1.8 percent in 2015, down 0.1 percentage point from the previous estimate.

Over the next 10 years, 2014 to 2023, the forecasters expect headline CPI inflation to average 2.3 percent at an annual rate. The corresponding estimate for 10-year annual-average PCE inflation is 2.0 percent.

Lower and lower rates of inflation merge into deflation, of course, which is considered to be a risk currently for the eurozone. CNN Money reports that, in March, that the

..annual rate of inflation fell to 0.5%, down from 0.7% in February and weaker than most economists were expecting. Inflation is now at its lowest level since November 2009.

Actual deflation seems to be the reality in Spain, Portugal and Greece. Thus, the Wall Street Journal reports that ..

Spain’s preliminary estimate for March said the European Union-harmonized consumer-price index slipped 0.2% compared with the same month last year, down from February’s increase of 0.1%.

The data makes Spain the latest euro-zone country to slip into deflation. Prices have been dropping in Greece since early last year, and Portugal last month recorded its first year-over-year decline since 2009.

Japan is special among advanced industrial economies in having inflation which dipped into deflation several times for relatively extended periods, dating back to the 1980’s.

Japan is especially relevant, of course, because the Bank of Japan (BOJ) was the first to experiment with the zero-bound on short term interest rates and its aggressive bond-buying program – both features found in the US and European central bank environments today.

What is the Forecasting Problem?

Forecasting inflation rates have been one of the signature efforts of many macroeconomic forecasting organizations and agencies.

As a result, there is considerable research on the stochastic nature of inflation/deflation time series and the relative performance of univariate (autoregressive) versus multivariate forecasting models. It’s interesting to look over this literature with an eye to evaluating, in specific contexts, whether declining inflation rates can mean price change will dip below zero in key economic regions.

[I will add the finishing touches to this post, when I can. But meanwhile, this reflects my current thinking. Soon – hyperinflation]

African development issues, geopolitical risk, income and wealth factors, machine learning, macroeconomic forecasting

Links – end of March

US Economy and Social Issues

Reasons for Declining Labor Force Participation

LFchartVital Signs: Still No Momentum in Business Spending

investment

Urban Institute Study – How big is the underground sex economy in eight cities employs an advanced statistical design. It’s sort of a model study, really.

Americans Can’t Retire When Bill Gross Sees Repression

Feeble returns on the safest investments such as bank deposits and fixed-income securities represent a “financial repression” transferring money from savers to borrowers, says Bill Gross, manager of the world’s biggest bond fund.

Robert Reich – The New Billionaire Political Bosses

American democracy used to depend on political parties that more or less represented most of us. Political scientists of the 1950s and 1960s marveled at American “pluralism,” by which they meant the capacities of parties and other membership groups to reflect the preferences of the vast majority of citizens.

Then around a quarter century ago, as income and wealth began concentrating at the top, the Republican and Democratic Parties started to morph into mechanisms for extracting money, mostly from wealthy people.

Finally, after the Supreme Court’s “Citizen’s United” decision in 2010, billionaires began creating their own political mechanisms, separate from the political parties. They started providing big money directly to political candidates of their choice, and creating their own media campaigns to sway public opinion toward their own views.

Global Economy

Top global risks you can’t ignore – good, short read

How Can Africa’s Water and Sanitation Shortfall be Solved? – interesting comments by experts on the scene, including –

Most African water utilities began experiencing a nose-dive in the late 1970s under World Bank and IMF policies. Many countries were suffering from serious trade deficits which had enormous implications for their budgets, incomes, and their abilities to honour loan obligations to, among others, bilateral and multilateral partners. These difficulties for African countries coincided around that period, with a major shift in global economic thought; a shift from heterodox economic thinking which favoured state intervention in critical sectors of the economy, to neoliberal economic thought which is more hostile to state intervention and prefers the deregulation of markets and their unfettered operation. This thought became dominant in the IMF and World Bank and influenced structural adjustment austerity packages that the two institutions prescribed to the struggling African economies at the time. This point is fundamental and cannot be divorced from any comprehensive analysis of the access deficit in African countries.

The austerity measures enforced by the Bank and IMF ensured a drastic reduction of state funding to the utilities, resulting in deterioration of facilities, poor conditions for staff and a mass exodus of expert staff. In the face of the resulting difficulties, the Bank and IMF held out only one option for the governments; the option of full cost recovery and of privatisation. This sealed the expectations of any funding for the sector as the private sector found the water sector highly risky to invest in. Following the common interventions set out by the World Bank, the countries achieved mostly poor results.

Contrary to much mainstream discourse, neither privatisation nor commercialisation constitute an adequate or sustainable way of managing urban water utilities to ensure access to people in Africa given the extreme poverty that confronts a significant portion of the population. The solution lies in a progressive tax-supported water delivery system that ensures access for all, supported by a management structure and a balanced set of incentives that ensure performance.

Analytics

Machine Learning in 7 Pictures

Basic machine learning concepts of Bias vs Variance Tradeoff, Avoiding overfitting, Bayesian inference and Occam razor, Feature combination, Non-linear basis functions, and more – explained via pictures

The Universe

Great picture of the planet Mercury https://twitter.com/Iearnsomething/status/448165339290173440/photo/1

Mercury

accuracy of forecasts, financial forecasting, forecasting research, forecasting turning points, global business forecasts, macroeconomic forecasting, many predictors

Credit Spreads As Predictors of Real-Time Economic Activity

Several distinguished macroeconomic researchers, including Ben Bernanke, highlight the predictive power of the “paper-bill” spread.

The following graphs, from a 1993 article by Benjamin M. Friedman and Kenneth N. Kuttner, show the promise of credit spreads in forecasting recessions – indicated by the shaded blocks in the charts.

CPTBspread

Credit spreads, of course, are the differences in yields between various corporate debt instruments and government securities of comparable maturity.

The classic credit spread illustrated above is the difference between six-month commercial paper rates and 6 month Treasury bill rates.

Recent Research

More recent research underlines the importance of building up credit spreads from metrics relating to individual corporate bonds , rather than a mishmash of bonds with different duration, credit risk and other characteristics.

Credit Spreads as Predictors of Real-Time Economic Activity: A Bayesian Model-Averaging Approach is key research in this regard.

The authors first note that,

the “paper-bill” spread—the difference between yields on nonfinancial commercial paper and comparable-maturity Treasury bills—had substantial forecasting power for economic activity during the 1970s and the 1980s, but its predictive ability vanished in the subsequent decade

They then acknowledge that credit spreads based on indexes of speculative-grade or “junk” corporate bonds work fairly well for the 1990s, but their performance is uneven.

Accordingly, Faust, Gilchrist, Wright, and Zakrajsek (GYZ) write that

In part to address these problems, GYZ constructed 20 monthly credit spread indexes for different maturity and credit risk categories using secondary market prices of individual senior unsecured corporate bonds.. [measuring]..the underlying credit risk by the issuer’s expected default frequency (EDF™), a market-based default-risk indicator calculated by Moody’s/KMV that is more timely that the issuer’s credit rating]

Their findings indicate that these credit spread indexes have substantial predictive power, at both short- and longer-term horizons, for the growth of payroll employment and industrial production. Moreover, they significantly outperform the predictive ability of the standard default-risk indicators, a result that suggests that using “cleaner” measures of credit spreads may, indeed, lead to more accurate forecasts of economic activity.

Their research applies credit spreads constructed from the ground up, as it were, to out-of-sample forecasts of

…real economic activity, as measured by real GDP, real personal consumption expenditures (PCE), real business fixed investment, industrial production, private payroll employment, the civilian unemployment rate, real exports, and real imports over the period from 1986:Q1 to 2011:Q3. All of these series are in quarter-over-quarter growth rates (actually 400 times log first differences), except for the unemployment rate, which is simply in first differences

The results are forecasts which significantly beat univariate (autoregressive) model forecass, as shown in the following table.

Cspreadresults

Here BMA is an abbreviation for Bayesian Model Averaging, the author’s method of incorporating these calculated credit spreads in predictive relationships.

Additional research validates the usefulness of credit spreads so constructed for predicting macroeconomic dynamics in several European economies –

We find that credit spreads and excess bond premiums, when used alongside monetary policy tightness indicators and leading indicators of economic performance, are highly significant for predicting the growth in the index of industrial production, employment growth, the unemployment rate and real GDP growth at horizons ranging from one quarter to two years ahead. These results are confirmed for individual countries in the euroarea and for the United Kingdom, and are robust to different measures of the credit spread. It is the unpredictable part associated with the excess bond premium that has greater influence on real activity compared to the predictable part of the credit spread. The implications of our results are that careful selection of the bonds used to construct the credit spreads, excluding those with embedded options and or illiquid secondary markets, delivers a robust indicator of financial market tightness that is distinct from tightness due to monetary policy measures or leading indicators of economic activity.

The Situation Today

A Morgan Stanley Credit Report for fixed income, released March 21, 2014, notes that

Spreads in both IG and HY are at the lowest levels we have seen since 2007, roughly 110bp for IG and 415bp for HY. A question we are commonly asked is how much tighter can spreads go in this cycle

So this is definitely something to watch. 

financial forecasting, forecasting interest rates, macroeconomic forecasting, time series forecasting, yield curve

Interest Rates – 1

Let’s focus on forecasting interest rates.

The first question, of course, is “which interest rate”?

So, there is a range of interest rates from short term rates to rates on longer term loans and bonds. The St. Louis Fed data service FRED lists 719 series under “interest rates.”

Interest rates, however, tend to move together over time, as this chart on the bank prime rate of interest and the federal funds rate shows.

IratesFRED1

There’s a lot in this chart.

There is the surge in interest rates at the beginning of the 1980’s. The prime rate rocketed to more than 20 percent, or, in the words of the German Chancellor at the time higher “than any year since the time of Jesus Christ.” This ramp-up in interest rates followed actions of the US Federal Reserve Bank under Paul Volcker – extreme and successful tactics to break the back of inflation running at a faster and faster pace in the 1970’s.

Recessions are indicated on this graph with shaded areas.

Also, almost every recession in this more than fifty year period is preceded by a spike in the federal funds rate – the rate under the control of or targeted by the central bank.

Another feature of this chart is the federal funds rate is almost always less than the prime rate, often by several percentages.

This makes sense because the federal funds rate is a very short term interest rate – on overnight loans by depository institutions in surplus at the Federal Reserve to banks in deficit at the end of the business day – surplus and deficit with respect to the reserve requirement.

The interest rate the borrowing bank pays the lending bank is negotiated, and the weighted average across all such transactions is the federal funds effective rate. This “effective rate” is subject to targets set by the Federal Reserve Open Market Committee. Fed open market operations influence the supply of money to bring the federal funds effective rate in line with the federal funds target rate.

The prime rate, on the other hand, is the underlying index for most credit cards, home equity loans and lines of credit, auto loans, and personal loans. Many small business loans are also indexed to the prime rate. The term of these loans is typically longer than “overnight,” i.e. the prime rate applies to longer term loans.

The Yield Curve

The relationship between interest rates on shorter term and longer term loans and bonds is a kind of predictive relationship. It is summarized in the yield curve.

The US Treasury maintains a page Daily Treasury Yield Curve Rates which documents the yield on a security to its time to maturity .. based on the closing market bid yields on actively traded Treasury securities in the over-the-counter market.

The current yield curve is shown by the blue line in the chart below, and can be contrasted with a yield curve seven years previously, prior to the financial crisis of 2008-09 shown by the red line.

YieldCurve

Treasury notes on this curve report that –

These market yields are calculated from composites of quotations obtained by the Federal Reserve Bank of New York. The yield values are read from the yield curve at fixed maturities, currently 1, 3 and 6 months and 1, 2, 3, 5, 7, 10, 20, and 30 years. This method provides a yield for a 10 year maturity, for example, even if no outstanding security has exactly 10 years remaining to maturity.

Short term yields are typically less than longer term yields because there is an opportunity cost in tying up money for longer periods.

However, on occasion, there is an inversion of the yield curve, as shown for March 21, 2007 in the chart.

Inversion of the yield curve is often a sign of oncoming recession – although even the Fed authorities, who had some hand in causing the increase in the short term rates at the time, appeared clueless about what was coming in Spring 2007.

Current Prospects for Interest Rates

Globally, we have experienced an extraordinary period of low interest rates with short term rates hovering just at the zero bound. Clearly, this cannot go on forever, so the longer term outlook is for interest rates of all sorts to rise.

The Survey of Professional Forecasters develops consensus forecasts of key macroeconomic indicators, such as interest rates.

The latest survey, from the first quarter of 2014, includes the following consensus projections for the 3-month Treasury bill and the 10-year Treasury bond rates.

SPFforecast

Bankrate.com has short articles predicting mortgage rates, car loans, credit card rates, and bonds over the next year or two. Mortgage rates might rise to 5 percent by the end of 2014, but that is predicated on a strong recovery in the economy, according to this site.

As anyone participating in modern civilization knows, a great deal depends on the actions of the US Federal Reserve bank. Currently, the Fed influences both short and longer term interest rates. Short term rates are keyed closely to the federal funds rate. Longer term rates are influenced by Fed Quantitative Easing (QE) programs of bond-buying. The Fed’s bond buying is scheduled to be cut back step-by-step (“tapering”) about $10 billion per month.

Actions of the Bank of Japan and the European central bank in Frankfurt also bear on global prospects and impacts of higher interest rates.

Interest rates, however, are not wholly controlled by central banks. Capital markets have a dynamic all their own, which makes forecasting interest rates an increasingly relevant topic.

asset bubbles, forecasting asset bubbles, forecasting research, macroeconomic forecasting

The Worst Bear Market in History – Guest Post

This is a fascinating case study of financial aberration, authored by Bryan Taylor, Ph.D., Chief Economist, Global Financial Data.

**********************************************************

Which country has the dubious distinction of suffering the worst bear market in history?

To answer this question, we ignore countries where the government closed down the stock exchange, leaving investors with nothing, as occurred in Russia in 1917 or Eastern European countries after World War II. We focus on stock markets that continued to operate during their equity-destroying disaster.

There is a lot of competition in this category.  Almost every major country has had a bear market in which share prices have dropped over 80%, and some countries have had drops of over 90%. The Dow Jones Industrial Average dropped 89% between 1929 and 1932, the Greek Stock market fell 92.5% between 1999 and 2012, and adjusted for inflation, Germany’s stock market fell over 97% between 1918 and 1922.

The only consolation to investors is that the maximum loss on their investment is 100%, and one country almost achieved that dubious distinction. Cyprus holds the record for the worst bear market of all time in which investors have lost over 99% of their investment! Remember, this loss isn’t for one stock, but for all the shares listed on the stock exchange.

The Cyprus Stock Exchange All Share Index hit a high of 11443 on November 29, 1999, fell to 938 by October 25, 2004, a 91.8% drop.  The index then rallied back to 5518 by October 31, 2007 before dropping to 691 on March 6, 2009.  Another rally ensued to October 20, 2009 when the index hit 2100, but collapsed from there to 91 on October 24, 2013.  The chart below makes any roller-coaster ride look boring by comparison (click to enlarge).

GFD1

The fall from 11443 to 91 means that someone who invested at the top in 1999 would have lost 99.2% of their investment by 2013.  And remember, this is for ALL the shares listed on the Cyprus Stock Exchange.  By definition, some companies underperform the average and have done even worse, losing their shareholders everything.

For the people in Cyprus, this achievement only adds insult to injury.  One year ago, in March 2013, Cyprus became the fifth Euro country to have its financial system rescued by a bail-out.  At its height, the banking system’s assets were nine times the island’s GDP. As was the case in Iceland, that situation was unsustainable.

Since Germany and other paymasters for Ireland, Portugal, Spain and Greece were tired of pouring money down the bail-out drain, they demanded not only the usual austerity and reforms to put the country on the right track, but they also imposed demands on the depositors of the banks that had created the crisis, creating a “bail-in”.

As a result of the bail-in, debt holders and uninsured depositors had to absorb bank losses. Although some deposits were converted into equity, given the decline in the stock market, this provided little consolation. Banks were closed for two weeks and capital controls were imposed upon Cyprus.  Not only did depositors who had money in banks beyond the insured limit lose money, but depositors who had money in banks were restricted from withdrawing their funds. The impact on the economy has been devastating. GDP has declined by 12%, and unemployment has gone from 4% to 17%.

GFD2

On the positive side, when Cyprus finally does bounce back, large profits could be made by investors and speculators.  The Cyprus SE All-Share Index is up 50% so far in 2014, and could move up further. Of course, there is no guarantee that the October 2013 will be the final low in the island’s fourteen-year bear market.  To coin a phrase, Cyprus is a nice place to visit, but you wouldn’t want to invest there.