The Unassuming Economist

From a new post by Francis Diebold:

“I just returned from a great BFI Conference at U Chicago, Developing and Using Business Expectations Data, organized by Nick Bloom and Steve Davis.

Wonderfully, density as opposed to point survey forecasts were featured throughout. There was the latest on central bank surveys (e.g., Binder et al.), but most informative (to me) was the emphasis on surveys that I’m less familiar with, typically soliciting density expectations from hundreds or thousands of C-suite types at major firms. Examples include Germany’s important IFO survey (e.g.,Bachman et al.), the U.S. Census Management and Organizational Practices Survey (e.g., Bloom et al.)., and fascinating work in progress at FRB Atlanta.

The Census survey is especially interesting due to its innovative structuring of histogram bins. There are no fixed bins. Instead users give 5 bins of their own choice, and five corresponding probabilities (which add to 1). This solves the problem in fixed-bin surveys of  (lazy? behaviorally-biased?) respondents routinely and repeatedly assigning 0 probability to subsequently-realized events.”

From the Economic Outlook for the Euro Area in 2018 and 2019:

“Signs of a slowing world economy are piling up: Since the beginning of the year purchasing manager indices have been declining globally, in summer higher US interest rates led investors to withdraw capital from emerging markets, and as a consequence, capital costs rose and currencies depreciated in many emerging markets economies. In October stock prices decreased markedly worldwide including the US, despite the strong upswing in this country.

As a consequence of the turmoil on financial markets, monetary conditions in many emerging economies are no longer favorable. What ultimately counts for the prospects of the global economy, is, however, the performance of the US, the Euro area, China and Japan. The upswing in the US appears stable enough to continue well into 2019. While at present the rest of the group appears to lose momentum, there is a good chance that production in each of these economies will still expand at rates that are close to their potential growth. Further protectionist rounds are the most important risk to this scenario.”

“In the first half of the current year the euro area economy expanded at a markedly slower rate than in 2017, about 0.4% per quarter, but still substantial. Rising risk premia on Italian assets will probably force banks in this large country to tighten credit conditions, and a slowing world trade will dent export growth. All in all, we expect GDP growth in the euro area to go down from 2.4% in 2017 to 2.0% in 2018 and to 1.7% in 2019.”

“Employment continues to expand and vacancy rates are at present higher than in 2017. As a consequence, wages rise more quickly: nominal compensations per employee started accelerating early in 2017, and negotiated wages followed at the beginning of 2018. Wage inflation of slightly below 2.5% and healthy growth in employment raise real labor incomes markedly.

Our forecasts are based on the assumption that rating agencies will continue as-signing investment grade to the Italian government debt, and that the Italian government and the European Commission will find a compromise about the draft budget of the country in the coming weeks. Another assumption is that the UK will not exit the EU in an unorderly way in March 2019”

A new paper assesses “the accuracy and efficiency of crude oil price forecasts published by different organisations, think tanks and companies. Since the sequence of published forecasts appears as smooth, the weak efficiency criterion is clearly violated. Even combining forecasts, cannot increase efficiency due to high correlation among various forecasts. This pattern of oil price forecasts can be attributed to combining myopia (use current oil price levels as a basis) with Hotelling-type exponential growth. Another behavioural explanation in source of inefficiencies is that forecasters prefer to harmonise their forecasts with other forecasters in order to be not an outlier.”

From a new post by David Andolfatto:

“Does the recent flattening of the yield curve portend recession? Not necessarily. The flattening of the real yield curve may simply reflect the fact that real consumption growth is not expected to accelerate or decelerate from the present growth rate of about 1% per annum. On the other hand, a 1% growth rate is substantially lower than the historical average of 2% in the United States. Because of this, the risk that a negative shock (of comparable magnitude to past shocks) sends the economy into technical recession is increased. While the exact date at which the shock arrives is itself is unpredictable, the likelihood of recession is higher relative to a high real interest rate, high growth economy.”

Herman Stekler, a giant in the field of forecasting, has passed away. He was known for insisting that “the cost of a recession is so great that a forecaster should never miss one. Some people argue that turning points are unpredictable. I disagree. I have never had trouble predicting recessions. In fact, I have predicted n+x of the last n recessions.”

Herman shuttled between academia and policy institutions throughout his career. His interest was in business and applied economics; in fact, during his graduate school years at MIT he complained to Paul Samuelson about his “frustration and concern that economics was too abstract. There were not enough applications to the real world.” This interest in practical applications led him to spend a summer during his graduate school years at the Rand Corporation helping to devise an early warning radar system to alert the US and Canada in the event of an attack from the USSR. This experience nurtured some of Herman’s interest in leading indicators for economic activity, and it also led to a lasting interest in the defense industry.

For his starting job after graduate school, however, he chose academia over business because he “did not like some of the culture of the business community: During my visit to IBM, I was struck by the fact that every professional had to have his shoes polished before lunch.” He started instead at the University of California, Berkeley, in the business school, in 1959.

In July 1966, he moved to the Fed, starting work in the National Income Section of the Research and Statistics Division. Economic forecasts had just started to be included in the Fed’s so-called ‘Greenbook’ in the fourth quarter of 1965. Herman’s task at the Fed was to help prepare the Greenbook forecasts and sometimes to write an explanation for the rationale behind the forecasts. While the experience was exhilarating, Herman eventually left the Fed as the process of preparing the forecasts – at that time FOMC meetings were held every three weeks – left little time for research (including research on the assessment of the Fed’s forecasts, which Herman spent a lot of time on in the years that followed).

In 1968, Herman joined the faculty at the State University of New York–Stony Brook as a full professor. Reflecting his interest in the defense industry, he later moved to the Industrial College of the Armed Forces (ICAF), where he taught until the early 1990s. As he was planning to retire in 1994, George Washington University tempted him with a research professor position for a year. For every year since then, Herman incorrectly forecast the date of his retirement, which he always said would be “next year.” Nearly, twenty five years later, Herman was still at George Washington, writing papers and advising students to the very end of his days.

Herman published widely in nearly every branch of forecasting, including sports forecasting. A colleague of his once said that Herman “is willing to forecast anything that moves and everything that stands still.”

Herman, my dear friend, it is sad to see you stand still.

**

The Stekler Award for Courage in Forecasting

Herman’s views and his relentless push to get forecasters to predict “early and clearly” led me to institute a Stekler Award for Courage in Forecasting. The recipients of the award thus far are given below:

Groundhog Day Tradition: 2017 Stekler Award for Courage in Forecasting

A Groundhog Day Tradition: The Stekler Award for Courage in Forecasting

The Stekler Award for Courage in Forecasting (Recessions Inaccurately)

Special section in International Journal of Forecasting honoring Herman

In 2012, Herman’s colleagues and friends organized a workshop (Honoring a Forecasting Giant) on the occasion of his 80^th birthday, the proceedings of which were published in a special section (On Groundhog Day, Honoring A Forecasting Giant) in the International Journal of Forecasting. The preface to that section describes Herman’s professional work, from which I quote below.

In a famous paper in the American Economic Review (Stekler, 1972), Herman tried to understand why forecasters fail to predict recessions. The evidence that forecasters prefer to miss turning points that occur rather than to predict recessions that do not occur led him to suggest asymmetric loss functions as one possible explanation. (For the record, in October 2007 Herman correctly predicted the onset of the Great Recession and also predicted that the ensuing recovery would be quite weak).

Herman had an abiding interest in improving the ability of economic forecasters to predict turning points. This was the theme of his Ph.D. thesis at MIT. Building on work by his advisor Sidney Alexander, Herman tried to develop a procedure that could be used to forecast turning points in real time. His idea was to predict a turning point if a composite indicator of economic activity was below (above) its previous peak (trough) for a certain number of months. Obviously, a longer lead was more desirable but it also increased the odds of calling a false turning point; Herman’s thesis analyzed the tradeoff between the length of the forecasting lead and the frequency of false signals (Alexander & Stekler, 1959).

In subsequent work, Herman found that forecasters’ record of predicting turning points left a lot to be desired. This was true regardless of the method of forecasting—whether through leading indicators, econometric models or judgmental methods. In the 1960s, he did a consulting project where his ‘”task was to evaluate every econometric model then in existence and determine whether any of them provided accurate forecasts [of turning points]. They did not” (this quote and others in this overview are from an interview with Herman—see Joutz, 2010).