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Inside the Crystal Ball. Harris Maury
Читать онлайн.Название Inside the Crystal Ball
Год выпуска 0
isbn 9781118865101
Автор произведения Harris Maury
Жанр Зарубежная образовательная литература
Издательство John Wiley & Sons Limited
Yet even on a pass/fail test, most forecasters have had trouble getting by. As earlier noted, only 5 of the 34 economists participating in 10 or more of the semiannual surveys of bond rates were directionally right more than half the time. And of those five forecasters, only two – Carol Leisenring of Core States Financial Group and I – made forecasts that, if followed, would have outperformed a simple buy-and-hold strategy employing intermediate-term bonds during the forecast periods. According to calculations discussed in the article, “buying and holding a basket of intermediate-term Treasury bonds would have produced an average annual return of 12.5 percent – or 3.7 percentage points more than betting on the consensus.”10
In their study of forecasters' performance in predicting interest rates and exchange rates six months ahead, Mitchell and Pearce found that barely more than half (52.4 percent) of Treasury bill rate forecasts got the direction right. (See Table 1.3.) Slightly less than half (46.4 percent) of the yen/dollar forecasts were directionally correct. And only around a third of the Treasury bond yield forecasts correctly predicted whether the 30-year Treasury bond yield would be higher or lower six months later.
Table 1.3 Percentages of 33 Economists' Six-Month-Ahead Directional Interest Rate and Exchange Rates Forecasts That Were Correct
Source: Karlyn Mitchell and Douglas K. Pearce, “Professional Forecasts of Interest Rates and Exchange Rates: Evidence from the Wall Street Journal's Panel of Economists,” North Carolina State University Working Paper 004, March 2005.
Although it is easy to poke fun at the forecasting prowess of economists as a group, it is more important to note that some forecasters do a much better job than others. Indeed, the best forecasters of Treasury bill and Treasury bond yields and the yen/dollar were right approximately two-thirds of the time.
Some economic statistics are simply easier to forecast than others. Since big picture macroeconomic variables encompassing the entire U.S. economy often play a key role in marketing, business, and financial forecasting, it is important to know which macro variables are more reliably forecasted. As a rule, interest rates are more difficult to forecast than nonfinancial variables such as growth, unemployment, and inflation.
If we'd like to see why this is so, let's look at economists' track records in forecasting key economic statistics. Consider, in Table 1.4, the relative difficulty of forecasting economic growth, inflation, unemployment and interest rates. In this particular illustration, year-ahead forecast errors for these variables are compared with forecast errors by hypothetical, alternative, “naive straw man” projections. The latter were represented by no-change forecasts for interest rates and the unemployment rate, and the lagged values of the CPI and gross national product (GNP) growth. Displayed in the table are median ratios of errors by surveyed forecasters relative to errors by the “naive straw man.” For example, median errors in forecasting interest rates were 20 percent higher than what would have been generated by simple no-change forecasts. Errors in forecasting unemployment and GNP were about the same for forecasters and their naive straw man opponent. In the case of CPI forecasts, however, the forecasters' errors were only around half as large as forecasts generated by assuming no change from previously reported growth.
Table 1.4 Relative Year Ahead Errors of Forecasters versus “Naive Straw Man”
Note: Short-term and long-term interest rates and unemployment rates are relative to a hypothetical no-change straw man forecast. CPI and GNP growth rates are relative to a same-change straw man forecast.
Source: Twelve individual forecasters' interest rate forecasts, 1982–1991; other variables, 29 individual forecasts, 1986–1991, as published in the Wall Street Journal.Stephen K. McNees, “How Large Are Economic Forecast Errors?” New England Economic Review, July/August 1992.
There are many more examples of forecaster track records, and we examine some of them in subsequent chapters. While critics use such studies to disparage economists' performances, it's much more constructive to use the information to improve your own forecasting prowess.
Why It's So Difficult to Be Prescient
Because so many intelligent, well-educated economists struggle to provide forecasts that are more often right than wrong, it should be clear that forecasting is difficult. The following are among the eight most important reasons:
1. It is hard to know where you are, so it is even more difficult to know where you are going.
The economy is subject to myriad influences. At each moment, a world of inputs exerts subtle shifts on its direction and strength. It can be difficult for economists to estimate where the national economy is headed in the present, much less the future. Like a ship on the sea in the pre-GPS era, determining one's precise location at any given instant is a difficult challenge.
John Maynard Keynes – the father of Keynesian economics – taught that recessions need not automatically self-correct. Instead, turning the economy around requires reactive government fiscal policies – spending increases, tax cuts and at least temporary budget deficits. His “new economics” followers in the 1950s and 1960s took that conclusion a step further, claiming that recessions could be headed off by proactive, anticipatory countercyclical monetary and fiscal policies. But that approach assumed economists could foresee trouble down the road.
Not everyone agreed with Keynes' theories. Perhaps the most visible and influential objections were aired by University of Chicago economics professor Milton Friedman. In his classic address at the 1967 American Economic Association meeting, he argued against anticipatory macroeconomic stabilization policies.11 Why? “We simply do not know enough to be able to recognize minor disturbances when they occur or to be able to predict what their effects will be with any precision or what monetary policy is required to offset their effects,” he said.
Everyday professional practitioners of economics in the real world know the validity of Friedman's observation all too well. In Figure 1.2, for example, consider real GDP growth forecasts for a statistical quarter that were made in the third month of that quarter – after the quarter was almost over. In the current decade, such projections were 0.8 percent off from what was reported. (Note: This is judged by the mean absolute error – the absolute magnitude of an error without regard to whether the forecast was too high or too low.) Moreover, these “last minute” projections were even farther off in earlier decades.
Figure 1.2 In the Final Month of a Quarter, Forecasters' Growth Forecasts for That Quarter Can Still Err Substantially
Source: Federal Reserve Bank of Philadelphia.
Moving forward, we discuss how the various economic “weather reports” can suggest winter and summer on the same day! Let's note, too, that some of the key indicators of tomorrow's business weather are subject to substantial revisions. At times it seems like there are no reliable witnesses, because they all change their testimony under oath. In later chapters we discuss how to address these challenges.
2. History does not always repeat or even rhyme.
Forecasters address the future largely by extrapolating from the past. Consequently, prognosticators can't help but be historians. And just as the signals on current events are frequently mixed and may be subject to revision, so, too, when discussing a business or an economy, are interpretations of prior events. In subsequent chapters, we discuss how to sift through history and judge what really happened – a key step in predicting, successfully, what will happen in the future.
The initially
Herman, “How to Profit from Economists' Forecasts.”