An Introduction to Business Cycle Indicators and Forecasting
This page introduces you to the basic facts of business cycle indicators and how they can be used for forecasting the economy and asset prices.

Characteristics of business cycles:

1. Fluctuations of aggregate economic activity.

2.Cycles  Expansion/Boom and Contraction/Recession

Peak and Trough: They are the turning Points of the business cycle.

3. Comovements of many macro variables over the business cycle.

4. Business Cycles are Recurrent but not periodic.

5. Persistence of economic activity.

Cyclical behavior of economic variables and BC indicators:

Direction: procyclical, countercyclical or acyclical variable.

Timing: leading, coincident or lagging variable.

Example: Industrial Production is Procyclical and Coincident

What are the business cycle properties of other macroeconomic variables ? The attached figures of various macro series can be interpreted as follows. Industrial production is pro-cyclical and coincident; both consumption and investement are pro-cyclical with investment more sensitive than consumption to the business cycle, as durable goods are a larger fraction of investment than of consumption; capacity utilization is procyclcial; employment is procyclical and coincident; the unemployment rate is countercyclical; the inflation rate is pro-cyclical and lags the business cycle (it tends to build up during an expansion and fall after the cyclical peak); the short-term nominal interest rate is procyclical and lagging; corporate profits are very pro-cyclical as they tend to increase during booms and strongly fall during recessions.


One of the leading uses of economists (one that tends to give us a bad name) is in forecasting the economy. But as John Kenneth Galbraith put it (Wall Street Journal, Jan 22, 1993, C1): "There are two kinds of forecasters: those who don't know, and those who don't know they don't know.'' Still, forecasting is the next subject, so let's see what we can make of it. The national income and product accounts give us, as we've seen, some idea of the current state of the economy as a whole. We'd also like to know, if (say) we're planning to expand capacity, what the economy is likely to be doing in the next few months or years. The answer, if we are honest about it, is we know a little about the future, but not much. In fact we typically don't even know where we are now. Thus on October 27, 1992, the BEA announced its preliminary estimates of third quarter GDP for 1992: in 1987 prices, real GDP was 4924.5. Compared with the previous quarter of 4891.0, the growth rate was 2.6 percent annually (you multiply by 4 to get an annual growth rate). One month later, the estimate was revised upwards to 4933.7, a growth rate of 3.5 percent. This may not sound like a big difference, but it might have had a significant impact on the election, and on Clinton's thinking about whether to focus policy on long term growth or the short term. The fact is, it's difficult to compute GDP until all the data is in, sometimes a year or more down the road. The best advice I can give you is to realize that there is an unavoidable amount of uncertainty in the economy. This is even more true of firms and their financial statements.

So what do we do? My choice is to get out of this game altogether, but not everyone has this option---a firm, for example, has to forge ahead the best it can. The first thing you should know is that there's a lot of uncertainty out there, and no amount of commercial forecasting is going to change that. If you're Al Checchi at Northwest Airlines, it doesn't help to say that your forecasters didn't predict the Gulf War, the 1991 recession, and the related decline in air traffic. Or GM: their forecasters reportedly came up with three scenarios for 1991, and what happened was worse than all of them.

But you still want to get the best forecasts possible. Business economists look at anything and everything to get an idea where the economy is headed. Among the best variables are those related to financial markets. One of these is the stock of "money,'' by which I mean the stock of cash and bank deposits held by firms and households. There are a number of different monetary aggregates, as we'll see later, but we'll focus for now on M2, which includes most of the deposits at commercial banks and other other financial institutions that accept deposits. As you see from Figure 1 the growth rate of the money stock moves up and down, roughly, with the growth rate of GDP. In this sense it is a good indicator of the state of the economy. And since money stock measures are generally made available more quickly than GDP, it tells us something about the current state of the economy as well.

Even better indicators are financial prices and yields, which have the additional advantage of being available immediately. As you might expect if you've ever taken a finance course, asset prices tend to incorporate "the market's'' best guess of future events and, by and large, they are as good predictors of the economy as we have. Maybe the best of these is the stock market. In Figure 2 I've plotted the annual growth rate of real GDP with the annual growth rate of the S&P 500 composite stock index. What you might expect is that the stock market anticipates movements in the economy: in recessions profits and earnings are down so stock prices should fall as soon as a recession is anticipated by the market. That's pretty much what you see. In the figure we see that every postwar downturn in the economy has been at least matched, if not anticipated, by the stock market. The problem is that there have been several downturns in the stock market that didn't turn into recessions---so-called false signals. A classic case is the October 1987 crash, which was followed by several years of continued growth. As we say in the trade, the stock market has predicted twelve of the last eight recessions.

Other useful financial variables are yield spreads, especially the long-short spread (the difference between yields on long- and short-term government bonds) and the junk bond spread (the difference between yields on high- and low-grade bonds). Both of these have been useful in predicting downturns in the economy. Recent work by Stock and Watson for the NBER suggests that stock prices and yield spreads contain almost all the usable statistical information about the future of the economy.

Financial variables and some others are combined in official indexes of leading indicators, which are constructed every month by the Conference Board and reported in the Wall Street Journal and other business publications. The current index of leading indicators (it changes from time to time) combines the following series:

Leading Indicators

1. Hours of production workers in manufacturing

5. New claims for unemployment insurance

8. Value of new orders for consumer goods

19. S&P 500 Composite Stock Index

20. New orders for plant and equipment

29. Building permits for private houses

32. Fraction of companies reporting slower deliveries

83. Index of consumer confidence

99. Change in commodity prices

106. Money growth rate (M2)

(The numbers are labels assigned in Business Conditions Digest, a Commerce Department publication on the state of the economy.) We see in Figure 3 that the index is closely related to the cycle, but only leads it by a month or two (which is hard to see in the quarterly data that I've graphed). Nevertheless, this is useful, since we don't know yet what GDP was last month. A related index of coincident indicators (Figure 4) does not lead the cycle, but has a stronger correlation with it. Its components are

Coincident Indicators

41. Nonagricultural employment

47. Index of industrial production

51. Personal income

57. Manufacturing and trade sales

Both of these indexes combine economic indicators to give us a clearer picture of current and, to a limited extent, future economic conditions.

If we use these data, how well do we do in forecasting the future? The short answer is that there's a lot of uncertainty in the economy, and no amount of economic or statistical sophistication is going to change that. Let me try to make this specific (at the risk of being a little technical). Using time series statistics (which I'll presume you remember from your Data Analysis course) you might estimate a linear regression of the form,

gtk = a + b xt ,

where gtk is the annualized growth rate between time t ("now'') and time t+k ("later''), with time measured in quarters. The variable x is whatever you use to predict g . If we do all this, we can use the estimated equation to forecast future GDP and get a quantitative measure of the amount of uncertainty in the economy as a whole. That is, we plug in the current value of the leading indicators for x and the latest estimate of GDP, and use the equation to tell us what GDP in k quarters is expected to be, relative to GDP now.

We find, of course, that our predictions are invariably wrong, sometimes by a little, sometimes a lot. A measure of how well we do is the standard deviation of the forecast error, the difference between what we predicted and what actually happened. To make this concrete, I used for x the spread between the ten-year treasury yield and the 6-month tbill yield. The parameters a and b of the regression line were then estimated by least squares. The estimates of b are invariably positive, indicating that upward sloping yield curves (see the next section) indicate high growth, downward sloping yield curves the reverse. The overall performance of this procedure is summarized by the statistics:

Forecast Horizon (k)   Std Deviation of Forecast Error     R2

1 quarter                 0.9%                             0.11

4 quarters                2.1%                             0.30

8 quarters                3.2%                             0.25
The technical aspects were discussed in your Data Analysis course, but to understand what the numbers mean let me run through the predictions for k=4. We find that for predictions of the growth rate of GDP between now and a year from now (k=4) that only 30 percent of the typical yearly variation (the variance) is predictable. The other 70 percent is unpredictable (at least by this method). We also see that the standard deviation of the forecast error is 2.1 percent: that is, we expect our prediction to be within 2.1 percent, in either direction, of what actually occurs about 70 percent of the time, which is a pretty wide band (Think of telling your boss: sales will either grow 3 percent this year or fall 1 percent.) This is a simple procedure, you might be able to do better. But it's unlikely that you'll consistently do a lot better. (If you do, you should go into business.) It gives us a concrete measure of how much uncertainty is out there in the economy as a whole, and indicates that there's a lot going on that's unpredictable. For individual firms it's worse, since lots of things affect individual firms that don't show up in the aggregate.

Perhaps the best lesson you can take from this is that the future is, to a large extent, unpredictable. It's misleading, and probably dangerous, to assume otherwise, no matter what you pay your economists. One of the things you probably want to do in business is learn to deal with uncertainty. You might do this by making contingency plans, so you'll be prepared when something unexpected occurs, by following flexible manufacturing methods so that you can adapt your product quickly if the market changes, by adopting a financial strategy that hedges you against (say) adverse movements in interest rates or currencies, and so on. That's not the topic of this course, but it may help to put some of what you learn in other courses in perspective.

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