Even though the different business cycles can be described through relatively simple models such as the one explained in section (U.S. business cycles), the underlying reasons for the developments and the amplitude of the business cycles seems to be changing with each cycle. Wesley Clair Mitchell who was one of the early researchers of business cycles and leaders of NBER stated that; “since each business cycle in a sense is unique, a thoroughly adequate theory of business cycles, applicable to all cycles is unattainable” (Dua 2004, Page 1). This suggests the need to take a broad set of factors into consideration when analyzing the state of the economy and when trying to forecast the future developments of the macro economy.
The following approach to forecasting uses a broad range of indicators towards different sectors of the economy. This inclusion of many different indicators ensures the forecast from being biased from false signals, and also helps the forecaster in understanding the unique attributes of the future cycles. The forecasts which are based on a too narrow set of indicators, will always suffer from threats of being biased by the changing environments and potential false signals from individual indicators.
A flexible and dynamic approach
In contrast to the well defined econometric approaches to economic forecasting, the more general approach to economic indicators in this paper gives opportunities for a more dynamic analysis and handling of risks. This is needed because of the ever changing underlying dynamics of the business cycles. As the different business cycles are in many ways independent and unique, they also have different underlying reasons for their developments.
Because of this we cannot set any specific rules to which indicators to use, and we will need to attain a flexible and dynamic approach to their analysis. As already mentioned, my approach will include a pre specified group of different indicators, but as will be seen, they might all need supplemental analysis of different underlying factors to give a thorough understanding of their developments. But despite this, economic theory and empirical data can give us some vital pointers to what we should look for and what predictive information the different indicators might contain.
In the analysis of the indicators, both separately and in conjunction, I will use the three D’s presented by The Conference Board in their “Business Cycle Indicators Handbook” (2001) as my main working tools. This approach to economic indicators gives a simple and dynamic analysis which suites well to the ever changing environments of the US economy.
Even though the discussion used in this analysis of the economic indicator approach will be mainly towards forecasts of US business cycles, the same approach can be used on other economies as well. But the forecaster’s needs to be aware that some of the different indicators used might hold different information and relevance in different economies, and they will need to justify the use of the different indicators through the relevant attributes which will be presented in section 5.4.
The time horizon and economic forecasting
The time horizon of economic forecasts plays an important role to how much influence the forecast get. As uncertainty grows with the time horizon, the longer periods you try to predict, the more difficult it gets. Because of this forecasts of very long time horizons often gain less influence and attention.
While econometric approaches to forecasting have specifically stated time horizons on beforehand, the analysis in this paper will not have a specific time horizon on the forecast. Instead this approach looks for current trends in the economic indicators to give qualified expectations through economic theory on what trends we can expect in the future. In other words the forecast will not give any specific date where we expect changes in economic activity, but it will simply imply how the economic activity will develop during the next 3 to 18 months. While this might seem a somewhat diffuse choice of time horizon, it should be noted that no forecasting approach can with certainty tell what will happen at an exact date, but only give more or less qualified suggestions. This means that forecasting the exact date of future turning points in the business cycle is close to impossible. But as will be shown in the later analysis, forecasting future trends for the next 3-18 months are not impossible. These trends are also normally of more importance in developing long term corporate strategies than the exact turning point of the business cycle.
Economic indicators and their implications
Economic indicators are statistical measures of the economic conditions of a specific market or sector of the economy. They are produced to support economic analysis as snapshots of economic performance at a specific sector at a specific point in time (Baumohl, 2008). Good examples of popular indicators are employment reports and the consumer price index, which respectively gives helpful information on the employment situation and inflation. Through analyzing the history and economic theory behind such time series we can get an understanding of the current state of the US economy, and generate qualified expectations about the future.
Even though there are an almost indefinite number of economic indicators available for the US economy, it is not an easy job to interpret the available information. Some indicators are inaccurate and offer for revisions, while others are made available only with a significant lag so that the information within is of less importance in real time. On top of this there is the problem of contradicting information where the different indicators analyzed tell widely different stories about the state of the economy. An example of two indicators contradicting each other is consumer confidence and personal savings during both the 1990 and 2001 recessions. As consumer confidence was plummeting during both recessions one would expect an increase in personal savings as a percent of personal income as consumers were showing little faith in the health of their personal and the macro economy. But instead personal savings was in both instances low and stable, and even at record low levels during the 2001 recession. In other words, these two indicators are at the same time giving signs of both strong and weak levels of consumption.
The Conference Board (2001) states; . there is no single time series that fully qualifies as an ideal cyclical indicator”, when arguing the need to assess multiple indicators to get an unbiased understanding of the economy. I have already argued that all business cycles are in some way unique, which is the reason why we need to take a broad specter of indicators under evaluation when trying to find answers about the state of the economy in question. This leads to yet another problem; as there are so many different indicators, very few have the time and ability to absorb all information available. The obvious question is how to choose which indicators to concentrate the analysis on. Bernard Bauhmohl (2008) and The Conference Board (2001) both suggest some specific attributes which you should look for when choosing the indicators to form your analysis. I will in the following section concentrate on the more flexible attributes of Bernard Bauhmol, as I believe the attributes of the Conference Board are better suited for an econometric approach.
As I will be looking to forecast the future trends of total US economic activity I will choose a broad range of different indicators to give a simple all-round understanding of the state of the economy. The following attributes of Bernard Bauhmohl will be some of the main factors behind the choice between the many available indicators for the different sectors of the economy.
The quality of the information within is an obvious and important attribute to consider when choosing indicators. Many indicators are offers for high levels of revisions or seasonality which creates uncertainty and biases to the information within.GDP which is one of the most popular economic indicators, are also well known for being offer for endless revisions. Other indicators such as the consumer sentiment survey hold much information about the behavior of the consumers and are only seldom offer for revisions. As the indicators are the basis of the predictions, it is vital that the data received in real time are as accurate as possible.
Some indicators are only made available with a significant lag. To make real time analysis you would need up-to-date information, and you should pay attention to indicators whose information are made available relatively early after the end of the relevant period. GDP is again an example of a popular indicator which comes with a considerable lag, while employment reports on the other hand normally are made available only shortly after the closing of a month.
Although this paper is written ex post the start of the 2007 recession, I will try to choose indicators which can be strong also in real time forecasting. This means that both the timeliness and the accuracy will play a part in my choice of indicators, and the approximate release dates and amplitude of revisions will be stated in most of the descriptions of the respective indicators.
The Business Cycle Stage
Sometimes the amount of emphasis put on an indicator changes with the stage of the business cycle. For example, in periods of growth economist often put less consideration to the levels of auto sales. In these times of high growth and high employment, general consumption is normally high and analysts takes high sales numbers for granted. In recessionary periods on the other hand, such sales numbers might get more attention as it gives a good pointer on consumers’ economic confidence and might be a good indication to whether the business cycle is getting closer to reaching a trough.
The forecasting approach used in this paper will be general with the possibilities to be used in both forecasting peaks and troughs. But as stated in the problem formulation my main focus will be on the possibilities to forecast the 2007 recession. I will therefore give most attention to the indicators’ abilities towards forecasting recessions.
The predictive ability of the indicator is especially important when you are trying to forecast future developments. The problem with selecting predictive indicators is again that the economy changes over time. But despite this, there are some indicators that seem to be more consistent in their predictive abilities than others. Zarnowitz and Moore (1982) state that economic time series that represent the early stages of production and investment processes might help forecasting future levels of economic expenditures and output. For example popular indicators such as the number of new orders for durable goods or new housing starts might lead future economic output in the sense that it might take some time from the order of a good, or the building of a house before the actual sales and delivery takes place.
Also market expectations can play an important role in the predictive abilities of the different economic indicators. Share prices are per definition dependent on future dividend payouts, and when stock prices fall it might be a sign that investors expect or know that the future corporate profits and dividends will fall in the future, and hence that the business cycle might be closing on its peak.
There are numerous examples of indicators with such theoretical forecasting abilities, and in section 6 there will be an analysis of a number of different indicators where both their importance for the economy, their theoretical forecasting abilities, and their empirical forecasting performances will be mentioned.
Degree of interest and relevance
It is important to remember that different indicators can be of different relevancy in different economies. A good example is an indicator which will not be examined in detail in this paper, namely the price of oil. While the price of oil can have a negative relationship with economic activity in importing countries such as USA, this can be a very important positive indicator for exporters of oil such as Norway or Venezuela. In these oil producing countries, a higher price would mean increased corporate profits in their most influential industrial sectors. For importing countries on the other hand, increased prices would imply higher costs which would lay negative pressure on profits. I have decided not to include the price of oil in this analysis, although it certainly holds some relevance.
The level of interest in an indicator can also be of importance when choosing what information to include. A popular indicator is likely to carry much influence in the market, and should hence be considered in a forecasting approach. Because of this it can often be smart to choose the most popular indicator over the most sophisticated, when choosing between two indicators towards the same market.
Leading, coincident and lagging indicators, and their value to economic forecasting
Researchers of business cycles normally classify economic indicators into three different categories; leading, coincident and lagging. Leading indicators are those with the best predictive qualities and therefore start the negative or positive trends of the business cycle ahead of the actual business cycle. These are the indicators which are of most interest for forecasters, and which will get the most attention in this paper.
Coincident indicators are those who move relatively parallel with the business cycle, and experience their up- and downtrends at the same time as the general economic activity. Lagging indicators on the other hand, are the ones who enter stages of growth or decline only after the actual business cycle has already changed its direction.
Because we don’t have any forecasting methods that with certainty can give us the exact date when the business cycle is going to experience a peak or a trough ahead of time, the institutions who date the different stages of the business cycles make their announcements with a considerable lag. The Business Cycle Dating Committee (BCDC) of NBER states that they never announce any dates without being perfectly sure that the economy has hit a turning point. This results in a 6-18 month lag on US business cycle dating.
As the leading indicators are only suggesting that we might be heading towards a peak or trough in the future, without neither stating any specific date nor depth, coincident and lagging indicators can be of great importance when trying to estimate when the economy is actually turning. After receiving strong signs from the leading indicators, the forecaster will thus be waiting for the turning point to materialize in the coincident indicators. As the announcements from the BCDC also come with a considerable lag, understanding coincident and lagging indicators can give important information about whether the economy already has reached its top (bottom) and has in fact entered a recession (growth) stage. In other words the coincident and lagging indicators can be of great value also for forecasters, in terms of understanding more exactly when the economy reaches its expected turning points. The indicators chosen in the following analysis will therefore not be solely leading indicators, but also indicators which normally move more coincident, or with a lag, compared with the CI index.
Analyzing the indicators
Even though no indicator holds a perfect empirical merit, and even worse; they sometimes show opposite signs, it is nice to have some guidelines to what to look for when trying to analyze economic indicators and forecast economic turning points. The conference board has produced a handbook 1 to help analyzing the leading index ahead of recessions through three important elements; the three D’s2. Although the approaches suggested in the handbook are made towards the leading index, they are flexible enough to be used on bigger approaches with multiple indicators as well. As the conference board themselves suggests; . it is imprudent to forecast a recession using a simple and inflexible rule. The US economy is continually evolving, and is so far too complex to be summarized by one economic series” (Conference Board, 2001). In the following I will give a short introduction to the analysis of economic indicators starting with the business cycle and the importance of history, before explaining fundamentals and the three D’s. In the following section I will talk most about forecasting recessions, but the methods described can be used in much of the same way when trying to predict business cycle troughs.
With recurrent phenomenons such as the business cycle, history plays a very important role in forecasting. Historical trends contain hints on how the relevant indicators are likely to behave ahead of a peak or trough, that is, are they leading, coincident or lagging. An understanding of the time series’ former max and min values and its long term trend, together with its former behavior ahead of peaks and troughs is an important part of forecasting through economic indicators. From understanding history and analyzing what happened ahead of business cycle peaks and troughs in the past, we can look for similar developments ahead of the business cycles of the future. Because of this, a thorough understanding of the empirical behavior of the relevant economic indicators can be of vital importance, and I will include some empirical details on whether the different indicators in fact did show signs of strength or weakness ahead of earlier business cycle peaks3. But as the business cycles before the 2007 recession are generally outside the scope of this paper, the empirical analysis will not be detailed nor hold much descriptive information.
But with this said, it is again important to remember that the economy is evolving and that no business cycles are identical. This means that different times with different developments ahead of peaks and troughs, can give different trends in the indicators. In other words, while we basically learn how to forecast through understanding the past, it is also important to be aware of the possibilities that history will not repeat itself every time. The housing market did for example stay relatively stable, showing few signs of weakness during the 2001 recession, while it was experiencing a significant decline both ahead of and during the recession starting December 2007.
Where in the business cycle are we?
To be able to gain qualified expectations about the future developments of the economy it is vital to first understand the cycle theories explained in section U.S. business cycles and where in the business cycle the economy is today. From that information alone one can get indications of what to expect for the future. Knowing that historical business cycles have an average duration of 5-6 years and understanding what to expect from the different stages of the cycle, the CI alone can hold important information on both the current and the future. If the economy has experienced 5 years of positive growth, history tells us that it is very likely that growth will slow or even turn negative during the coming years. But we have learned from the years of the Great Moderation that the average duration from the past is not necessarily the correct duration for the future, as the last couple of growth stages have lasted for almost 10 years.
Through the analysis of a combination of leading and coincident indicators, the forecaster should be able to gain a good understanding of the current state of the economy, and of its strengths and weaknesses going forward.
Are the developments fundamentally supported?
As the business cycle grows towards a new peak, some indicators tend to reach extreme levels. Understanding the fundamentals behind these values is vital to gain the correct conclusions on whether the developments are signs of good health or those of a potential bubble. Indicators with positive long term trends often have rational explanations behind reaching these record levels, and might hence not be a sign of an overheating economy after all. But record levels should always be monitored against the fundamental reasoning behind the developments as this could indeed be the developments of a bubble. This means that we might have to include new information to explain the fundamentals behind developments in the indicators which was originally under examination. In other words we control the underlying developments behind the relevant indicator. For the price of a house, such underlying factors could be the costs of construction and the general supply and demand of houses4. A sudden increase in the costs of construction while all other factors stay the same, could for example be a rational explanation for an increase in the price of private houses.
A good example is the market for private housing during the years after World War 2 and after the Great Moderation. This market experienced a boom after World War 2 which generated record growth in house prices (Shiller 2005). At this point the housing market boom was well supported by fundamentals which resulted in a natural increase in demand, and the extreme developments were hence not danger signs, but rather positive developments supported by market fundamentals5.
During the Great Moderation the US housing market experienced another boom. Later in this paper I will show how the economic indicators reached record levels which when controlled against the fundamental data which were supposed to explain the developments, actually indicated that this was just the economy blowing a new bubble, and that the record prices were in fact too high.
The three D’s
Many economic indicators are very volatile and sometimes suffer from false signals of downturns when single indicators for some reason fall while the economy keeps on going strong. The possibility of different indicators pointing in different directions has also been discussed, and does indeed help to complicate the process of forecasting the future developments of the business cycle. To help structure the analysis The Conference Board suggests use of the three D’s; duration, depth and diffusion. They argue that even though we cannot base any conclusions on any single rule, the three D’s can be used as guidelines to summarize the information gathered from the many different indicators when trying to predict future recessions. The longer the period, the stronger the magnitude, and the broader the spread of the negative signs produced by the different indicators, should support any conclusions on whether a recession is in the loom, or not.
The high volatility within many indicators means that we are likely to see both good and bad numbers within the same month, but several months in a row with negative developments is often a sign that something is wrong. The Conference Board suggests that three consecutive months with negative growth in their leading index is a sign of future problems, but one would often like to see even longer periods of downward trends to draw any firm conclusions.
As the negative trend over time is relevant, so is the magnitude of the fall. If the fall is only minor then the economy might only be making some periodic corrections, and in these cases it is often easier to stimulate further growth through monetary policy. If on the other hand the depths of the downward trends in the indicators are more significant, it might be a sign that the threat of recession cannot be stopped. It is difficult to make any rules of thumb on what a significant depth is, as this can be different from indicator to indicator, but a thorough empirical analysis of the respective indicators can help the forecaster to understand which levels are regarded as normal. This type of analysis should also be considered in conjunction with the already mentioned fundamental analysis.
Alongside the timeline and depth of the trend, the diffusion among different indicators can tell something about how widespread the economic problems are. Remembering that the definition of a recession points to a broad downturn in total economic activity, it is obvious that the more widespread the trend is between different sectors of the economy, the harder it might be to fight off the recession through monetary policy. A diffused downturn in multiple indicators can also work as confirmation that the trends are not false signals, but indeed an indication of economic problems.
The three D’s can be used separately or simultaneously, although simultaneous signs from all of the three D’s should be noticed as stronger than indications within only one. Downturns in the economic indicators are signals that the economy might be weaker and hence the probability that the business cycle is closing on a peak increases. But with the help of the three D’s we can detect whether the signs are those which The Conference Board (2001) calls a tropical storm, or nothing more than a simple rain shower.
1 The Conference Board - Business Cycle Indicators Handbook (2001)
2 The Three D’s will be explained in detail in section 5.6.4
3 I will only focus on business cycle peaks, but similar analysis could be used to understand their behaviour ahead of troughs.
4 This is obviously not an exhaustive list of factors influencing the housing market.
5 Government restrictions had limited the supply of new homes during World War 2. When soldiers returned to settle with their families after the war, there was an increase in the demand for houses leading to high, but justified, growth in house prices. (Shiller 2005)