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The earlier version of business-cycle models started from the story of Prophet Yusuf in Chapter Yusuf, verses 42-48. The Pharaoh of Egypt summoned Prophet Yusuf, then an imprisoned slave, to interpret two dreams. In the first, seven plump cattle were followed and devoured by seven lean, starving cattle. In the second, seven thin ears ate full ears of corn. After hearing these dreams, Prophet Yusuf prophesied that Egypt would enjoy seven years of prosperity, followed by seven years of famine. He recommended a consumption-smoothing strategy to provide for the years of famine, under which Pharaoh would appropriate and score a fifth of the grain produced during the years of plenty. According to the tafseer ibnu Khathir, Pharaoh embraced this plan, made Prophet Yusuf his finance minister, and thereby enabled Prophet Yusuf to save Egypt from starvation.
The above verses have led towards a better understanding of macroeconomic fields. And it motivate us to draw the main objective of macroeconomic analysis, i.e., to provide a coherent explanation of aggregate movements of output, employment and price level, both in the short- and long-run. Then, the neoclassical macroeconomic theory dictates that aggregate demand and monetary shocks were the main sources of aggregate disequilibrium. The neoclassical school of thoughts assumes "full employment" represented equilibrium and recessions are considered disequilibrium periods of market failure. Thus, state intervention in the form of a stabilization policy is justified to return the economy to equilibrium.
In their seminal paper, Kydland and Prescott (1982) offered an additional explanation of business cycles - they postulated that each stage of a business cycle (peak, recession, trough and recovery) is equilibrium in itself. Thus, the RBC school of thoughts rejects the market failure view of an economy in disequilibrium. Recessions represent the aggregate outcome of responses by economic agents to unavoidable shifts in the constraints that they face. As agents responded rationally to changes in their constraints, market outcomes which are displaying aggregate fluctuations are actually efficient. Thus, the research by Kydland and Prescott (1982) posed a serious challenge to all previous mainstream accounts of business cycles that focused on aggregate demand or monetary shocks.
Hence, this paper is aimed to: identify business cycles for the Malaysian economy using the Malaysian System of Economic Indicators (SEI); and assess the effectiveness of the SEI in estimating the turning points of a cycle, especially a recession.
The remaining discussion of this paper is as follows. Section 2 will briefly review the RBC theory. Section 3 will discuss the methodology adopted by this paper followed by a discussion on the results obtained in Section 4. Finally, Section 5 will discuss some strengths and weaknesses of the methodology adopted as well as some suggestions for improvements before Section 6 concludes.
Real Business Cycle Theories
The classical and most widely accepted definition of a business cycle was constructed by Burns and Mitchell (1946) : "Business cycles are a type of fluctuation found in the aggregate economic activity of nations that organize their work mainly in business enterprises: a cycle consists of expansions occurring at about the same time in many economic activities, followed by similarly general recessions, contractions and revivals which merge into the expansion phase of the next cycle; this sequence of changes is recurrent but not periodic; in duration, business cycles vary from more than one year up to ten to twenty years ...".
The main assumption behind the RBC theory is that large random fluctuations in the rate of technological progress cause supply-side shocks to production functions. In turn, rational economic agents respond to changes in prices by changing their labour supply decisions (based on the trade-off between work and leisure) and changing their consumption decisions (based on the trade-off between current and future consumption). The resulting changes in optimization behaviour by the agents then lead to fluctuations in aggregate output and employment.
The importance of the RBC theory was enhanced by the supply-side shocks due to the OPEC oil price hikes during the 1970s, where the demand-oriented Keynesian model failed to offer an explanation of the simultaneous rising unemployment and hyper-inflation phenomena. In addition, research work by Kydland and Plosser (1983) argued convincingly that real shocks may be more important than monetary shocks in explaining output growth.
On the other hand, in carrying out research work on RBC, the main empirical problem is to separate the trend and cycle components. The conventional approach (as adopted by neoclassical trend-reverting growth models) has been to assume that the long-run trend of GNP is smooth with short-run fluctuations about trend being primarily determined by demand shocks. Thus, output deviations from the trend are temporary and recessions create no long-run effects on the GNP. In other words, the conventional approach postulates that reversible cyclical fluctuations can account for most of the short-term fluctuations, as shown:
Yt = gt + bYt-1 + zt ; 0 < b < 1 (1)
where Yt is GNP, gt is underlying average GNP growth rate, and zt = Random shocks with E(zt) = 0
In equation (1), gt describes the deterministic trend in output. Suppose there is a shock to zt that pushes up output and lasts for only 1 period. Since Yt depends on Yt-1, the shock will be transmitted forward in time, generating serial correlation. However, since 0 < b < 1, the impact of shock on Yt will eventually die out and Yt will return to its growth trend. Thus, output is trend-reverting or trend stationary as shown in Chart 1 (1A).
Chart 1: The Path of Output According to the Conventional and RBC Approaches
In contrast, the RBC approach as presented in Nelson and Plosser (1982), argued that if real factors are the source of aggregate fluctuations, then business cycles should not be viewed as temporary events and that recessions may have permanent effects on the GNP. So, if changes in GNP are permanent, there is no tendency for output to revert to its former trend following a shock, that is output follows a "random walk". Equation (2) shows a random walk with a drift for GNP:
Yt = gt + Yt-1 + zt ; b = 1 (unit root) (2)
where gt is drift of output. Thus, equation (2) shows that any shock to zt will change output permanently as the change in output persists in every future period, as shown in Chart 1(1B).
The RBC theory has two major implications. First, if the shocks to productivity growth due to technological changes are frequent and random, then the path of output following a random walk will exhibit features that resemble a business cycle but the observed fluctuations in output are fluctuations in the natural/trend rate of output, not as deviations of output from a smooth deterministic trend. Secondly, the economic forces determining the trend are not different from those causing the fluctuations but they must be real shocks.
However, the stylised facts of a business cycle, as summarised by Abel and Bernanke (2001), can be classified according to the direction and timing of variables in relation to the movement of GDP. In terms of direction of movement, variables that move in the same direction (thus display positive correlation) as GDP are pro-cyclical; variables that move in the opposite direction (negative correlation) to GDP are counter-cyclical; and variables that display no clear pattern (zero correlation) are acyclical. In terms of timing, variables that move ahead of GDP are leading variables; variables that follow GDP are lagging variables; and variables that move at the same time as GDP are coincident variables.
Table 1 indicates that the main stylised facts as set out by Abel and Bernanke (2001), show that among others, output movements tend to be correlated across all sectors of the economy, and that industrial production, consumption and business investment are pro-cyclical and coincident. Government purchases also tend to be pro-cyclical. Investment is much more volatile than consumption, although spending consumer durables is strongly pro-cyclical. Employment is pro-cyclical while unemployment is counter-cyclical. Both the real wage and productivity are pro-
cyclical. The money supply and stock prices are pro-cyclical and lead the cycle.
Table 1 : The Stylised Facts of the Business Cycle
Based on the stylised facts above, it is thus possible to trace business cycles using economic indicators that exhibit the direction and timing as specified in Abel and Bernanke (2001). However, the calibration method as opposed to econometric testing is used in this case of indicator approach to analysing business cycles.
Some Basic Definitions and Concepts
In a business cycle model, a turning point is defined as the point where the direction of a cycle changes. A peak (P) point is the highest point from a long-term trend in a cycle while a trough (T) point is the lowest. The confirmation of a turning point is based on the size, duration and scope of movement of the particular point. In differentiating growth and business cycles, it is assumed that growth cycles move more smoothly along the long-term trend, as shown in Chart 2. Business cycles are shorter in duration and fluctuate along the growth cycles. Admittedly, it is very difficult to distinguish between the growth and business cycles. Thus, more focus is given on business cycles as they are more readily observable.
Chart 2: Growth versus Business Cycles (A Hypothetical Scenario)
The data used in this paper is sourced from the Malaysian System of Economic Indicators (SEI), published by the Malaysian Department of Statistics. The economic indicators are on monthly basis while the data on real GDP is on quarterly basis. The period coverage is from January 1970 to July 2008 for the SEI and the real GDP growth rate data is from first quarter of 1979 to second quarter of 2008.
Construction of SEI
The SEI is made up of three composite indices - the Leading, Coincident and Lagging Indices. A total of 67 indicators were initially considered by the Department of Statistics to construct these indices. The indicators have been subjected to, and have survived, several statistical and economic tests, as follows:
Conformity - the series must conform well to the business cycle;
Consistent Timing - the series must exhibit a consistent timing pattern over time as a leading, coincident or lagging indicator;
Economic Significance - cyclical timing must be economically logical;
Statistical Adequacy - data must be collected and processed in a statistically reliable way;
Smoothness - month-to-month movements must not be too erratic; and
Currency - the series must be published on a reasonably prompt schedule
Out of the 67 indicators, 19 indicators (each given zero weightage) were finally used in constructing the indices in the SEI as follows:
Leading Index (8 indicators) :
Real money supply (M1);
Bursa Malaysia Industrial Index;
Real trade of 8 major trading partners;
CPI for Services;
Industrial material price Index;
Ratio of price to unit labour cost (manufacturing);
Approved housing permits; and
New companies registered.
Coincident Index (6 indicators) :
Industrial production index;
Real gross imports;
Real salaries (manufacturing);
Total employment (manufacturing);
Real sales (manufacturing); and
Real contributions to the Employees' Provident Fund (EPF).
Lagging Index (5 indicators)
7 day call money;
Real excess lending;
Approved investment projects;
EPF defaulters; and
New vehicles registered
The composite indices are calculated using the Moore-Shiskin method which consists of averaging the month-to-month growth rates of the index components, after standardizing them to the same units, and then cumulating this average growth rate into an index. The index is then adjusted to have the same average absolute percentage changes as the cyclical component of industrial production and the same average trend rate of growth as real GDP.
Identification of the Malaysian Business Cycles
Based on the constructed composite indices, the Malaysian economy has gone through eight confirmed growth cycles since 1970 as shown in the Table 2 under the Reference Cycle (Coincident Index) column.  This implied that there are eight episodes of growth recession, where the growth of real GDP was below the long-term trend of 6.3 per cent per annum. However, the Malaysian economy has experienced three business recessions (where the GDP registered negative growth rates) - the first recession was from July 1974 to February 1975; the second from January 1985 to January 1986; and the third from December 1997 to November 1998.
From Table 2, it can be seen that that the Leading Index is able to give early warning of a turning point 5 months on average. However, out of the 17 turning points, only 13 points were able to give leading information, thus the Leading Index is able to give leading information 76 per cent of the time. On the other hand, the Lagging Index is able to confirm the turning points after a 6-month lag. Out of the 11 turning points, 8 of them are lagging in nature, giving the Lagging Index a 70 per cent capability to confirm a turning point.
Chronology of the Peak and Trough Points of the Leading, Coincident and Lagging Indices
The lead and lag records for both the Leading and Lagging Indices are compared to Coincident Index reference cycles.
The number in months : A negative value means that the turning point of the index is leading that of the Coincident Index, while a positive value shows that it is lagging in nature. A zero value means that it happens at the same time with the turning point of the Coincident Index.
In an ideal situation, the turning points of the Leading Index must lead those of the Coincident Index, that is its turning points should show negative values in terms of number of months, while the turning points of the Lagging Index should have positive values, that is lagging in nature.
Chart 3 shows the movement of the Coincident Index as deviations from trend and the real quarterly GDP growth rate on annual basis. The down trend of a cycle is represented in yellow for ease of tracking. It can be seen that the timing and duration of a business cycle varies over the years. In the past decade, the business cycles happened more frequent (on average every two years) and their duration is shorter between 1-2 years long. The Coincident Index also tracks the real GDP growth quite well too. Thus, the cycles of the Coincident Index seemed to be a good representation of the actual business cycles in the Malaysian economy. Henceforth, the dates of the turning points of the Coincident Index (indicated at the top of the chart) are used as reference.
Chart 4 tracks the movement of the Leading Index. Ideally, the turning points of the Leading Index should occur prior to the relevant reference dates of the reference cycles. For instance, the peak point of the 1985 cycle occurred in January 1985 (based on the Coincident Index) but the peak point of the Leading Index occurred in January 1984, giving a 12-month lead. Thus, the Leading Index can be a useful tool for policy makers in timing their policy actions.
Chart 5 shows the movement of the Lagging Index, where its turning points ideally should occur after the relevant reference dates of the Coincident Index. This seems to suggest that the lagging index has little practical value on the surface and may be dismissed as inconsequential. To do so, however, ignores vital information about the business cycle process, because the indicators can help to indicate any structural imbalances that may be developing within the economy. These indicators represent costs of doing business as well as consumer and social costs. Thus, an accelerated rise in the lagging index, which often occurs late in an expansion, provides a warning that an imbalance in rising costs may be developing. Moreover, the lagging indicators help confirm recent movements in the leading and coincident indices, and thus enable the differentiation of turning points in these series from idiosyncratic movements.
The Effectiveness of SEI in Estimating Turning Points of a Cycle
The second part will assess the effectiveness of the SEI in estimating the turning points of a cycle. In determining a turning point, several conditions have to be met depending on the type of turning point as follows:
The case of tracking a recession period :
Condition 1 :
The Leading Index declines towards 4-5 per cent growth rate  ;
Condition 2 :
The Leading Index in the following months falls towards 0 per cent growth; and
The Coincident Index declines towards 4-5 per cent growth.
The Leading Index falls below 0 per cent growth;
The Coincident Index moves towards 0 per cent growth; and
The Lagging Index declines towards 4-5 per cent growth.
The case of tracking a recovery period :
Condition 1 :
The Leading Index rises to 0 per cent growth from a negative rate;
The Leading Index in the following months moves towards 4-5 per cent growth; and
The Coincident Index rises to 0 per cent from a negative rate.
The Leading Index rises more than 5 per cent growth;
The Coincident Index moves towards 4-5 per cent growth; and
The Lagging Index rises towards 0 per cent growth from a negative rate.
As a test, the SEI was used to track the recession and recovery period of the 1998 crisis  in the Malaysian economy. Chart 6 shows the recession and recovery periods as indicated by the conditions set above.
In tracking the recession period, the first condition could be seen fulfilled in July 1997, when the growth rate of the Lagging Index fell to below the 5 per cent rate. The second condition was met in January 1998, where the growth of the Leading Index dipped to -1.6 per cent and that of the Coincident Index fell to 2.4 per cent. In March 1998, the growth rate of all three of the indices recorded a negative rate, which signalled the fulfilment of the third condition of recession. Based on the movements of the GDP, its growth rate fell from 7.2 per cent in the third quarter of 1997 to -1.6 per cent in the first quarter of 1998.
In tracking the recovery period, it is found that the first condition of recovery was met in October 1998, where the growth of the Leading Index moved towards 0 per cent from a negative range. The second condition was met in February 1999, where the growth of the Leading Index strengthened to 5.9 per cent and the Coincident Index recorded a positive growth of 1.0 per cent. The third condition of recovery was fulfilled in September 1999, where all three indices registered positive growth rates. The recovery phases were also reflected in the GDP growth rates. The GDP, which recorded -11.2 per cent in the fourth quarter of 1998, rebounded in the first quarter of 1999 by recording a smaller decline in growth at 1.0 per cent before registering a positive growth of 4.8 per cent in the following quarter.
In terms of turning points, it can be confirmed that the trough point of the Leading Index happened in September 1998 (see Table 2). If the Leading Index is able to provide lead information by 5 months, than this implies that economic recovery would begin in the first quarter of 1999. Based on the Coincident Index, which reflects the current economic situation, its trough point happened in November 1998 (see Table 2). This further implied that economic recovery would begin from December 1998 onwards. As pointed earlier, the data also show that the third condition of recovery was fulfilled in September 1999, where the growth rate of both the Leading and Coincident Indices was more than 5 per cent. In the same month, for the first time since December 1998, the Lagging Index recorded a positive growth at 3.2 per cent and it went on to strengthen to 8.9 per cent in December 1999 and 7.5 per cent in March 2000, thus confirming the recovery period.
The SEI was also tested to see whether the Malaysian economy is heading for a recession in the coming months based on the global financial crisis that began in the United States in early 2008. Chart 7 tracked the SEI and the GDP from January 2006 to July 2008. It can be seen that the first condition of recession was met in March 2008, where the growth rate of the Leading Index dipped below the 5 per cent rate. The second condition was fulfilled 4 months later in July 2008 where the growth rate of the Leading Index moved towards 0 per cent. The Coincident Index, which has been growing below the 5 per cent rate since December 2006, recorded a negative growth of 0.2 per cent in July 2008. Although the third condition has yet to be seen, these developments seemed to point to a slowdown in growth in the coming months. In fact, official estimates have been released confirming the expected slowdown in growth of the Malaysian economy in 2009.
Strengths and Weaknesses
As shown previously, the SEI is a useful tool to monitor and track the cycles of the Malaysian economy, where analysis of its movements has proven its credibility in tracking economic movements. The Leading Index is able to predict a turning point 76 per cent of the time with a 5 month's lead time, while the Lagging Index is able to confirm a turning point 70 per cent of the time with a 6 month's lag. However, its uses must be accompanied with a cautionary note.
It must be noted that the Leading Index cannot predict the size of an impending change; it can only indicate the direction of change in the economic cycle. For example, when the Leading Index recorded a trough point, this suggested that in about 5 month's time, economic expansion would happen but the extent of this expansion cannot be determined. Secondly, the Coincident Index cannot measure the current performance of the economy; it can only give an indication of the state of the current economic performance. The peak for this index reflected that the economy has reached the highest point in the expansionary phase, while the trough point reflected the end of the recessionary phase. It cannot measure the size of the expansion or the decline at any one time. Thirdly, there is a 24 per cent chance that Leading Index cannot provide information on the emergence of a turning point; that is there is a one in five chance that the Leading Index will give a false signal. Finally, the SEI cannot capture the external influences on the economy, such as the impact of disasters, diseases, wars, terrorist attacks and so on. Thus, human judgment is still needed in interpreting the signals from the economic indicators. These points suggested that the SEI should not be used on its own as a policy-making tool but as a complementary tool to other indicators/tools.
Suggestions for Improvement
As noted above, the Leading Index is able to predict a turning point 76 per cent of the time but there is a 24 per cent chance it can give a false signal. To further improve the capability of the SEI, the SEI needs to be reviewed to see whether the existing indicators are still suitable and whether there are other indicators that may be better suited for incorporation in the indices. Since it's inception in 1995, the SEI has never been comprehensively reviewed and it is high time one is carried out. As a comparison and a guide, listed below are the list of 21 indicators that are currently being used in the Leading, Coincident and Lagging Indices for the United States as published by The Conference Board  :
Leading Index (10 indicators):
Average weekly hours (manufacturing);
Average weekly initial claims for unemployment insurance;
Manufacturers' new orders (consumer goods and materials);
Vendor performance (slower deliveries diffusion index);
Manufacturers' new orders (non-defence capital goods);
Building permits (new private housing units);
Stock prices (500 common stocks);
Money supply (M2);
Interest rate spread (10-year Treasury bonds less Federal funds (%));and
Index of consumer expectations.
Coincident Index (4 indicators):
Employees on non-agricultural payrolls;
Personal income less transfer payments;
Index of industrial production; and
Manufacturing and trade sales.
Lagging Index (7 indicators):
Average duration of unemployment;
Inventories to sales ratio (manufacturing and trade);
Change in labour cost per unit of output (manufacturing (%));
Average prime rate charged by banks (%);
Commercial and industrial loans outstanding;
Consumer instalment credit outstanding to personal income ratio; and
Change in consumer price index for services (%).
Admittedly, the biggest problem in Malaysia is data availability, especially personal income data and data on capital/investment/inventories. However, with recent development in data collection progress within the Department of Statistics, especially with recent signings of MOUs between the Department of Statistics and other data collection agencies such as the Internal Revenue Board, more indicators may be made available which may be of used to strengthen the SEI capability.
Another suggestion to improve the capability of the SEI is for the Department of Statistic to construct a diffusion index. Diffusion indices can provide another source of useful information about how widespread a particular business cycle movement (expansion or contraction) has become, and measure the breadth of that movement. Diffusion indices measure the number of components that are increasing in any given month. For example, if the leading index has ten components/indicators, a diffusion index value of 70 would indicate that seven of the ten components were rising. A diffusion index of zero would indicate that all ten fell. Thus, the diffusion index will complement the effort in identifying a turning point in addition to the three conditions set previously.
The RBC theory is an alternative view to neoclassical macroeconomic models in explaining the movements of aggregate economic variables. The RBC postulated that random technological changes caused shocks to the real economy and each stage of a business cycle is equilibrium, even a recession, where rational economic agents optimize their consumption/work choices. In the Malaysian case, based on the SEI, there are currently eight confirmed business cycles and the SEI can be used to predict turning points of the cycles. Although useful as a policy-making tool, the SEI does have some weaknesses, thus it must not be used on its own but as a complementary policy tool to other indicators.