Swiss Composite Leading Indicators: An Appraisal

Transcription

1 Swiss Composite Leading Indicators: An Appraisal Attilio Zanetti and Simon Wey Swiss National Bank January 11, 2005 Abstract The most relevant piece of information requested to depict the cyclical dynamics of an economy is real GDP growth. However, GDP figures are quarterly and their publication occurs with a substantial delay. For real-time assessments of the situation, economists often refer to leading indicators. These are conceived to deliver a picture of current developments and of short-term economic perspectives. This paper investigates the features of the main Swiss composite leading indicators. We examine their relation to GDP growth and describe how to best interpret their behavior and what their limits are. attilio.zanetti@snb.ch, Swiss National Bank, Boersenstrasse 15, Postfach CH Zurich, Switzerland. We thank Eveline Ruoss and Jonas Stulz for their helpful comments. The view expressed here are solely those of the authors and do not necessarily represents those of the Swiss National Bank. Any errors are the responsibility of the authors. 1

2 1 Introduction The most relevant piece of information required to depict the cyclical dynamics of an economy at any given point in time is real GDP growth. In most countries, however, GDP figures are released on a quarterly basis only. In addition, their publication occurs with a substantial delay and time series are submitted to important revisions. These shortcomings are unfortunate as, for many purposes, an early assessment of the situation is necessary. Economists, business analysts and policy makers have therefore tried to compensate for these difficulties by considering the evolution of leading indicators. Leading indicators (LIs) are meant to provide as accurate a picture as possible of current developments and short-term perspectives of an economy. Their usefulness depends on their capacity to anticipate the cyclical movements of a reference series, usually GDP growth. The ideal leading indicator has a stable leading relationship to its reference series; displays exactly the same number of cycles (no missing or additional cycles); has a constant lead at turning points; provides hints about the magnitude of GDP movements and is not submitted to any significant revisions - the latter being crucial in determining the actual usefulness of the real-time information provided by LIs. In practice, what exactly can we expect from LIs? This contribution aims to formally investigate the features of the main Swiss LIs - the KOF barometer, two OECD leading indicators, the Purchasing Managers Index and the UBS leading indicator - in order to determine how to best interpret their behavior. The paper is structured as follows. The next section presents the Swiss leading indicators that we will analyze and introduces a definition of the business cycle. Section 3 evaluates the information content of LIs with respect to year-on-year GDP growth. Section 4 considers the link between LIs and quarter-on-quarter GDP growth. Section 5 summarizes our findings and concludes. 2 Swiss composite leading indicators All main leading indicators are composite in nature. Composite LIs are constructed by extracting the signals stemming from a selected group of economic time series, called component series; this information is then aggre- 2

3 gated in a single series. Component series are taken from different sources (monetary, financial and business survey series are among those most frequently used) on the basis of their leading features with respect to the reference series. The aggregation of component series in a single composite leading indicator is meant to reduce the impact of short-term noisy movements in the component series. Aggregation thus allows the accuracy of the signals provided by the composite leading indicator to be increased and the tracking qualities of the composite LI, as compared with those of the individual component series, to be improved. In this analysis, we will consider the performance of five composite leading indicators: the KOF barometer published by the Swiss Institute for Business Cycle Research, the OECD six-month rate of change and the OECD amplitude-adjusted leading indicator, the Purchasing Managers Index (PMI) published by Credit Suisse as well as the UBS leading indicator. The goal is to assess their predictive performance rather than focus on the details of their computation method. Table 1 summarizes some of the relevant features of the five composite leading indicators that we consider. The second column of the table shows which component series are used for the construction of the LI and what the sources are. Columns 3-5 provide information about the publication frequency, the release schedule and the reference series. For the computation of the barometer, the KOF relies mainly on time series resulting from its various monthly and quarterly surveys. In addition, the set of component series is extended to one of the sub-indexes of the quarterly consumer confidence survey conducted by the State Secretariat for Economic Affairs (seco). The series are smoothed and the barometer is derived by applying the principal component approach. The principal component is a weighted average (i.e. a linear combination) of the individual series in which the weights are chosen so that the composite variable reflects the maximum possible proportion of the variation in the initial set of series. There are two OECD leading indicators. Both LIs originate from the same pool of component series. The amplitude-adjusted OECD LI (OECD AA) is obtained in two steps. First, component series are normalized and aggregated on the basis of an equal-weighted average. Then, the amplitude of the detrended reference series is added, generating the OECD AA LI. After compilation of the amplitude-adjusted LI, the long-term trend of the reference series is reintroduced. From this trend-restored time series, six-month rates 3

4 of change are computed, producing the annualized six-month rate of change OECD LI (OECD 6MR). For the construction of its two LIs, the OECD relies on variables from the KOF s monthly manufacturing test. Contrary to the KOF barometer, however, OECD LIs also take into account some financial and monetary variables, as well as a labor demand indicator (OECD (1987a) [7]). Table 1: Features of leading indicators Indicator Component series Frequency Release Reference series KOF -Orders inflow, year-on-year change, KOF industry survey (M). -Backlog of orders, month-on-month change, KOF industry survey (M). -Expected primary product purchases, level, KOF industry survey (M). -Wholesale inventory evaluation, KOF wholesale survey (Q). -Order Backlog duration, year-on-year change, KOF construction survey. -Evaluation of the financial condition of households during the next 12 months, seco consumer survey. M End of month for month M. Year-on-year GDP change OECD OECD AA OECD 6MR -Unfilled Job Vacancies (seco). -Finished Goods Stocks, level, KOF industry survey (M). -Orders Inflow, month-on-month, KOF industry survey (M). -Production, month-on-month, KOF industry survey (M). -UBS 100 Share Price Index (2000=100) -Yield 10-Year Confederation Bonds. -Deflated Money Supply (M1). M First Friday of the first full week of the month for month M-2. Industrial production, excluding construction. PMI -Backlog of orders, SVME-CS -Output, SVME-CS -Employment, SVME-CS -Suppliers delivery time, SVME-CS -Stocks of purchases, SVME-CS M First working day of the month for month M-1. Year-on-Year GDP change UBS Two-step procedure. Estimation for quarter Q t 1 considers Incoming orders, Production and lagged Backlog of orders, UBS manufacturing survey. The estimation for Q t is based on the Backlog of orders only. Q At the beginning of the first month of the quarter for Q t and Q t 1. Year-on-Year GDP change The construction of the Purchasing Managers Index is based on information gathered in a survey conducted by the Swiss Association for Materials Management and Purchasing (SVME) covering some 200 manufacturing companies. The results are then evaluated and published by Credit Suisse (CS). The global PMI is a weighted average of five out of eight sub-indexes which result from the survey (CS (2005) [1], Pelaez (2003) [9]). 4

5 The UBS LI is based on information from the UBS quarterly survey in the manufacturing sector. It is computed by extracting a forecast from two OLS regressions in which the component series are regressed on GDP growth. Most of the component series enter as explanatory variables in the regression used to estimate GDP growth in the current quarter. GDP growth in the following quarter is estimated on the basis of a regression containing the backlog of orders only (Furustol and Ettlin (2000) [4]). Four of the LIs are released on a monthly basis whereas the UBS LI is a quarterly time series. For the purposes of this paper, we aggregated the monthly LIs to a quarterly frequency in order to allow a comparison of all LIs with GDP figures. Aggregation is based on quarterly averages of the monthly data. An aggregation based on the last observation of the quarter has also been tried. It leads to very similar results. The fourth column of the table provides information regarding the release schedule of the LIs, an important factor influencing the usefulness of LIs for their users. Two LIs might display a similar kind of correlation with the reference series, but depending on their publication schedule, one might provide information sooner than the other. As far as the monthly series are concerned, it is clear from Table 1 that the KOF barometer and the PMI are more rapidly available than the two OECD LIs. The last column indicates the reference series of the LIs. In the case of the KOF barometer and the UBS LI, the explicit reference series is yearon-year GDP growth. This is made very clear in the comments provided by these institutes together with their indicators, as well as in the graphical representation of the LIs. According to the PMI press releases, the corresponding LI is supposed to depict the development of manufacturing production with respect to the previous month. This formulation is derived from the fact that, in the survey, manufacturing firms are asked to evaluate the situation as compared with the previous month. However, in the same press releases, the PMI is actually plotted against year-on-year GDP growth (CS (2004) [3]). In a recent paper (CS (2005) [1]) which evaluates the performance of the PMI, year-on-year GDP growth is also used as the reference series. Traditionally, the OECD uses industrial production as the benchmark for the construction of LIs for member countries. The advantage of using industrial production series is that, in most countries, these data are avail- 5

6 able on a monthly basis, although this does not apply to Switzerland. In any case, the ultimate reference series remains GDP growth. In fact, fluctuations in industrial production explain a large proportion of fluctuations in the aggregate economy, indicating that the cyclical patterns of industrial production and GDP are closely related. It follows that OECD leading indicators constructed using industrial production as the reference series are thus well suited as indicators of GDP movements (OECD (1987b [8], 2004 [6])). In the context of our analysis, we will necessarily refer to the business cycle. Therefore, before starting with empirical observations, we will provide a definition which is conventional in business cycle analysis (OECD (1987b) [8]). We define a business cycle as a time span that lasts between 5 and 32 quarters and is composed of an expansion and a contraction phase. Phases are delimited by peaks and troughs. Each phase, the time distance between a trough (peak) and a peak (trough), must last at least two quarters. Note that the cycles which we will consider in the following are growth cycles. A contraction is thus a period of declining (although not necessarily negative) growth; an expansion is a period of accelerating growth. 3 Leading indicators and year-on-year GDP growth The quality of LIs is evaluated on the basis of the accuracy of their information content. The evaluation of LIs performances is based on a two-pillar standard strategy: a correlation analysis and a turning point analysis. In a first step, we consider the link between LIs and GDP cycles as measured by the year-on-year growth rates. In section 4, we will look at the significance of LIs for the quarter-on-quarter GDP dynamics. Figures 1.1 through 1.5 provide graphical support for our correlation and turning point analyses. They trace the pattern of year-on-year GDP growth and of each of the five LIs that we consider. Data are quarterly. In all figures, GDP growth is plotted with blue bars. Vertical blue lines signal the peaks and troughs in GDP growth cycles. Red lines represent the LIs, and the red dots indicate their turning points. In order to identify turning points in the GDP as well as in the LI time series, we apply the definition of the business cycle provided in the previous section. 6

7 Figure : Leading indicators and year-on-year GDP growth Figure 1.1: KOF barometer GDP KOF TP_KOF Figure 1.2: OECD amplitude-adjusted GDP OECD_AA TP_OECD_AA 7

8 Figure 1.3: OECD 6-month rate of change GDP OECD_6MR TP_OECD_6MR Figure 1.4: PMI GDP PMI TP_PMI Figure 1.5: UBS GDP UBS TP_UBS 8

9 The analysis covers the period between 1980 and the third quarter of During this time span, our reference series experienced seven full growth cycles. Of the five LIs, only the OECD and the UBS LIs cover the entire period. The KOF barometer begins in 1984 and the PMI in Correlation analysis We will first look at the results of a simple correlation analysis. Table 2 below shows the pair-wise correlations between the five leading indicators, on the one hand, and year-on-year GDP growth, on the other. Correlation analysis provides information about the strength of the relationship between the two time series and about the medium lead or lag. The higher the value of the correlation coefficient, the greater the similarity between the cyclical profile of the LI and that of GDP. Three distinct cases emerge. The UBS LI displays by far the strongest similarity to year-on-year GDP growth. Correlation is highest for simultaneous movements (0.96). The KOF barometer, the OECD AA LI and the PMI are somewhat less strongly correlated with GDP growth (between 0.70 and 0.80), but they anticipate GDP growth by one quarter. The OECD 6MR LI anticipates GDP growth movements by as much as three quarters. It fits the GDP series with less precision than any of the other LIs (0.53). All in all, there seems to be a trade-off between the precision of the LI concerning GDP growth movements, on the one hand, and the rapidity of its signals, on the other. Table 2: Correlations between year-on-year GDP growth and the leading indicators from t 3 t 2 t 1 t t + 1 t + 2 t + 3 KOF OECD AA OECD 6MR PMI UBS The high degree of correlation between the UBS LI and GDP growth is not surprising as the computation method maximizes the in-sample fit of the LI as compared with GDP growth. Although it is not presented as such, the UBS LI is actually providing a GDP growth forecast. 9

10 The other indicators display a positive lead as compared with the reference series, but they are much less reliable regarding the magnitude of GDP growth. This appears quite evident also from the observation of Figures 1.1 through 1.5. There is clearly a comovement between each of the LIs and GDP growth. LI changes in one direction are often followed by GDP growth changes in the same direction. When it comes to an interpretation of the LI level as compared with the level of GDP growth, however, relations become much looser. Of the Swiss LIs, the UBS LI is the one that comes closest to signaling the exact magnitude of GDP growth. In most cases, institutes explicitly remind users of the limits of their LI. The KOF points out that the barometer allows statements regarding growth acceleration or growth slowdown. Increasing (or decreasing) barometer values are synonymous with a signal of growing (or declining) values in GDP year-to-year growth rate ; however, no assumptions regarding the level of year-to-year growth rates of gross domestic product may be made based on the barometer level (KOF (2004) [5]). The OECD (1987 [7], 2004 [6]) also clearly affirms that its LIs are designed to provide qualitative information on short-term economic movements, especially at turning points, rather than quantitative estimates. Similarly, according to Pelaez (2003) [9], an increase in the PMI indicates that an expansion is more widespread, but does not imply a proportionate acceleration in GDP growth. 3.2 Turning points analysis The main goal of a LI is to provide information about the direction of growth in the short term. With this respect, a central issue is the early detection of turning points. LIs are potentially very useful, as the correct identification of turning points is one of the most difficult aspects in economic forecasting. The quality of any LI depends very much on its ability to deliver early and reliable information concerning turning points. Figures 1.1 through 1.5 allow the tracking of the performance of an LI at turning points. Table 3 shows the date of all GDP turning points and the lead of each LI for the specific turning point (a negative value implies a lead). Table 4 summarizes the relevant information. It indicates the number of peaks and troughs that have been observed in the year-on-year GDP growth series during the interval of existence of each LI. It provides the number of peaks and troughs identified too late by the LI (Missed Peaks and Missed Troughs). It then takes the turning points that were correctly detected by the LI and 10

11 computes the average lead, its variance and the median lead. The last two columns show whether the LI displays extra or missing cycles as compared with GDP. Table 3: Indicators leads and lags at turning points Peak Trough Peak KOF OECD AA nd 1 OECD 6MR PMI UBS Trough Peak Trough Peak Trough Peak Trough Peak KOF OECD AA OECD 6MR PMI UBS Trough Peak Trough Peak Trough nd: not displayed Looking at these two tables, it appears that the perfect LI does not exist. Neither of them have detected all the turning points correctly. The LI with the lowest number of missed peaks or troughs is the OECD 6MR. On the other hand, however, in the early 1980s it signaled a cycle that was not observed in the GDP. The KOF barometer displays exactly the same number of cycles as the reference series, although it signaled two out of seven peaks ( and ) and one out of seven troughs (1995.4) with a delay. The OECD AA LI missed four out of eight peaks and one of eight troughs. In addition, the variability of the lead, in particular at peaks, is substantial. The UBS LI also missed five turning points, but it is the LI with the most stable relation to GDP in terms of the variance of the lead. It appears once again that this indicator is meant to track the simultaneous GDP growth developments. With a lead of one quarter, the PMI LI also displays a relatively 11

12 stable link to the reference series. Table 4: Peaks and troughs analysis Peaks Missed Peaks Average Lead Variance Median Lead Troughs Missed Troughs Average Lead Variance Median Lead Additional cycles Missing cycles KOF OECD AA OECD MR PMI UBS All in all, the KOF barometer, the OECD 6MR LI and the PMI perform better than the other two with regard to early signaling of turning points. But none of them are systematically correct. In particular, the size of the lead can vary over time and false signals cannot be excluded. It thus appears that, even when looking at the simple direction of change of the LI, no mechanical interpretation is possible as to whether and when the GDP growth cycle will turn. 3.3 Revision issues We have so far evaluated the performance of our LIs using the current complete time series. The use of historical time series when analyzing the quality of LIs tends to overestimate their predictive capacity. This is due to the fact that it completely disregards the issue of revisions. Ultimately, what matters is not the historical fit of the LI series, but the information provided in real time. With this respect, revisions play a central role. As a matter of fact, all LI series are subject to revisions - although to different extents. There are several reasons for such revisions. Firstly, component series are not always available at the same time. This implies that LI are sometimes computed on the basis of an incomplete set of information. In the case of Switzerland, this argument applies to both OECD LIs as well as to the KOF barometer. On the contrary, all data used in the computation 12

13 of the PMI and of the UBS LI are collected simultaneously. Secondly, component series are sometimes smoothed before the aggregation step. This is done in order to eliminate irregular components and to increase the probability that each movement of the component series and of the LI as a whole provides the best cyclical information even in the short run. As new data are added and the time series is prolonged however, the smoothing procedure leads to changes in past values of the time series. This development is particularly important at turning points. There is a trade-off between smoothness and the importance of revisions. A smoothing procedure is implemented for the series entering the KOF barometer and the two OECD LIs. Finally, there is a third source of revisions as sometimes more fundamental modifications in the group of component series occur: a component series might need to be substituted because it has been discontinued or because, over time, it has lost its leading features. 3.4 Real-time information at turning points: an example Figures 2.1 through 2.4 show the real-time information provided by our LI around the turning point that led to the beginning of the recovery in the second half of As far as the KOF barometer, the OECD 6MR LI and PMI are concerned, we turn here to their monthly time series, whereas Figure 2.4 shows the quarterly series of the UBS LI. Data for the OECD AA LI could not be collected. The time series plotted in the figures are named after the month of the last observation they included. The March 2003 series in Figure 2.1 is thus the KOF barometer series that first contained an observation for that month. The last series being plotted is always the current series. Note that, in terms of year-on-year GDP growth, the trough was reached in the second quarter of

14 Figures : Leading indicators and the 2003 upswing Figure 2.1: KOF barometer March 03 April 03 May 03 June 03 July 03 August 03 November 04 Figure 2.2: OECD 6-month rate of change March 03 April 03 May 03 June 03 July 03 Oktober 04 14

15 Figure 2.3: PMI March 03 April 03 May 03 June 03 July 03 Figure 2.4: UBS Q Q Q Q

16 In the current KOF barometer series, the trough is observed in April When we look at the real time KOF barometer time series, we notice that a turning point was signaled several months later. The time series ending with the June observation was still clearly trending downwards. The July series started to signal a change, but it was not until the August series appeared that the turnaround became evident. The current OECD 6MR time series sets the trough in March. An indication of a possible turnaround was first provided by the March series (published in May). It then disappeared and the May series still exhibited a downward trend. With the July series (published in September), it then became apparent that a cyclical turning point had been reached. Nonetheless, between May and September the situation remained quite unclear. The PMI reached a trough in June. This first clearly emerged with the publication of the July series at the beginning of August. These data have since remained unrevised. In fact, the PMI is the only LI for which revisions are always limited to the last observation. This means that each observation is revised only once. For the UBS LI, the trough occurs in the second quarter of The figure shows that this indicator correctly identified the turning point, with the release of the third-quarter value in July. This was then confirmed by the series ending with the fourth quarter (released in October). Figure 2.4 also provides insights into the implications in terms of GDP growth of the potential revisions of this LI. As the figure shows, the indicator value for the second quarter was corrected from 0.5 to If we were to consider the UBS LI as a proxy for future year-on-year GDP growth, this would imply a forecast revision for the second quarter of 2003 of no less than 1.6 percentage points. GDP official data for the second quarter of 2003 were released on September 4 th and those for the third quarter on November 27 th. In spite of all the revisions mentioned, by the beginning of September and therefore well in advance of the GDP schedule, all indicators were signaling a rebound. But when was this rebound in year-on-year growth to be expected? If we refer to the median lead at troughs of each LI (Table 4), the OECD 6MR and the UBS LIs placed the turnaround in GDP growth in the third quarter, whereas the KOF barometer and the PMI located it rather in the fourth quarter. All in all, in the late summer of 2003, LIs were unanimously asserting 16

17 that we were likely to observe a pickup in GDP growth in the second half of the year. But it remained impossible to derive precise information about the timing and the strength of the upswing. This is quite a standard situation for LIs users. 4 Leading indicators and quarter-on-quarter GDP growth In the previous section, we dealt with year-on-year GDP growth. In fact, comments and analysis of GDP growth tend to focus far more on the quarteron-quarter dynamics than on the year-on-year rate of change. It would thus be advantageous if information about quarter-on-quarter GDP growth could be derived from LIs. In our next step, we thus investigate this relation. Table 5: Correlations between quarter-on-quarter annualized GDP growth and the leading indicators from t 3 t 2 t 1 t t + 1 t + 2 t + 3 KOF OECD AA OECD 6MR PMI UBS Table 5 shows the correlation between our five LIs and Swiss quarter-onquarter annualized GDP growth. Across the border, correlations turn out to be weaker than in the case of year-on-year GDP growth. Furthermore, in four out of five cases, the LI actually appears to be lagged as compared with GDP. This correlation analysis points out that the KOF barometer, the OECD AA LI, the PMI and the UBS LI have no predictive power as far as quarter-onquarter GDP growth is concerned. Only the OCDE 6MR LI seems to have some leading properties. Note, however, that even in this case, correlation is weaker than in Table 2. Also, an analysis of the type that we conducted for year-on-year growth shows that the OECD 6MR would have anticipated only ten out of sixteen turning points. Thus, even the OECD 6MR proves to be more appropriate as a LI for year-on-year rather than for quarter-on-quarter GDP growth. 17

18 5 Concluding remarks In this paper, we have analyzed the features of five of the main leading indicators of the Swiss economy. Results of this analysis allow us to draw several conclusions. Firstly, at least in the case of Switzerland, leading indicators are linked to year-on-year GDP growth rather than to quarter-on-quarter growth. It is thus with respect to year-on-year GDP growth that they can deliver useful information. Secondly, leading indicators provide qualitative information only. They indicate whether year-on-year GDP growth will get stronger or weaker and whether a turning point can be expected. But no quantitative estimates of growth can be derived. Even for short-run assessments, LIs must thus be considered as a complement to and not as a substitute for econometric models. Thirdly, none of the leading indicators we considered present the ideal features we described in the introduction. In particular, both the unstable size of the lead and data revisions are important sources of uncertainty. The fourth point we would like to make is largely related to the previous one. No leading indicator clearly outperforms the others. It seems, therefore, that the most sensible option would be to use them all simultaneously. When doing so, the user must be prepared to deal with possibly contradictory information. As in our example, signals provided by leading indicators eventually converge, but in the short run divergences are not uncommon. All in all, leading indicators are undoubtedly a useful analysis tool. They provide qualitative hints about the orientation of growth. Nonetheless, they cannot, by themselves, remove the uncertainty surrounding the short-term evolution of the economy, with the result that leading indicators must be integrated into a broader set of instruments. 18

19 References [1] Credit Suisse, 2005, Ten Years of the Purchasing Managers Index, January 3 rd, d/en/publikationen/pub_spotlight.jsp [2] Credit Suisse, 2004, SVME Purchasing Managers Index, Press release, December 1 st, p/d/en/schweiz/konjunktur/kon_pmi.jsp. [3] Credit Suisse, 2004, SVME Purchasing Managers Index, Press release, December 1 st, p/d/en/schweiz/konjunktur/kon_pmi.jsp. [4] Furustol, Oyvin and Franz Ettlin, 2000, UBS Konjunkturindikator: Zuverlässiger Gradmesser der schweizerischen Wirtschaftsentwicklung, UBS Volkswirtschaftliche Analyse Schweiz, August. [5] KOF, 2004, KOF economic barometer, [6] OECD, 2004, OECD Composite Leading Indicators: a tool for shortterm analysis, [7] OECD, 1987a, OECD Leading Indicators and Business Cycles in Member Countries, , Sources and Methods, No. 39, January. [8] OECD, 1987b, OECD leading indicators, OECD economic studies, [9] Pelaez, Ronaldo F., 2003, A Reassessment of the Purchasing Managers Index, Business Economics, Vol. 28, No. 4, October. 19