Development of Composite Leading Indicators (CLIs) in Indonesia

Transcription

1 Development of Composite Leading Indicators (CLIs) in Indonesia By Slamet Sutomo and Puguh B. Irawan BPS - Statistics Indonesia Importance of CLIs in the Context of Indonesian Economy In recent years, composite leading indicators (CLIs), as one of three sets of indicators required in economic and business forecasting, are becoming more widely recognized in predicting turning points of business cycles in Indonesia. The importance of CLIs as a policy making tool in Indonesian economy can be related to three reasons. First, CLIs can be used to indicate early detection and timely recognition of business cycle turning points that allow policy makers to elicit preventive countercyclical measures. Second, for the need of private sector, CLIs are useful for businesses to adjust their sales or investment strategy, and for investors to reallocate their resources among alternative investments in order to optimize their return. Information on CLIs is indispensable for planning in increasing production, investment, expanding and diversifying business activities. Third, as an open economy, Indonesian economy is very much affected not only by internal factors, but also external factors including the exchange rate of rupiah, the fluctuating prices of primary products and interest rates. Along with the dynamics of manufacturing sector that currently contributes around one-fifth of the total GDP, all of these factors play a combined role in shaping the economic trend and business cycle in the country. In addition, CLIs method has been proved as a reliable and inexpensive quantitative forecasting tool, as compared to the standard macroeconometric models. BPS-Statistic Indonesia has been carrying out business cycle analysis since the end of 970s. By 984, in collaboration with Institute of Developing Economies, Japan, BPS further improved the methodology applied in the analysis. Since 995, BPS has also been developing an early monitoring system for economic and business planning that comprises three complementary indices: CLIs, index of business tendency, and index of consumer tendency. This short paper summarizes the experience of BPS-Statistics Indonesia in the field of cyclical analysis with a particular reference to the development of CLIs in the country. It begins with a brief account of methodology applied in the analysis. Then the discussion continues to present data on the chronological trend of the Indonesian economy, and cyclical indicators. Methodology Predicting turning points of business cycles applying the leading indicator approach requires four major steps. The first step is to choose an appropriate indicator of economic activity, which is also called a reference series; and to identify dates of turning points (peaks and troughs) of the underlying business cycles in that series. The most commonly used measures of economic activity are the monthly index of industrial production or manufacturing production, and gross domestic product (GDP). Given the data availability, BPS-Statistics Indonesia uses GDP as a reference series. The second step is to select a set of appropriate economic and financial indicators as predictors of the turning points of business cycles. As indicators selected are expected to lead, coincide or lag the turning points of business cycles, they are then called as The other two sets of indicators are coincident and lagging indicators.

2 leading, coincident or lagging indicators respectively. The third step is to construct a composite leading index from the selected individual leading indicators, and similarly for composite coincident and lagging indices. The final step is to predict turning points in the reference series on the basis of outcomes of the composite leading index and an appropriate decision rule system. In principle, analysis of business cycle is to identify cyclical phases of business in one time period, which include phase of expansion, contraction, and turning points. Such identification is quite useful for estimating the turning points of the economic trend that is influenced by a certain business cycle. In analyzing business cycles in Indonesia, BPS-Statistics Indonesia adopts phase-average trend (PAT) method, as developed by the National Bureau of Economic Research (NBER) of the United States and applied in the business cycle analysis system of OECD member countries. PAT method can also be used to select appropriate leading indicators as predictors of turning points, on the basis of their strong correlations with the reference series. In general, PAT method of analyzing business cycle as employed by BPS-Statistics Indonesia systematically comprises procedures as follows: ) Identifying turning points at the peaks and troughs from the reference series that has previously been seasonally adjusted; 2) Measuring long trend of reference series and predicting indicators, and calculating averages of their changes; 3) The above procedure produces trend-adjusted data series; 4) Selecting appropriate indicators as predictors of turning points that can be categorized as leading, coincident or lagging indicators, by observing their coefficients of cross-correlation with the reference series. 5) The selected candidate indicators are then evaluated by looking at the averaged number of months that lead, coincide or lag the reference series and by calculating the correlation between the turning points of the candidate indicators and the turning points of reference series. The number of months that lead, coincide or lag the reference series indicate that those candidate indicators have consistent movement patterns as they lead, coincide, or lag the turning points of the reference series (GDP). By applying PAT method, the identification of leading, coincident and lagging indicators is based on the selection of candidate indicators that have high crosscorrelations with the cyclical phases of the reference series (GDP). Cross-correlations here consist of Lead (+) as to indicate whether the candidate indicator is leading (+), coincident (0) or lagging (-), and Coefficient of cross-correlation as to indicate the strength of relationship between the candidate indicator and the reference series. The higher the coefficient of cross-correlation, the stronger the correlation between the cycles of candidate indicator and the cycle of reference series. Data Used The selection of reference series data is the most crucial prerequisite in constructing leading indicators of business cycle. In principle, series data than can be used as reference series should have: a) a long time and uninterrupted series, and b) a strong correlation with economic trend (growth). There are at least two series data that can be used as reference series: gross domestic product (GDP) and industrial production index, published quarterly or monthly. Statistics Indonesia uses GDP as reference series since the available data series of GDP in the country meets both requirements. 2

3 Likewise, a number of candidate variables are also selected as leading, coincident and lagging indicators with main selection criteria as follows: a) Data series must have economic significance, as they are able to indicate the overall economic condition including periods of economic expansion and contraction; b) Data series must be long and uninterrupted, available up to date and timely; c) Data series are selected mainly for those monthly basis, rather than quarterly; However, given monthly based data are not always available, quarterly based data are used by interpolating them into monthly basis; d) There is no any major conceptual revision during the period being covered; e) With regard to the use of PAT method, those indicators selected as leading, coincident and lagging indicators should not only have long and uninterrupted series data, but also their cyclical patterns should be consistent over time; f) Consistency in cyclical patterns between candidate variables categorized as leading, coincident and lagging indicators with the reference data series is indicated by fairly high cross-correlations between the former and the latter; g) There is no any missing cycles in data series of candidate indicators, comparable to the reference series; h) The series must be smooth, allowing separation from irregular movements. There are sixteen groups of candidate indicators selected as leading, coincident and lagging indicators. These indicators are published on monthly basis (e.g. volume and value of total industrial production, volume of vehicle production, volume of vehicle sale, volume and value of export and import, number of international flights arrival and departing passengers, occupancy rate of hotels, money supply, monetary survey, interest rates, consumer price index), and on quarterly basis (including industrial production index, wholesale price index, volume of housing development by National Housing Company and balance of payment). Trend in the Reference Series: Turning Points in Business Cycles During the period of January 985 December 2003, there were three cycles in Indonesian economy that can be identified by PAT method. Using GDP as the reference series of measuring the economy, the first cycle occurred in the period of October 985 January 988, the second cycle in the period of January 988 October 994, and the third cycle in the period of October 994 October 998 (see Figure 2). Table. Indonesia: Dates of Turning Points in Business Cycles, Turning points Length Amplitude Phase/Cycle (Month/Year) (months) As % to Trough Peak Trough Phase Cycles trend Expansion Oct. 985 Oct Contraction Oct. 986 Jan Cycle Oct. 985 Jan Expansion Jan. 988 Jan Contraction Jan. 989 Oct Cycle 2 Jan. 988 Oct Expansion Oct. 994 Oct Contraction Oct. 997 Oct Cycle 3 Oct. 994 Oct Average: Expansion Contraction

4 Cycles 26.0 Table indicates that there were three troughs and three peaks in the business cycles in Indonesian economy during , which altogether formed three cycles. The length of the first cycle was 27 months, comprising 2 months of expansion phase and 5 months of contraction phase. Amplitudes for both expansion and contraction during the first cycle were relatively small, accounting for 4.4% and -8.6% respectively. The duration of the second cycle was quite long covering 8 months that consisted of 2 month of expansion and 69 months of contraction, with fairly small amplitudes at 9% during phase of expansion and 4.5% during phase of contraction. Finally, the timespan of the third cycle was 48 months, comprising 36 months of expansion and 2 months of contraction, with high amplitudes at 24.8% and -32.7% respectively. Figure. I ndonesia: T rend in GDP ( O riginal Series and Final T rend) Original Series Final Trend

5 30 25 Figure 2. Indonesia: I dentifying Cycles of Reference Series ( GDP) It is worth noting that the expansion and contraction of the Indonesian economy during Cycle (October 985-January 988) was characterized by deregulation in investment policy and devaluation of rupiah to US dollar by around 45% that brought about increase in non oil and gas export, but at the same time decline in the value of total export. Relatively short expansion and long contraction in the economy during Cycle 2 (January 988-October 994) were most likely related to policy on liberalization of banking system and deregulation on financial sector that instead led to drop in investment and plummeting stock market. Meanwhile, turning points in Cycle 3 recorded a relatively long economic expansion for 36 months (October 994-October 997), before a short economic contraction for one year (October 997-October 998) but with an unprecedented amplitude at -32.7% which was reflected by a minus 3.3% growth rate due to the severe impact of financial and economic crisis. This phase of economic contraction during Cycle 3 was initiated by a sharp depreciation of rupiah value from US$= 2,450 rupiah in June 997 to 3,035 rupiah in August 997, to,375 rupiah in January 998, and alarmingly to 4,900 rupiah in June 998. Composite Leading, Coincident and Lagging Indices of Business Cycles in Indonesia Leading, coincident and lagging indicators of business cycles in Indonesia were selected by scrutinizing and screening more than 39 indicator series available in regular publications by BPS-Statistics Indonesia, the Central Bank (Bank of Indonesia) and other reliable data sources. As noted earlier, the selection criteria for these indicators are among others: availability of monthly data (or monthly-interpolated quarterly series), economic rationale, having cyclical movements, leading (or coinciding or lagging) the turning points in the reference series, and having relatively high coefficients of cross-correlation at above On the basis of these criteria, 22 series were selected as candidates of leading indicators, 7 series as candidates of coincident indicators, and series as candidates of lagging indicators. Having grouped a number of indicator series into sets of leading, coincident and lagging indicators, composite indices of these three categories can be constructed. However, not 5

6 all indicators have significant correlations with the reference series. Thus, a further selection of appropriate indicator series to be included in composite indices was carried out by screening out for those with highest coefficients of cross-correlation between the combined re-selected indicators and the reference series. The selection criteria were: having coefficients of cross-correlation at above 0.60 and at least 3 months for cyclical dominance. Of 2 candidates of leading indicators, 8 indicator series were finally selected as components of the composite leading indicators with the coefficient of cross-correlation between these combined series and the reference series reaching Using the same procedure, 6 of 7 indicator series were selected as components of composite coincident indicators with the coefficient of 0.847, while 5 of series were lastly picked up as components of composite lagging indicators with the coefficient of Table 2 presents the list of composite leading, coincident and lagging indicators and their characteristics. Table 2. List of Leading, Coincident and Lagging Indicators and Their Characteristics Period MCD/ Standard Cross-correlation QCD Deviation Lead (+) Coefficient LEADING INDICATORS:. Export volume of machines and electrical tools 988M-2003M Sale volume of motor vehicles 5- tons 989M-2003M Import value of fertilizer and natural mineral 988M-2003M Import value of coal, coke and briquette 988M-2003M Import volume of meat and processed meat 988M-2003M Import volume of motor vehicles 988M-2003M Occupancy rate of 5 star hotels in Jakarta 992M-2003M Volume of international flights arriving passengers 994M-2003M Composite Leading Index 989M-2003M COINCIDENT INDICATORS:. Import of oil and gas 2. Total import 3. Import of motor vehicle 4. Average occupancy rate of hotels in Jakarta 5. Occupancy rate of 4 star hotels in Jakarta 6. Occupancy rate of one star hotels in Jakarta Composite Coincident Index 994M-2003M M-2003M M-2003M M-2003M M-2003M M-2003M M-2003M LAGGING INDICATORS:. Export value of shoes and other footwear 2. Export value of iron and steel 988M-2003M M-2003M

7 3. Import value of processes metal 4. Import volume of office machineries and data processors 5. Net domestic asset: claim of payment to government Composite Lagging Index 988M-2003M M-2003M M-2003M M-2003M Finally, the composite leading, coincident and lagging indices are constructed by aggregating eight, six and five individual leading, coincident and lagging indicator respectively with equal weights. The composite leading index, the composite coincident index and the composite lagging index together with the reference series are shown in Figure 3, 4 and 5. 7

8 Figure 3. Indonesia: Reference Series (GDP) and Composite Leading Indicator - trend restored series, Ref_Orig_smoothed CLI_TreRest Figure 4. Indonesia: Reference Series (GDP) and Composite Coincident Indicator 4 - trend restored series, Ref_Orig_smoothed CLI_TreRest Figure 5. Indonesia: Reference Series (GDP) and Composite Lagging Indicator 5 - trend restored series, Ref_Orig_smoothed CLI_TreRest 8

9 Closing Remarks Attempts have been made in this short paper to provide a brief account of the development of composite leading indicators in Indonesia. The application of leading indicator approach in predicting turning points of growth cycles in the Indonesian economy is increasingly becoming more recognized among researchers and policy makers. Both public and private sectors are eager to benefit from the results of the analysis, as CLIs can be used to indicate early detection and timely recognition of business cycle turning points that allow policy makers to elicit pre-emptive countercyclical measures. The results of data analysis suggest that during January 985-December 2003, there were six turning points in Indonesian business cycles, comprising three troughs and three peaks. Based on the observation of 39 readily available economic and financial indicators using PAT model, eight series were selected as leading indicators of the reference series of GDP. Applying the same procedure, six indicator series were selected as coincident indicators of GDP and five series were picked up as lagging indicators of GDP. The eight leading indicators are export value of machines and electrical tools, sale volume of motor vehicles 5- tons, import value of fertilizer and natural mineral, import value of coal, coke and briquette, import volume of meat and processed meat, import volume of motor vehicle, occupancy rate of five star hotels in Jakarta and volume of international flights arriving passengers. Apart from its usefulness, business cycle analysis applying leading indicator model in Indonesia is still in its early development. Lacks of the most commonly used data series, such as industrial production index, and other indicators that have sensitive economic significance, are the main pitfall in the development of CLIs in the country. There are much work to be done in improving the availability of reliable data series to support an attempt of further developing the optimal use of leading indicator approach in the future. Besides that, the present publication by BPS-Statistics Indonesia (2003) on business cycle analysis using leading indicator model has not included the calculation of predicting turning points as a mechanism of providing early warning of economic downturns or upturns. Predicting turning points is in fact the main purpose of constructing the leading index. By using the sequential probability model (SPM), for example, the model provides a decision rule system for interpreting movements in CLIs (Neftci, 982). 2 Apparently, inadequate data availability and the complication of model are main constraints for BPS-Statistics Indonesia to be able to predict turning points in business cycle analysis. Referencess BPS (Badan Pusat Statistik), Analisis Daur Bisnis, Indeks Tendensi Bisnis dan Tendensi Konsumen di Indonesia 2003 (Business Cycle Analysis, Business Tendency Index and Consumer Tendency Index in Indonesia 2003). Jakarta: BPS. Neftci, S. N., 982. Optimal Prediction in Cyclical Downturn. Journal of Economic Dynamic and Control 4: This model uses sequential analysis to calculate the probability of a cyclical turning point. It makes use of three types of information. The first is the likelihood that the latest observation in CLIs is from the recession sample or the recovery/expansion sample. The second is the likelihood of a recession (recovery) given the current length of the expansion (recession) relative to its historical average. Finally, these two components are combined with previous months probability estimates. A signal of a turning point will be issues when the probability reaches a certain threshold level. 9