CONSTRUCTING AN ECONOMIC COMPOSITE INDICATOR FOR THE UAE 1 ASSIL EL MAHMAH Research and Statistics Department June 2017 1 The views expressed in this paper are those of the author and should not be interpreted as those of the Central Bank of the United Arab Emirates.
Constructing an Economic Composite Indicator for the UAE Abstract In order to assess the economic activity for the UAE, policymakers examine different economic variables that could provide them high frequency information about the economic developments. However, since the UAE GDP, the main measure of the economic activity, is available only on an annual basis with a considerable publication delay, policymakers have to make decisions with a large amount of information obtained from different sources. To overcome this problem, this paper proposes to construct an Economic Composite Indicator (ECI) that can closely track the economic activity of the UAE on a quarterly basis. Given the absence of a reference variable, the Principal Component Analysis (PCA) is the most appropriate method to calculate this indicator. This approach aims to extract a common factor from a group of relevant economic series and to capture the highest level of common trend. The results show that, the ECI reflects the economy s historical performance since 2006 and confirms the economic development recorded by the UAE Purchasing Managers' Index (PMI). On an annual basis, there is a high correlation between the constructed ECI and the historical GDP growth of the UAE. Thus, the ECI can be considered as a valuable tool for the policymakers to provide them timely information about the development of the UAE economic activity and an early indication of turning points. Keywords: UAE economic activity, Composite indicator, Principal Components Analysis JEL classification codes: C32, C51, C87, E32 2
I. INTRODUCTION Given the increasing need to obtain an early and a clear signal about the current and future developments of economic activity, policymakers examine different economic variables from different sources. Nevertheless, not all economic variables are published simultaneously, with various lags and frequencies, delaying the appropriate policy responses. The most used tool to resolve this problem is the composite indicator, which is an aggregate index of a large number of relevant variables reflecting the development of the economic activity. While such indicators are widely used and well developed in all advanced economies, it has received relatively little attention in many emerging and developing economies, because of the lack of long historical as well as high frequency reliable data. In the United Arab Emirates (UAE), to overcome the lack of the quarterly GDP, the Central Bank of UAE (CBUAE) constructed an Economic Composite Indicator (ECI) to assess the economic activity of the UAE. This is the first experience at the national level that aims to improve the understanding of the direction of economic activity and to estimate economic growth on a quarterly basis. This constructed indicator could be a useful analytical and empirical tool for the policymakers since it offers a timely clear picture about the current economic situation. The ECI has three important advantages: first, it takes into account all important policy issues by synthesizing a large number of economic variables, both at the national and international levels. Secondly, it captures economic fluctuations for the UAE at relatively high frequency, compared to the available information. Finally, it will be used to give an early indication of turning points. This paper presents an estimation of an Economic Composite Indicator for the UAE, which aims to provide an interesting reference for the construction of these indicators for other similar economies and also to facilitate future research on the UAE. The remainder of this paper is organized as follows: Section II describes the available data as well as criteria and transformations applied in this paper. Section III gives an overview of the adopted methodological approach, while section IV focuses on the main steps in the construction of the ECI. The section V presents the empirical results and the interpretation of the estimated indicator from a historical perspective. Finally, section VI concludes. 3
II. DATA DESCRIPTION The building of the dataset represents the crucial step for the computation of the composite indicator, since the quality of the constructed index depends highly on the quality of data. The dataset should contain relevant variables on a quarterly basis reflecting the overall economic activity and taking into account all important policy issues. However, the choice of the data sample is dictated by data availability and the structure of the economy. While there are some reliable and well-established databases for most of the advanced countries, there is no large dataset containing a large number of macroeconomic variables for the UAE, at the requested frequency. Therefore, the selected macroeconomic variables are collected from different sources, in order to obtain a data set that covers a wide range of economic indicators, such as the global economy, sectorial activity, financial markets, price trends and the money market. To ensure objectivity throughout the selection process of most appropriate series, we have chosen two main criteria: economic significance and statistical significance. The first requirement is that the selected series should ensure good representation of the relevant sectors of the economy and reflect the anticipation of future economic fluctuations. The second requirement is the availability of long historical series on a quarterly basis, timely and easily accessible. These variables should be highly correlated with GDP growth, with clear trends and low volatility. Thus, according to the two criteria and the availability of data, the final dataset consists of 14 series on a quarterly basis from 2006Q1 to 2017 Q1. It covers, as much as possible, the major sectors of the UAE economy. It also includes some international variables that can influence the national economic activity. The table below presents the detailed list of all variables used to construct the ECI. Table 1: Information on variables used to construct the ECI Sector Variables Sources Global Economy US GDP (Quarterly y-o-y, % change) EU GDP (Quarterly y-o-y, % change) Emerging market GDP (Quarterly y-o-y, % change) Brent oil price (Quarterly y-o-y, % change) International Monetary Fund International Monetary Fund International Monetary Fund Energy Information Administration Financial Markets MSCI UAE (Quarterly y-o-y, % change) Bloomberg/ MSCI Prices CPI (Quarterly y-o-y, % change) UAE's oil production (Barrel/day) Federal Competitiveness and Statistics Authority OPEC Sectorial activity Money Industrial Production Index (IPI) (Quarterly y-o-y, % change) UAE airport passenger traffic (Quarterly y-o-y, % change) Dubai Economic Tracker (DET) M3 (Quarterly y-o-y, % change) Domestic credit (Quarterly y-o-y, % change) Gross international reserves (Quarterly y-o-y, % change) Real effective exchange rates Statistics Centre Abu Dhabi UAE airports Emirate NBD / Markit Central Bank of UAE Central Bank of UAE Central Bank of UAE Bank for International Settlements 4
III. METHODOLOGICAL APPROACH A wide range of methodologies for estimating the composite indices have been developed over time and used in many advanced economies, such as the weighted average method, the regression model and the principal component approach. However, the choice of the appropriate methodology depends highly on the quality of the data available and on the reference indicator measuring the economic activity. In order to construct a composite index, the first step is to determine a reference series for the state of the economy. The main variable that more closely approximates the economic activity and is mainly used is real GDP. According to the literature, the non-oil GDP, the non-agricultural GDP and the Industrial Production Index (IPI) are the main reference series used in many developed countries and international organizations for building quarterly composite indices. Nevertheless, in the UAE, real GDP and its components are available only on an annual basis and with a considerable publication delay, while the available (Industrial Proudction Index) IPI provides only a partial coverage of the economy, especially with the rising share of services and financial sector which are not taken into consideration. Given the absence of a relevant reference variable and the lack of long historical, as well as high frequency reliable data, the Principal Component Analysis (PCA) is the most appropriate method to calculate this indicator for the UAE. This approach, introduced for the first time by Karl Pearson (1901) and developed afterwards by Harold Hotelling (1933), aims to extract a common factor from a group of relevant economic series and to capture the highest level of common trend. Formally, the PCA approach stipulates that for any set of variables X 1, X 2, X 3,, X n, n>=1, it is possible to create a set of variables Z 1, Z 2, Z 3,, Z n, n>=1 as a linear combination of the first set. Analytically, the model can be presented as follows: Where is the coefficient of the corresponding eigenvector, to be the biggest eigenvalues of the symmetric variance-covariance matrix of the original variables. The principal components are ranked such that: Var( ) Var( ) Var( ). Thus, the variation of the data set can be described by k principal components, with k n. Therefore, with a few common factors, the contribution of the variables to the total variance is important, especially if the original variables are highly correlated. In this sense, the first principal component is considered as an indicator of economic activity since it contains the most relevant information contained in the data. 5
IV. ESTIMATION OF THE ECI The construction procedures of this Economic Composite Indicator (ECI) consist of four main steps (see figure 1). The first step is to determine a reference series reflecting the economic activity fluctuation. As mentioned in section II, there is no available relevant reference variable that can closely approximate the economic activity on a quarterly basis. To overcome this limitation, many studies and working papers propose to interpolate quarterly real GDP from annual real GDP. However, even if this method has often good results, there might be some errors in the interpolation which can influence the quality and the precision of the constructed indicator. For all these reasons, a reference series is not used in this paper. Figure 1: Main steps in constructing this indicator The second step is the important one, since the quality of the constructed indicator depends highly on the quality of data. As mentioned above, according to the economic and the statistical criteria, the final data set contains 14 time series on a quarterly basis, both at the national and international levels, from 2006Q1 to 2017Q1. In the third step, given the absence of a reference variable, the Principal Component Analysis (PCA) is the most appropriate method to calculate this indicator. This approach aims to extract a common factor from the data set and to capture the highest level of common trend. However, the PCA is very sensitive to the unit of measurement in which the data are expressed. For this reason, before computing the common components, each time series was normalized so that it has a zero sample mean and a unit variance. This procedure allows the series to be independent of any unit of measurement. This normalization is a necessary step in order to avoid overweighting any given time series with a large variance during the estimation of the ECI. 6
The fourth step consists of applying PCA method, by using Eviews, to transform the 14 selected variables to an equal number of orthogonal variables called principal components. Each component contributes to the explanation of the total variance. According to the eigenvalue criterion (Appendix Figure 2), the results obtained by Eviews shows that, there is only one principal component to extract. The contribution of the first component exceeds 60% (see table 2). Therefore, we can consider the first principal component as an indicator of the economic activity of the UAE. Table 2: Eviews output of PCA to construct the ECI Principal Components Analysis Sample (adjusted): 2006Q1 2017Q1 Included observations: 44 after adjustments Computed using: Ordinary correlations Extracting 14 of 14 possible components Eigenvalues: (Sum = 14, Average = 1) Cumulative Cumulative Number Value Difference Proportion Value Proportion 1 8.740900 6.571981 0.624164 8.7409 0.6242 2 2.168919 0.873748 0.154877 10.9098 0.7790 3 1.295171 0.642080 0.092485 12.2050 0.8715 4 0.653091 0.193383 0.046635 12.8581 0.9182 5 0.459708 0.178471 0.032827 13.3178 0.9510 6 0.281237 0.131389 0.020082 13.5990 0.9711 7 0.149848 0.059669 0.010700 13.7489 0.9818 8 0.090180 0.003882 0.006439 13.8391 0.9882 9 0.086298 0.055496 0.006162 13.9212 0.9944 10 0.030802 0.009383 0.002199 13.9520 0.9966 11 0.021419 0.003501 0.001529 13.9734 0.9981 12 0.017918 0.012909 0.001279 13.9913 0.9994 13 0.005009 0.001342 0.000358 13.9963 0.9997 14 0.003667 --- 0.000262 14.0000 1.0000 Given the importance of the oil sector and its implication on the UAE economy, we construct a second index, reflecting the development of the economic activity in the non-oil sector. To this end, the same steps are applied, excluding the Brent oil price and the UAE oil production from the data set (see table 3). 7
Table 3: Eviews output of PCA to construct the Non-oil ECI Principal Components Analysis Sample (adjusted): 2006Q1 2017Q1 Included observations: 44 after adjustments Extracting 12 of 12 possible components Eigenvalues: (Sum = 12, Average = 1) Cumulative Cumulative Number Value Difference Proportion Value Proportion 1 8.216381 6.597105 0.684698 8.216381 0.684698 2 1.619276 0.595204 0.134940 9.835657 0.819638 3 1.024072 0.616531 0.085339 10.859729 0.904977 4 0.407541 0.108029 0.033962 11.267270 0.938939 5 0.299511 0.111434 0.024959 11.566781 0.963898 6 0.188078 0.091410 0.015673 11.754859 0.979572 7 0.096667 0.039799 0.008056 11.851526 0.987627 8 0.056868 0.002330 0.004739 11.908394 0.992366 9 0.054538 0.036087 0.004545 11.962932 0.996911 10 0.018451 0.006366 0.001538 11.981383 0.998449 11 0.012085 0.005552 0.001007 11.993467 0.999456 12 0.006533 --- 0.000544 12.000000 1.000000 Thus, the results obtained by Eviews show that, there is only one principal component to extract, according to the eigenvalue criterion (Appendix Figure 3). The contribution of the first component is around 68% (see table 3). Therefore, we can consider the first principal component as an indicator of the non-oil economic activity of the UAE. V. MAIN RESULTS: ANALYSIS AND APPLICATIONS In this section, the quarterly Economic Composite Indicator (ECI) is constructed using the principal component analysis. The main results confirmed the historical developments observed in the UAE economy, especially during the financial crisis 2008 (Figures 2 & 3). These results are also consistent with the conjecture stipulating that the UAE is experiencing a slowdown amid the continued fiscal consolidation. During the first quarter of 2017, the overall economy expanded by 3.3%, following 3.7% in the previous quarter (Figure 2). This slowdown was due mainly to slower rate of growth in oil production in the first quarter of 2017. In fact, oil production increased by 3.6% in the first quarter of 2017 on a year-on- year basis against an increase of 4.6% in the previous quarter. Moreover, the Non-Oil Economic Composite Indicator (Non-Oil ECI), showed an improvement of the non-oil economic activity in the UAE (Figure 3), reaching 3.1% compared to 2.8% recorded during the last quarter of 2016 on a year-on- year basis. 8
Figure 2 : Overall ECI development (y-o-y, %) Figure 3 : Non-oil ECI development (y-o-y, %) Source: CBUAE analysis Therefore, this ECI can track down ongoing economic developments on a quarterly basis and provide to the policy-makers the current situation of the UAE economy, in the absence of any reliable information about the quarterly GDP, the main measure of the economic activity. Moreover, given the importance of the emirates of Abu Dhabi and Dubai in the UAE economy, Figures 4 & 5 showed the high correlation between the ECI and the GDP growth of the two emirates, confirming the observed trends. Figure 4: Non-oil ECI and Dubai GDP growth Figure 5 : Overall ECI and Abu Dhabi GDP growth Source: DSC, SCAD and CBUAE analysis In addition, the non-oil ECI is highly correlated with the Purchasing Managers Index 2 (PMI), the only high frequency available indicator that is commonly used to track the non-oil economy in the UAE. 2 The Purchasing Managers Index (PMI) measures the performance of UAE s companies in non-oil private sector and is derived from a survey of 400 companies, including manufacturing, services, construction and retail. 9
Both the ECI and the PMI share the same turning points of the non-hydrocarbon activity. This is not surprising, since these two indicators synthesize a large number of information reflecting the economic activity (Appendix figure 1). Figure 6 : The non-oil ECI (Y-o-Y change, %) and the PMI (%) Source: CBUAE analysis On an annual basis, the figures below compare the constructed Composite Indicators to the historical GDP growth of the UAE during the period 2006-2016. These figures clearly show that the annual ECI closely tracks the movements of the GDP growth rates and picks up the major turning points in the series reasonably well. Indeed, their correlations over the whole period are above 95%. Figure 4: ECI and GDP growth (y-o-y, %) growth Figure 5: Non-oil ECI and non-oil GDP Source: FCSA & CBUAE analysis This evidence reaffirms the quality of the index and solidifies the robustness of the methodology followed in its construction as this indicator synthesizes a large number of information that best represents economic activity in the UAE. 10
VI. CONCLUSION This paper tries to construct an ECI for the UAE, in order to provide policymakers high frequency information about the development of the UAE economic activity and an early indication of turning points. Based on this research, the first experiment with the ECI construction has yielded fruitful results. We believe that the methodology adopted to construct the ECI is the most appropriate method to calculate this indicator, taking into consideration all available data. This approach allows to extract a common factor from a group of relevant economic series and to capture the highest level of common trend. Thus, the constructed ECI is a useful tool, which can closely track the economic activity of the UAE on a quarterly basis. However, this study is still in preliminary stage due to the non-availability of the quarterly GDP and the lack of alternative sources for evaluating economic activity. The utility and the quality of the ECI seems promising, but could be improved if more data are available in a long-enough time series for our purposes and if there is more information reflecting the development of economic activity at relatively high frequency. Going forward, quarterly estimates of the ECI will be presented in the context of the Quarterly Review of the Central Bank on a regular basis. Revisions will be illustrated to reflect changes in the estimate based on the most recent available data. Using the updated estimates, projections for the year and subsequent years will be updated in the context of regular publications of the Central Bank. 11
REFERENCES Camba-Mendez, G., M. Kapetanios, R. J. Smith, and M. R. Weale (2001): An automatic leading indicator of economic activity: forecasting GDP growth for European countries, Econometrics Journal, 4, 56 90. Schneider, M., and M. Spitzer, 2004, Forecasting Austrian GDP using the generalized dynamic factor model, Artis, J. M., A. Banerjee, and M. Marcellino (2002): Factor forecasts for the UK, CEPR discussion paper 3119, Center for Economic and Policy Research. Hotelling, Harold (September 1933). "Analysis of a complex of statistical variables into principal components". Journal of Educational Psychology (American Psychological Association) 24 (6): 417 441. Geweke, J., 1977, The Dynamic Factor Analysis of Economic Time Series Models. Paper presented at the Latent Variables in Socio-Economic, Amsterdam. Bai, J., and S. Ng, 2002, Determining the Number of Factors in Approximate Factor Models, Econometrica, 70(1), pp. 191 221. Boivin, J., & S. Ng, 2006, Are More Data Always Better for Factor Analysis?, Journal of Econometrics, 132(1), pp. 169 194. Cristadoro, R., M. Forni, L. Reichlin, and G. Veronese, 2005, A Core Inflation Indicator for the Euro Area, Journal of Money, Credit, and Banking, 37(3), pp. 539 560. Forni, M., M. Hallin, M. Lippi, and L. Reichlin, 2005 The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting, Journal of the American Statistical Association, 100(471), pp. 830-840. 12
APPENDIX Appendix Figure 1: The Economic Composite Indicator and the relevant variable reflecting the UAE economy Quarterly non-oil ECI vs MSCI Quarterly non-oil ECI vs DET Quarterly ECI vs Credit Quarterly non-oil ECI vs US GDP Quarterly ECI vs Brent price Quarterly non-oil ECI vs M3 growth Source: FCSA, IMF, SCAD & CBUAE analysis 13
Appendix Figure 2: Variance Explained of the Principal Components for the ECI Appendix Figure 3: Variance Explained of the Principal Components for the non-oil ECI 14