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Chapter outline. • Scope and target audience of handbook. • Step by step guide to construct composite indicator (see Handbook OECD and.
Typology: Schemes and Mind Maps
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1.1. Chapter outline
1.2. Summary
This chapter is an introductory chapter that defines the scope of the handbook. Details will be confirmed once annotated outlines of other chapters have been agreed on.
1.3. Points of discussion
3.1. Chapter outline
3.2. Discontinuities in time series: back casting exercises
Sample surveys conducted by national statistical institutes are generally conducted repeatedly in time with the purpose of constructing time series that describe the evolution of population parameters of interest. An important quality aspect of these surveys is comparability of the outcomes over time. To maintain consistent time series, the underlying survey process is generally kept unchanged as long as possible. It remains, however, inevitable to change or redesign a survey process from time to time. A major drawback of such redesigns is that it often has systematic effects on the outcomes of the survey, leading to discontinuities in the series. An important aspect of a survey redesign is to minimize this inconvenience for data users. This can be accomplished by quantifying the effect of the redesign on the outcomes of the main parameters. Van den Brakel et al. (2008) discuss different statistical methods to deal with discontinuities due to survey redesigns. One possibility is to apply an intervention analysis using structural time series models. In this case the time series is modelled with an appropriate structural time series model and the discontinuities in the underlying survey design are modelled with intervention variables. The statistical methodology of this time series modelling approach with state-space models is described in detail by Van den Brakel and Roels (2010). To maintain consistent time series, one might consider to correct the series observed in the past with the observed effects of the redesign. This is sometimes referred to as backcasting. This chapter develops the statistical time series modelling approach to construct long consistent series which are the basis to construct composite economic indicators.
3.3. Unavailability of appropriate price indexes
The use of price indexes in this context can be twofold. Firstly, an appropriately chosen price index may be desirable in itself as part of a composite indicator. Problems arise if in some countries this index is unavailable or not published in a timely fashion. Secondly, and more importantly, price indexes will be needed to deflate (other) indicators. The choice of deflator obviously depends on the indicators selected. The coverage of the price indexes compiled differs across
countries. In particular, good-quality PPI's are still lacking in many countries for parts of the services sector. There are several options to deal with this problem, although it will be difficult in general to quantify their appropriateness. For example, in some cases higher-level aggregate indexes, or indexes for similar industries, might be used instead. Also, price indexes from other countries can be used, such as approximating an import price index by an export price index from another country.
3.4. Problems related to the presence of outliers and of seasonal and calendar effects: Seasonal Adjustment
Seasonal adjustment plays an important role in analysing cyclical movements. Seasonally adjusted growth rates show how the economy develops and can provide help in determining turning points. At the same time the seasonal adjustment process is susceptible to strong fluctuations in the time series. The process is therefore subject tot several kinds of uncertainty, the magnitude of which can never be assessed precisely. One of the main problems in estimating the seasonal pattern is deciding on which part of the fluctuations should be attributed to which time series component (trend, seasonal, cyclical, and irregular). Adjustment at the recent end of a time series is particularly difficult, especially in times of strong economic changes. It is difficult at an early stage to assess whether fluctuations are temporary or indicate a more structural change in the seasonal pattern. Ideally, we should wait for more observations to be available before proper seasonal adjustment can be done. A common solution is to model sudden changes as outliers so that seasonal estimates are not affected too much. This assumes the seasonal pattern does not change in the short term. The main question is then what type of outlier we are dealing with. The most common types are additive outliers, level shifts, or transitory changes. Determining the exact type is difficult (or impossible) if we do not know the future of the time series. An alternative solution is not to model outliers and letting the trend-cycle and seasonal estimates absorb fluctuations.
Points of discussion:
Point of discussion :
3.7. Relevant documentations
3.8. Other points of discussion:
4.1. Chapter outline
4.2. Summary
When dealing with turning points composite indicators usually we start from a quite small dataset on which the following steps should be accomplished to achieve the optima selection of variables:
a) Analyze the leading-lagging structure of the variable; b) Identify for each variable or group of variables what kind of cycle they are describing in a closer way (E.g. growth cycle in the case of qualitative surveys); c) Identify the most appropriate set of transformation to be applied to data in order to have clear and less noised cyclical signals.
Usually these kinds of indicators are built up staring from a quite small dataset of containing potential candidate variables to be used. In this specific case the selection is then done mostly by using a prior knowledge of data and checking them, empirically, using indicators such as the QPS and the concordance index. This does not exclude the use or more formal selection criteria (e.g. classification techniques). Nevertheless the use of factor analysis and/or principal components is not largely used when constructing turning points indicators.
4.3. Points of discussion:
Subject to the agreement, the section will go on to recommend a harmonized set of component indicators to be used for the construction of business cycle composite indicators. This information will not only be useful for those compiling business cycle composite indicators, but also form the basis for statistical agencies for the collection of economic short term statistics that are relevant for the construction of the business cycle composite indicators.
Points of discussion:
5.5. The choice of the reference cycle
As it is important for producers to have a clear identification of the kind of business cycle they are referring to, this section will first state and describe conventional approaches that define business cycle, namely 1, classical cycle; 2, growth (deviation-from-trend) cycle; and 3, acceleration (growth rate) cycle. The classical business cycles are identified as recurrent, alternating phases of expansion and contraction in a large number of economic activities such as output, consumption, prices, investment, employment, etc. The cycles are characterized by co-movements in the fluctuations of the economic activities, with periodicities larger than one year. The concept of growth (deviation-from-trend) cycles can be defined in terms of the deviations of the economy from its potential (i.e. the long-term trend). The concept of acceleration cycles refers to the sequences of prolonged phases of acceleration and deceleration, which is measured by cyclical changes in the growth rate of economic activity. The underlying theory and assumptions, together with the advantages and disadvantages of each approach will be discussed. This section will also elaborate the usefulness of simultaneous monitoring of classical and growth cycle.
Points of discussion:
5.6. Detrending methods: Parametric versus non parametric detrending methods
A convention approach to business cycle measurement is the concept of growth (deviation-from-trend) cycle, where business cycle fluctuations are identified in terms of the deviations from the trend of the process. Hence, the construction of cyclical indicators involves the detrending process in order to identify the underlying cyclical pattern in the data.
This section describes the methodology of different types of parametric and non- parametric detrending procedure. First, filtering techniques are needed in order to achieve noise minimization and a proper estimation of trend-cycle component. Then detrending process is the applied to the estimated trend-cycle component to obtain the cyclical component of the series.
Notice that the detrending methods addressed in this section are univariate in nature and are purely a statistical based decomposition. Multivariate extension will be addressed in the later section. The large majority of the univariate detrending methods have been developed starting from the beginning of 1980s. Such methods can be classified into two main categories listed as follows:
5.6.1. Non-parametric methods
5.6.2. Parametric methods
This section will also address the limitation of detrending techniques, which include but not limited to issues such as instabilities of current estimates, over-smoothing the series, etc.
Point of discussion :
5.7. Aggregation of individual signals: choice of the weights versus combining forecasts
In order to emphasize the cyclical patterns in the data and de-emphasize the volatility of individual indicators, the best of them are combined into composite indexes. One motivation for combining several indicators into one composite index is the observation that there is no single “best” indicator. Another important motivation is to have a measure of diffusion among several indicators since business cycles are phenomena that are observed nearly simultaneously across all aspects of the economy. These business cycle composite indexes serve as summary measures of the current and historical behavior of the cyclical indicators.
The idea of a composite index is an extension of diffusion indexes which measure the proportion of components that are rising. In contrast to diffusion indexes, the
5.10. Other points of discussion
6.1. Chapter outline
6.2. Key points
Turning points under different cyclical definition, unified approach ABCD or αABβCD, limits of turning points indicators for classical business cycle, usefulness of detecting simultaneously turning points of different cycles, usefulness of multivariate methods to ensure the full consistency with the theoretical turning points sequence ABCD or αABβCD.
6.3. Introduction
A special case of composite indicators delivering to users signals is on the probability to be in a recessionary phase and on the occurrence of turning points. They complement cyclical indicators in the sense that the later do not necessary supply clear information on turning points. They can also be easily red and understudy even in a graphical way.
6.4. On the reference cycle
A first choice to be made by producers is a clear identification of the kind of cycle they are referring to: classical cycle, growth cycle or deviation cycle, acceleration cycle. The second important choice is to identify a reference cycle on which the composite indicator has to be validated over a sufficiently long period covering whenever possible several cycles. The ideal candidate for this role is an official chronology but, since official chronologies do not necessarily exist at national level and for all kind of cycle, approximate solutions have to be identified. The most obvious one is, subject to the availability of long time series; to produce a non official chronology by dating the target cycle starting from official statistics by using a sample and non-parametric dating rules. The composite indicator should be built up in such way to replicate as close as possible such chronology.
ii. Use of combining forecasting techniques; iii. Subjective definition of weights based on the relative reliability of signals returned by each component series.
Alternatively a multivariate approach can be adopted. In this case all component series will be modeled simultaneously. The delivered signal will then be already combined and the waiting structure is implicitly defined within the modeling procedure. Obviously in the case of a multivariate specification the level of threshold, the number of states as well as the application of censoring rules will be defined taking into account not the individual series behaviour, but the behaviour of the multivariate specification.
a) Additional considerations on the construction of turning points composite indicators b) A comparison of alternative specifications c) Some examples
6.7. Points of discussion
7.1. Chapter outline
7.2. Key points
Relationships between composite indicators to measure economic growth, nowcasting and flash estimates, volatility of macroeconomic variables and its effects on the reliability of composite indicators, the relative importance of soft and hard data to anticipate economic growth.
7.3. Points of discussion
