Composite Leading Indicators Research Articles

Analysis of cyclical behaviour of investment expenditures for the Turkish economy

This article aims to analyse the cyclical behaviour of fixed investments in Turkey and then construct a Composite Leading Indicators Index (CLI) to forecast the turning points of investment. The cycles of investment and selected leading indicators are identified following the growth cycle approach. The series are detrended using the Hodrick–Prescott (HP) detrending filter. After identification of the cycles and turning points of the selected leading indicators, they are combined into a single composite index. Then a probit model is estimated to evaluate the forecast performance of the composite index. The results show that the constructed CLI is considered to be helpful in monitoring and predicting investment expenditures.

Predictive Power of Confidence Indicators for the Russian Economy

This study examines the forecasting power of confidence indicators for the Russian economy. ARX models are fitted to the six confidence or composite indicators, which were then compared to a simple benchmark AR-model. The study used the output of the five main branches as the reference series. Empirical evidence suggests that confidence indicators do have forecasting power. The power is strongly influenced by the way which the indicator is constructed from the component series. The HSBC Purchasing Managers' Index (PMI), the OECD Composite Leading Indicator (CLI) and the OECD Business Confidence Indicator (BCI) were the best performers in terms of both the information criterion and forecasting accuracy.

A Note on Band-Pass Filters Based on the Hodrick-Prescott Filter and the OECD System of Composite Leading Indicators

The Organisation for Economic Co-operation and Development (OECD) began to use a band-pass filter, based on the Hodrick-Prescott filter (HPband-pass filter), to calculate the composite leading indicators (CLIs) in December2008. Other than the filter adopted by the OECD, there is an alternative HPband-pass filter. This note examines whether the application of these two alternative HPband-pass filters in the calculations of the OECD’s CLIs lead to negligible differences. JEL Classification: E32; C22 Keywords: Hodrick-Prescott band-pass filter; Composite leading indicators.© beawolf - Fotolia.com

The Forecasting Performance of Composite Leading Indicators

Using OECD Composite Leading Indicators (CLI), we assess empirically whether the ability of the country-specific CLIs to predict economic activity has diminished in recent years, e.g. due to rapid advances in globalisation. Overall, we find evidence that the CLI encompasses useful information for forecasting industrial production, particularly over horizons of four to eight months ahead. The evidence is particularly strong when taking cointegration relationships into account. At the same time, we find indications that the forecast accuracy has declined over time for several countries. Augmenting the country-specific CLI with a leading indicator of the external environment and employing forecast combination techniques improves the forecast performance for several economies. Over time, the increasing importance of international dependencies is documented by relative performance gains of the extended model for selected countries.

A Composite Leading Indicator of Tunisian Inflation

This paper investigates the possibility of constructing a composite leading indicator (CLI) of Tunisian inflation. For doing so, partial information about future inflation rate provided by a number of basic series is analyzed first. Based on the correlation analysis, a few of these basic series are chosen for construction of composite indicator. Empirical results show that the deviation from long‐term trend of two monetary aggregates (M1 and M3), short‐term interest rate (TMM), real effective exchange rate and crude petroleum production, are important leading indicators for inflation rate in Tunisia. Accordingly, based on monthly data on these basic series, one composite indicator is constructed and its performance is assessed by using turning point analysis, granger causality tests, and impulse response functions. The results indicate that our composite indicator is useful in anticipating changes in inflation rates in Tunisia.

A comparison of two alternative composite leading indicators for detecting Japanese business cycle turning points

The Organisation for Economic Cooperation and Development (OECD) has developed a system of Composite Leading Indicators (CLIs) for its member countries. On the other hand, the Japanese government has released another CLI for detecting the Japanese business cycle turning points. Both CLIs are widely used alternatives. These two CLIs may provide different business forecasts. When different forecasts occur, how can we interpret the discrepancies? This article tries to answer this question by clarifying their relationships.

Predicting Quarterly Hong Kong Tourism Demand Growth Rates, Directional Changes and Turning Points with Composite Leading Indicators

This study predicts numerical demand growth rates, directional changes and turning points in the growth rate using the single input leading indicator model and assesses its forecasting performance with the ARIMA model and the no-change model. To assess the forecasting performance from the March quarter of 2004 to the December quarter of 2006, models are fitted to the growth rates of Hong Kong inbound tourism demand from selected tourism markets (Australia, Japan, the UK and the USA). Composite leading indicators for the single input leading indicator model are constructed from selected national leading and lagged indicators. To avoid false signals in turning points, a method is specified to identify the correct turning points in tourism demand growth rates. The prediction performance of these models is then examined, based on the mean absolute percentage error, directional change error and turning point error. A statistical procedure is considered to determine whether the actual and predicted directional changes and turning points are independent or associated.

Composite Leading Indicators for Ukraine: An Early Warning Model

Our objective has been to experiment with diverse economic indicators in order to help equip Ukrainian policymakers with a relatively simple tool, which could deliver warning signals about a possibility of upcoming economic problems and thereby assist the Government in designing policy instruments which would help prevent or soften a slowdown or recession. The project has undertaken the following tasks: • Based on an analysis of the pattern of growth of the Ukrainian economy since the end of the post-Soviet recession (the year 2000) we have formulated the hypotheses concerning the factors preceding/affecting the upturns and downturns (with a focus on the latter) of the country’s growth; • We have studied international best practice in early warning indicators in order to design a similar system for Ukraine; • We have selected the relevant indicators, consistent with our hypotheses and used a probit model in order to experiment with these indicators; • The final set of indicators used in the model included the following lagged independent variables: changes in the value of export, changes in real exchange rate of the hryvnya, producers' price index adjusted for domestic price inflation index and the IMF’s metal price index, bank credit interest rate, changes in the industrial output of the European Union; our dependent variable (which was used as a proxy for the overall economic growth) was changes in real industrial output; • The model was used to formulate a warning forecast for the Ukrainian economy for the second half of 2008 based on the data for the January 2000 - June 2008 period; all predictions for the second half of 2008 have delivered warning about a downturn of the Ukrainian economy; • We ran a few additional experiments with the model, and • We have recommended several further steps of analysis toward a full implementation and institutionalization of such a model in the near future.

Leading Indicators in a Globalised World

Using OECD composite leading indicators (CLI), we assess empirically whether the ability of the country-specific CLIs to predict economic activity has diminished in recent years, e.g. due to rapid advances in globalisation. Overall, we find evidence that the CLI encompasses useful information for forecasting industrial production, particularly over horizons of four to eight months ahead. The evidence is particularly strong when taking cointegration relationships into account. At the same time, we find indications that the forecast accuracy has declined over time for several countries. Augmenting the country-specific CLI with a leading indicator of the external environment and employing forecast combination techniques improves the forecast performance for several economies. Over time, the increasing importance of international dependencies is documented by relative performance gains of the extended model for selected countries.

Current Period Performance of OECD Composite Leading Indicators

This paper presents a comprehensive analysis of the current period performance of the OECD composite leading indicators (CLIs) for 21 OECD Member countries and three zone aggregates for which CLIs are available for a longer time period. The main aim of the current analysis on CLIs is to further evaluate the quality of the indicator in order to identify areas where their reliability could be improved. The results show that first estimates of CLIs are revised frequently but the size of revisions is rather small for most countries and almost neglectable for zone aggregates and there is no evidence of bias. The OECD CLI is, however, designed to provide early signals of turning points (peaks and troughs) between expansions and slowdowns of economic activity. Forecasting turning points is one of the main objectives of the leading indicator technique, because predicting the timing of cyclical turning points is one of the least reliable activities in economic forecasting. The results provide evidence that first and second estimates of year-on-year growth rates give reliable signals of approaching cyclical turning points. Finally, the importance of smoothness of components in the calculation of first and second estimates of the CLI and the overall smoothness of the CLI itself is noted in the findings. The results support the argument that it is not enough to have timely components they also need to be smooth to guarantee small revisions. Overall, this study has shown that whilst it could be dangerous to draw conclusions on the directions up or down in growth rates from one or two months figures for several countries, the first and second estimates of the CLIs give early signals of approaching turning points which in most cases are not revised later.

Forecasting the German Cyclical Turning Points: Dynamic Bi-Factor Model with Markov Switching

Summary This paper proposes a dynamic bi-factor model with Markov switching which detects and predicts turning points of the German business cycle. It estimates simultaneously the composite leading indicator (CLI) and composite coincident indicator (CCI) together with corresponding probabilities of a recession. According to the bi-factor model, CLI leads CCI by about 3 months at both peaks and troughs. The model-derived recession probabilities of CCI and CLI capture the turning points of the ECRI’s and OECD’s reference cycles much better than the dynamic single-factor model with Markov switching.

Composite leading indicators of underlying inflation for seven EU countries

This paper presents short- and long-term composite leading indicators (CLIs) of underlying inflation for seven EU countries, namely Belgium, Germany, France, Italy, the Netherlands, Sweden and the UK. CLI and CPI reference series are calculated in terms of both growth rates and in deviations from its trend. The composite leading indicators are based on leading basic series, such as sources of inflation, series containing information on inflation expectations and prices of intermediate goods and services. Neftci's decision rule approach has been applied to transfer movements in the CLIs into a measure of the probability of a cyclical turning point, which enables the screening out of false turning point predictions. Finally, CLIs have been used to analyse the international coherence of price cycles. The forecast performance of CLIs of inflation over the past raises hope that this forecast instrument can be useful in predicting future price movements. Copyright © 1999 John Wiley & Sons, Ltd.

Probability forecast of downturn in U.S. economy using classical statistical decision theory

This paper presents a methodology for producing a probability forecast of a turning point in U.S. economy using Composite Leading Indicators. This methodology is based on classical statistical decision theory and uses information-theoretic measurement to produce a probability. The methodology is flexible using as many historical data points as desired. This methodology is applied to producing probability forecasts of a downturn in U.S. economy in the 1970–1990 period. Four probability forecasts are produced using different amounts of information. The performance of these forecasts is evaluated using the actual downturn points and the scores measuring accuracy, calibration, and resolution. An indirect comparison of these forecasts with Diebold and Rudebusch's sequential probability recursion is also presented. It is shown that the performances of our best two models are statistically different from the performance of the three-consecutive-month decline model and are the same as the one for the best probit model. The probit model, however, is more conservative in its predictions than our two models.

Scoring the composite leading indicators: A Bayesian turning points approach

AbstractThis paper explores a Bayesian decision‐theoretic approach for analysis and development of composite leading indicators. The methods used here are derived from work by Zellneret al.(1988) and Zellner and Hong (1988) aimed at forecasts time series turning points, and the multi‐process mixture models first described by Harrison and Stevens (1976) and more recently in West and Harrison (1989). Here, these methods are used to develop composite leading indicators formed by using the posterior probabilities derived from predictive relations between the individual indicator variables and the state of the economy measure as weights. This study, like those of Wecker (1979), Kling (1987) and Diebold and Rudebusch (1989), uses the time series observations on the measure of economic activity which we wish to predict along with an explicit definition of a turning point, either a downturn or upturn. Unlike those studies, we then establish a predictive relation between the individual component indicator series and the variable measuring economic activity which allows a Bayesian computation of probabilities associated with the turning point events. This parallels the developments in Zellneret al.(1988), where the focus was on forecasting turning points in economic times series. These probabilities are conditioned on the past data and the predictive probability density function (pdf) for future observations. A composite indicator is devised using a Class I, multi‐process mixture model suggested in West and Harrision (1989). The composite indicator arising from this approach is an average of the individual component series, where the averaging is done over the posterior probabilities of the individual component series predictive relations. An example of the procedure is provided using the national composite leading indicator (CLI).

THE USE OF TENDENCY SURVEYS IN EXTRAPOLATING NATIONAL ACCOUNTS

A well‐known way of using tendency survey data is the construction of Composite Leading Indicators (CLIs). It is rather peculiar that CLIs pretend to predict macroeconomic aggregates, while their construction is not consistent with the way actual macroeconomic statistics are compiled. This paper contains preliminary results of an attempt to integrate tendency survey indicators into the framework of the Dutch Quarterly Accounts. We conclude that tendency survey indicators can contribute to an explanation of trends in “real” economic variables. Many of these indicators lead the Quarterly Accounts indicators. They can, therefore, contribute to a timely estimation of National Accounts aggregates.