Coincident Composite Index Research Articles

A financial stress index for a highly dollarized developing country: The case of Lebanon

The aim of this paper is to construct the first comprehensive Financial Stress Index for Lebanon, dubbed the IFEFSI (Institute of Financial Economics Financial Stress Index). This is a broad coincident composite index that includes three different market segments; the banking sector, the equities market, and the foreign exchange and other markets. It is constructed as a continuous real-time measure that quantifies the level of systemic stress by measuring latent conditions. As a metric for financial conditions, the IFEFSI should provide valuable information to macroprudential regulators whose aim is to maintain a smooth and resilient financial system. By using it as a tool to help monitor, identify, and address any potential crisis, they are better equipped to maintain financial and economic stability in Lebanon.

경제환경 변화와 항공여객 수요 간의 관계 분석: 인천국제공항을 중심으로

The purpose of this study is to analyze the impact of macroeconomic variables on air passenger demand and provide useful information to airport managers and policymakers. Therefore, using the quarterly macroeconomic indicators from 2002 to 2017, the relationship with air passenger demand was demonstrated by multiple regression analysis. In the previous studies, they used GDP, Korea Treasury Bond, KOSPI index, USD/KRW Exchange Rate, and WTI Crude Oil Price variables. In this study, we used the Coincident Composite Index, Employment Rate, Consumer Sentiment Index, and Private Consumption Rate used as additional variables. It has confirmed that if the consumption of research results expands or the economic environment is right, it will affect the increase in international passengers. In other words, it confirmed that the overall economic situation acts as the main factor determining air passenger demand. It confirmed that the economic environment at the past has a significant impact on air passenger demand.

Spillover Effects of Regional Apartment Business Cycles Using Factor-Augmented VAR Model

구는 FAVAR 모형을 이용하여 지역별 아파트 경기지수를 작성하고, 아파트 경기지 수의 적정성을 검증하고, 작성된 아파트 경기지수를 이용하여 지역별 아파트 경기의 전이 효과를 분석하였다. 실증분석결과, FAVAR 모형을 이용하여 작성한 전국아파트 경기지수와 통계청에서 발표 하는 경기동행지수 순환변동과 유사한 움직임을 보이며, 특히 2008년 금융위기와 그 이후의 경제 충격을 전국 아파트 경기지수가 잘 트레이싱(tracing)하는 것으로 분석되었으며, 전국 아파트 경기지수는 경기동행 순환변동을 약 4-5개월 정도 선행하는 것으로 나타났다. 지역별 아파트 경기지수 분석결과, 2008년 세계금융위기의 영향을 공통적으로 받은 것을 확인되었지만, 금융위기 이후에는 지역별 아파트 경기지수가 차이를 보이는 것으로 분석되었다. 이러한 결과는 중앙정부에서 시행한 부동산 정책에 대한 효과가 지역별로 다르게 나타날 수 있다는 것이며, 이는 부동산 정책을 시행할 때, 지역별 특성과 영향을 고려해야 함을 시사하는 것이다. 지역별 경기지수 전이효과를 종합하면, 아파트 경기지수는 서울과 수도권이 상호 영향을 주고 받으며, 부산에도 상당한 영향을 미치는 것을 확인할 수 있지만, 부산에서 서울과 수도권에 미치는 영향은 상대적으로 미미하다고 할 수 있다.We used the FAVAR model in order to estimate the regional apartment business cycles. We also analyzed the regional spillover effects by using the methodology suggested by Diebold and Yilmaz (2009, 2012). Based on this analysis, a similarity was found between the national apartment business cycle and the coincident composite index. In addition, the national apartment business cycle can be traced back to the 2008 financial crisis and subsequent economic shocks. The national apartment business cycle also led the coincident composite index by about 4-5 months. The analysis of the regional apartment business cycles showed that the regional apartment business cycles were commonly affected by the 2008 global financial crisis. However, the regional apartment business cycles varied after the financial crisis. These results indicate that the real estate policies of the government should be differentiated by region. Moreover, there is a difference between the economic environment of each region. We conclude that when implementing real estate policies, regional characteristics and impacts need to be considered. The analysis result of the transition effect of regional economic indexes showed that the apartment business cycles have a significant influence on both Busan and Seoul. However, Busan has a relatively small impact on Seoul and the metropolitan area.We estimate that the variation in Seoul’s apartment business cycle is only about 0.291 due to the change within Seoul, and that 0.709 of the transition effect is explained by the change in the other regions. Meanwhile, the transfer effect of Incheon to the other regions was 0.819.

Earnings Persistence Over The Macroeconomic Cycle: Evidence From Korea

This paper examines whether the persistence of earnings components is affected by the macroeconomic cycle in Korea. To measure the macroeconomic cycle, we use the cycle variation value of Coincident Composite Index (CCI) data obtained from the Korea National Statistics Office. Results from a sample of 21,232 firm-quarter observations over the period 2002-2013 indicate that accruals (cash flows) are more persistent than cash flows (accruals) during expansions (recessions). Also, when going from an expansion to a recession, a decline in accruals persistence is greater than that in cash flows persistence. When total accruals are decomposed into non-discretionary and discretionary portions using the modified Jones model (Dechow et al., 1995), we find that non-discretionary accruals are most persistent than the other components during both expansions and recessions, and a decline in persistence is largest (smallest) for discretionary accruals (cash flows) when going from an expansion to a recession. Most of these results hold when we split the macroeconomic cycle into four phases including transitory periods. Taken together, we provide evidence on the differential effects of macroeconomic cycle on the persistence of individual earnings components in Korea. Our findings suggest that macroeconomic variables are needed to be considered in studies on earnings persistence.

소비자물가지수의 시계열모형 연구

소비자물가지수는 국가의 중요한 경제 척도 중의 하나이다. 본 연구에서는 4개 도시, 서울, 부산, 대구, 광주지역의 소비자물가지수를 연구하였다. 자료는 모두 통계청에서 발췌하였고, 기간은 1998년-2011년 월별자료이며, 시계열분석 기법인 자기회귀오차모형으로 분석하였다. 소비자물가 분석을 위한 설명변수는 9가지 경제변수인 경기동행지수, 미국환욜, 생산자물가지수, 원유수입단가, 원유수입물량, 국제경상수지, 수입물가지수, 실업율, 화폐통화량을 사용하였다. 분석 결과, 자기회귀오차모형으로 각 지역별 소비자물가지수를 46%-52% 정도 설명할 수 있다. The consumer price index (CPI) data is one of the important economic measurement of the country. In this article, the Autoregressive Error (ARE) model has been considered for analyzing the monthly CPI data at Seoul, Pusan, Daegu, and Gwangju Cities in Korea, In the ARE model, nine economic variables are used as the explanatory variables for the CPI data set. The nine explanatory variables are CCI (coincident composite index), won-dollar rate, producer price index, oil import price, oil import volume, international current account, import price index, unemployment rate, and amount of currency. The result showed that the monthly ARE models explained about 46-52% for describing the CPI.

Structural Change Detection in Multivariate Dynamic Linear Model with Application to Business Cycle

A Bayesian method to detect structural changes in multivariate dynamic linear model is introduced and it is applied to predicting and dating the turns in business cycle. As many researchers use for business cycle analysis, the composite leading index (CLI) and the composite coincident index (CCI) are used in this article and they are modeled as VARIMA(0,1,1) with drift terms where their drifts are abruptly changed by a random shock. Under the assumption of lead-lag relationship between CLI and CCI, the signaling procedure for predicting and dating the turns in business cycle is suggested. It uses the posterior probability that there is a drift change in CLI as a predictive signal for turning point and the posterior probability of a drift change in CCI based on predictive signal as dating signal. The proposed signaling procedure is applied to US business cycle and its result is compared with NBER chronology.

Forecasting with Leading Indicators by means of the Principal Covariate Index

A new method of leading index construction is proposed, which explicitly takes into account the purpose of using the index for forecasting a coincident economic indicator. This so-called principal covariate index combines the need for compressing the information in a large number of individual leading indicator variables with the objective of forecasting. In an empirical application to forecast future growth rates of the Conference Board’s Composite Coincident Index and its constituents, the forecasts of the principal covariate index are more accurate than those obtained either from the Composite Leading Index of the Conference Board or from an alternative index-based on principal components. JEL Classification: C32, C53, E27 Keywords: index construction, business cycles, principal component, principal covariate, time series forecasting, variable selection

Real-time macroeconomic forecasting with leading indicators: An empirical comparison

This paper demonstrates that the Conference Board’s Composite Leading Index (CLI) has significant real-time predictive ability for Industrial Production (IP) growth rates at horizons from one to six months ahead over the period 1989–2009. A popular but unrealistic analysis, which combines real-time data for CLI and final vintage data for IP as predictor variables, obscures the actual predictive content of the CLI, in the sense that in that case, the improvements in forecast accuracy relative to a univariate AR model are not significant. The CLI appears to be less useful for forecasting growth rates of the Conference Board’s Composite Coincident Index (CCI) in real time, as a univariate AR model performs better. This result is mostly due to its disappointing performance during the first five years of the forecast period. The CLI may not be the best way of exploiting the information contained in the underlying individual leading indicator variables. The use of principal components instead of CLI leads to more accurate real-time forecasts for both IP and CCI growth rates.

패널 데이터모형을 적용한 국내일반은행 연체율 결정요인 추정에 관한 연구

Abstract In respect complication of group, rate of arrearage in domestic bank is composed of various factors. This paper studies focus on estimating the determinants of the rate of arrearage in domestic bank using panel data model. The volume of analysis consist of 3 groups(loaned patterns of enterprise, housekeeping, credit card). Analyzing period be formed over a 54 point(2005. 1~ 2009. 06). In this paper dependent variable setting up rate of arrearage in domestic bank, explanatory(independent) variables composed of the consumer price index, composite stock price index, rate of exchange, the coincident composite index, national housing bonds and employment rate. The result of estimating the rate of arrearage in domestic bank provides empirical evidences of significance positive relationships between the consumer price index However this study provides empirical evidences of significance negative relationships between the coincident composite index and the composite stock price index. The explanatory variables, that is, rate of exchange, national housing bonds and the employment rate are non-significance variables of negative factor. Implication of these findings are discussed for content research and practices.Key Words : Random effect model, Fixed effect model, One-Way Error Component Regression Model

Dating rules for turning points of growth cycles in Korea

A business cycle is recognized as a growth cycle in a continuously growing economy such as Korea. This paper suggests reasonable dating rules for the reference date of a business cycle using various measures of a growth cycle. These measures are a cyclical component of the coincident composite index (CI), a coincident cumulative diffusion index, and a historical diffusion index with coincident component indicators. Dating rules include identifying turning points based on these measures of the growth cycle, and various approaches which confirm and review whether these turning points are appropriate for reference dates. And the dating rules are backed up by an administrative process to determine and disseminate these turning points as the reference dates of growth cycles in Korea. The process provides a strategy that gives authority to the released reference dates and minimises errorsin the dating. However, these dating rules have strict procedures to determine the reference date because the measures of a growth cycle are revised annually and their turning points could be affected by their revisions. Usually, a new reference date requires approximately three years before it is released officially. Due to the delayed dating strategy, the present and future business conditions need to be reviewed by detecting and forecasting models of the coming turning points with leading indexes and coincident indexes.

Measuring Chinese business cycles with dynamic factor models

The Stock–Watson method and the dynamic Markov switching factor (DMSF) model are employed to construct macroeconomic composite coincident indexes for the Chinese economy, January 1990–March 2008. Four coincident indicators, namely, industrial production, investment in fixed assets, sales revenues, and the money supply, M 1, are selected to compute the coincident index. Strong asymmetries are found with recent business cycles in China characterized by expansions of longer duration and smaller amplitude relative to the contraction stage. The two models produce similar composite index series, but the DMSF model shows frequent transitions that are difficult to interpret. A comparison of the composite coincident index and other measures of macroeconomic activity provides economic interpretations of the patterns in the index. There are notable differences between the index and GDP growth rates over this period, reflecting its more comprehensive measurement of economic activity. This more comprehensive view of macroeconomic activity increases understanding of changes in China's policies and economic fluctuations that are not shown by GDP growth rates alone.

Do Leading Indicators Lead Peaks More Than Troughs?

We develop a novel Markov switching vector autoregressive model to investigate the possibility that leading indicators have different lead times at business cycle peaks and at troughs. In this model, coincident and leading indicators share a common Markov state process, but their cycles are nonsynchronous, with the nonsynchronicity varying across regimes. An application shows that on average the Conference Board’s Composite Leading Index leads the Composite Coincident Index by nearly 1 year at peaks but by only 1 quarter at troughs. Allowing for asymmetric lead times yields improved real-time dating and forecasting of business cycle turning points.

Measuring and predicting turning points using a dynamic bi-factor model

In this paper a dynamic bi-factor model with Markov-switching is developed to measure and predict turning points. Both common factors, namely composite leading index (CLI) and composite coincident index (CCI) respectively, have their own cyclical dynamics, and their lead-lag relationships are reflected in the transition probabilities matrix. The model is applied to four coincident and four selected leading indicators for the US economy. The bi-factor model estimates that, on average, CLI leads CCI by 7–8 months at both peaks and troughs. The model-derived recession probabilities of CCI and those of CLI with a lag of 9 months capture the NBER business cycle chronology very well. The out-of-sample forecast using CLI successfully detected the latest recession from March to December 2001. This allows the measurement and prediction of turning points in a precise and timely fashion.

Forecasting Cyclical Turning Points by Means of a Probabilistic Approach: Some South African Evidence

In this paper the effectiveness of “transplanting” a forecasting method based on a probabilistic approach is assessed in the South African context. The method utilises leading indicators in regression models, with a dichotomous response variable which assumes values of 0 or 1 to indicate business cycle expansion or contraction. The study closely replicates the work of Nazmi (1993) on turning point prediction. The results indicate an ability of the model to accurately forecast business cycle turning points in the 1980s. In the 1990s, the model displays a diminished capacity to forecast the turning points with acceptable accuracy. Leading indicators, in the South African experience, show reliable leading relationships with the composite coincident index over the period between 1970 and 1980 and thereafter this relationship weakens, impacting negatively on their forecasting ability.

EXAMINING SIMILARITIES AMONG PHASES OF BUSINESS CYCLES : IS THE 1990S US EXPANSION SIMILAR TO THE 1960S'?

Statistical similarities among the latest long expansion in the U.S. and some other past expansions, in particular that of the 1960s, are examined. Corresponding to the definition of statistical similarity, a test based on the covariance matrices of business cycle component variables for the different expansions is proposed. Among available tests, the test based on partial common principal component analysis is argued to be most appropriate. The test is applied to the components of both GDP and the coincident composite index. As a result, the 1990s expansion is concluded to be statistically similar to that of the 1960s. Using the same method we also examine the statistical similarities of whole cycles (defined on a peak-tepeak basis).

A further test of the influence of leading indicators on the probability of US business cycle phase shifts

In earlier work Filardo (1994)used a variable transition probability Markov regime-switching model to investigate the usefulness of a number of leading indicators in anticipating phase changes in the business cycle. Filardo used Industrial Production as a proxy for the business cycle and found the leading indicators were not only statistically significant determinants of the transition probabilities but that they also substantially improved the dating of the business cycle. However, Industrial Production is a very narrow proxy for the business cycle and represents a relatively small and decreasing component of economic activity. Here a broader, more comprehensive proxy for the business cycle is employed to test the usefulness of leading indicators in forecasting the likelihood of future business cycle phase shifts. Specifically, the Economic Cycle Research Institute's (ECRI) coincident composite index is employed as a summative measure of the business cycle. Then, following Diebold et al. (1994), the transition probability parameters are allowed to vary. In particular, the ECRI leading and long leading indexes are used as putative determinants of these transition probabilities.

Leading Indicators for the Service Sector

Since the service sector currently accounts for a large and growing share of U.S. economic activity, we have undertaken to develop indicators that throw light on its current status and future prospects. A composite coincident index of aggregate economic activity in the service sector is constructed, and a growth-cycle chronology is identified based on growth rates in this index and in its components. Then several macroeconomic leading indicators of service are identified, and from them a composite leading index is constructed. The growth rate in this index is found to give advance warnings of major swings in the growth of the service sector.