Financial variables as leading indicators in Greece

This paper presents business cycle stylized facts for the Greek economy and extends the relevant Greek literature in the following directions. First, the index of industrial production (IOP) is used to represent real economic activity and business cycle conditions. Second, the behavior of certain financial variables throughout the various phases of the business cycle is analyzed in order to assess their leading indicator properties. Third, possible non-linearities in these variables are investigated and tested for their relation to the business cycle states. The results imply that the most reliable leading indicators are real Treasury bill rates. Volatilities of real short-term interest rates may also contain useful predictive information for IOP volatility. Finally, mean non-linearities seem to be associated with business cycle asymmetries in the mean. The author wishes to express his gratitude to conference participants at the 55th International Atlantic Economic Conference held in Vienna, Austria, March 12–16, 2003, and an anonymous referee for their valuable comments and suggestions. The author is responsible for any shortcomings in the paper.     

Using precursors as leading indicators of technological change

Technological forecasters frequently utilize the procedure of “monitoring for precursors” in an attempt to anticipate technological changes. In a recent study on patterns of innovation, it was found that the data provided examples of precursors which could be used to illustrate the monitoring technique. In addition, it proved possible to extend the monitoring technique through the generation of probability distributions. The examples, and the extension, are reported here.     

Currency equivalent monetary aggregates as leading indicators of inflation

This study examines whether the Currency Equivalent (CE) Monetary Aggregates proposed by [Hutt, 1963] and [Rotemberg et al., 1995] can perform better in predicting inflation as compared to their simple sum counterparts. The components of four official measures of monetary constructs - M1, M2, M3 and L1 - are used to construct monthly CE monetary aggregates for the period from April 1993 to June 2009. The empirical evidence indicates that the growth rate of CE aggregates has an edge over their sum counterparts in predicting inflation. Moreover, the predictive power of the growth rates of CE aggregates improves as the level of aggregation increases. These evidences suggest that observing the movements in the growth rates of weighted monetary aggregates can be a better option within the “multiple indicator approach” which is being currently practiced by the Reserve Bank of India.     

Forecasting UK commercial real estate cycle phases with leading indicators: a probit approach

This paper examines the significance of widely used leading indicators of the UK economy for predicting the cyclical pattern of commercial real estate performance. The analysis uses monthly capital value data for UK industrials, offices and retail from the Investment Property Databank (IPD). Prospective economic indicators are drawn from three sources namely, the series used by the US Conference Board to construct their UK leading indicator and the series deployed by two private organisations, Lombard Street Research and NTC Research, to predict UK economic activity. We first identify turning points in the capital value series adopting techniques employed in the classical business cycle literature. Probit models are then estimated using the leading economic indicators as independent variables and forecast the probability of different phases of capital values, that is, periods of declining and rising capital values. The forecast performance of the models is tested and found to be satisfactory. The predictability of lasting directional changes in property performance represents a useful tool for real estate investment decision-making.     

A composite leading indicator of the inflation cycle for the Euro area

We evaluate the performance of composite leading indicators of turning points of inflation in the Euro area, constructed by combining the techniques of Fourier analysis and Kalman filters with the National Bureau of Economic Research methodology. In addition, the study compares the empirical performance of Euro Simple Sum and Divisia monetary aggregates and provides a tentative answer to the issue of whether or not the UK should join the Euro area. Our findings suggest that, first, the cyclical pattern of the different composite leading indicators very closely reflect that of the inflation cycle for the Euro area; second, the empirical performance of the Euro Divisia is better than its Simple Sum counterpart and third, the UK is better out of the Euro area.     

Noncompensatory/nonlinear composite indicators for ranking countries: a defensible setting

Composite indicators (or indexes) are very common in economic and business statistics for benchmarking the mutual and relative progress of countries in a variety of policy domains such as industrial competitiveness, sustainable development, globalization and innovation. The proliferation of the production of composite indicators by all the major international organizations is a clear symptom of their political importance and operational relevance in policy-making. As a consequence, improvements in the way these indicators are constructed and used seem to be a very important research issue from both the theoretical and operational points of view. This article aims at contributing to the improvement of the overall quality of composite indicators (or indexes) by looking at one of their technical weaknesses, that is, the aggregation convention used for their construction. For this aim, we build upon concepts coming from multi-criteria decision analysis, measurement theory and social choice. We start from the analysis of the axiomatic system underlying the mathematical modelling commonly used to construct composite indicators. Then a different methodological framework, based on noncompensatory/nonlinear aggregation rules, is developed. Main features of the proposed approach are: (i) the axiomatic system is made completely explicit and (ii) the sources of technical uncertainty and imprecise assessment are reduced to the minimum possible degree.     

Using composite indicators to evaluate the efficiency of health care system

This article produces a system-level composite indicator on population health in publicly provided primary health care. We first summarize information from various indicators of care by principal component analysis (PCA). We then regress the costs of care against these indicators to evaluate whether they are related. The existing health status indicator provides a point of comparison. Our results suggest that PCA can be used to extract information efficiently and thus to reduce the dimensionality of data. Furthermore, the use of the existing health status indicator to estimate cost-efficiency leads to invalid inference on overall efficiency, while the use of composite indicator significantly reduces the bias.     

Forecasting GDP all over the world using leading indicators based on comprehensive survey data

Comprehensive and international comparable leading indicators across countries and continents are rare. In this paper, we use a free and instantaneous available source of leading indicators, the ifo World Economic Survey (WES), to forecast growth of Gross Domestic Product (GDP) in 44 countries and three country aggregates separately. We come up with three major results. First, for more than three-fourths of the countries or country-aggregates in our sample, a model containing one of the major WES indicators produces on average lower forecast errors compared to a benchmark model. Second, the most important WES indicators are either the economic climate or the expectations on future economic development for the next six months. And third, adding the WES indicators of the main trading partners leads to a further increase in forecast accuracy in more than 50% of the countries. It seems therefore reasonable to incorporate economic signals from the domestic economy’s main trading partners.     

The mortgage spread as a predictor of real-time economic activity

We analyse the predictive content of the mortgage spread for US economic activity. We find that the spread contains predictive power for real GDP and industrial production. Furthermore, it outperforms the term spread and Gilchrist–Zakraj?ek credit spread in a real-time forecasting exercise. However, the predictive ability of the mortgage spread varies over time.     

A longitudinal examination of social capital as a predictor of depression

Depression is the world’s most common mental health disorder and its prevalence is higher among women and the poor. This article seeks to examine the effect that social capital has on mental health, in a longitudinal study. We used data from the Fragile Families and Child Wellbeing Study, which has followed a cohort of families born in large US cities to mostly minority, unmarried parents for over 15 years. Two measures of social capital are constructed, an index of social support and trust, and a measure of social participation. Data from four waves (totalling 8 years) of the study are analyzed in a panel logit model. After controlling for socioeconomic and demographic factors, the measure of social support and trust is found to be a significant predictor of depression, and remains so even after controlling for the effect of social participation. Our measure of social participation, based on attendance at religious services, does not appear to be a significant predictor of depression. Intervention and policy initiatives that increase social capital, particularly social support and trust, may be viable for improving depression among low-income urban, minority women.     

An exercise in composite indicators construction: Assessing the sustainability of Italian regions

This paper presents a piece of research aimed at evaluating the relative sustainability of the Italian Regions. After selecting a core set of indicators, for which we referred to the EU Sustainable Development Strategy, we built a composite index and checked for its robustness. As a result we got ‘many numbers’, that is, a range of possible rankings for Italian Regions.     

Eudaimonic happiness as a leading health indicator: cross-country European evidence

ABSTRACT

Eudaimonic happiness (measured in terms of sense of life) is a relatively unexplored subjective wellbeing indicator. The empirical findings presented in this paper show that it has a significant and quantitatively remarkable correlation with the future insurgence of some chronic diseases and the reduction of most functionalities in the ageing population. These results document that eudaimonic happiness is a relevant leading indicator of future health outcomes and expenditure and that its correlation is independent from that of the traditional life satisfaction measure.     

On the measurement of patent stock as knowledge indicators

Most of the conventional indicators for measuring the amount of technological knowledge (TK) have so far been input-based indicators. Hence, there is growing need to develop output-based indicators, and accordingly some studies have been conducted thereon. However, previous research has adopted patent count or patent stock by simple count in measuring the amount of TK as output-based indicators. The principal problem with using this variable is that the value of individual patent is too heterogeneous. That is a large portion of these patent databases are either of little value or nothing at all. As a result, patent count or patent stock by simple count cannot be seen as a suitable measure of TK.In this study, we attempted to resolve the value-heterogeneity problem in measuring patent stock. The notion of citation-based patent stock (CPS) and valuation-based patent stock (VPS) is proposed in this paper and the calculation method is described in detail. In CPS, the economic value of individual patent is assumed to be proportional to the number of citations received from other patents. And in VPS, the economic value of individual patent is derived from the value distribution of patents registered in some cohort by manipulating the patent renewal data. We validated the indicators by comparing them with the usual input-based indicators and by analyzing the relationships between them and the productivity growth empirically.     

Substitutability and weighting of ecological and economic indicators: Exploring the importance of various components of a synthetic index

The establishment of an information system is meaningful to develop a dynamic assessment of environmental quality and degradation. In this paper an original, empirical framework called Factor Weighting Model (FWM) is proposed to integrate different indicators into a composite index. The FWM is able to work with variables depicting different themes (e.g. climate, soil, landscape, demography, economy) collected at various geographical and temporal scales. Three case studies carried out at different spatial scales were considered as examples of FWM application to a composite index of Land Degradation. It consists of several variables collected from various data sources and available at different temporal and spatial scales, which are aggregated into some thematic indicators. The FWM approach was applied separately for each case study to the single variables transformed into sensitivity indicators through standard procedures. A multiway factor analysis (MDA) was carried out to explore over time the relationships among indicators. The importance of the environmental indicators was estimated by attributing a percent weight to each of them according to MDA outputs. Climate and soil dimensions account for the highest weights in all the cases considered. These findings are in accordance with the results obtained in previous studies. The implications of FWM in the assessment of environmental quality are finally discussed.     

Splines and the proportion of the seasonal period as a season index

In this paper a seasonal model is proposed to deal with heterogeneous seasonal patterns, in which neither the length of the seasonal period nor the magnitude of the seasonal effects remains the same over time. In these settings, there is a need for parsimony and flexibility. To this end, the seasonal effect at a season is defined as a function of the proportion of the length of the seasonal period elapsed up to this season, and the seasonal pattern is modelled by means of evolving splines. The methodology is illustrated for weekly Canary tomato exports.     

Occupational commitment, education, and experience as a predictor of intent to leave the nursing profession

This national Internet-based study tested occupational commitment in predicting RN intent to leave the nursing profession. Results showed that occupational commitment was increased by educational level and experience. Findings suggest opportunities for policy changes that may increase retention of registered nurses.     

Modelling country default risk as a latent variable: a multiple indicators multiple causes approach

We study the determinants of country default risk by applying a Multiple Indicators Multiple Causes (MIMIC) model. This accounts for the fact that country default risk is an unobservable variable. Whereas existing (regression-based) approaches typically use only one of several possible country default risk indicators as the dependent variable, the MIMIC model enables us to consider several indicators at once. The simultaneous consideration of sovereign yield spreads and Standard and Poor (S&P) ratings may help to improve the identification of the latent country default risk. Our results confirm most of the literature's main findings regarding important determinants of country default risk, refute others and provide new evidence to controversial questions.     

The shadow rate as a predictor of real activity and inflation: evidence from a data-rich environment

This article examines the predictive content of the shadow rates for US real activity and inflation in a data-rich environment. We find that the shadow rates contain substantial out-of-sample predictive power for inflation in nonzero lower bound and zero lower bound periods. In contrast, the shadow rates are uninformative about future real activity.     

“一带一路”国家的货币金融合作——基于修正的OCA指数模型分析

本文根据最优货币区理论建立OCA指数模型,对“一带一路”沿线区域内中国与各国的宏观和微观OCA指数进行测算,旨在探究中国与哪些国家具有良好的货币金融合作基础与最优的合作成本收益。研究发现,在东南亚区域,中国与马来西亚进行货币金融合作的成本收益最优;在东欧区域,波兰和克罗地亚是与中国开展金融货币合作潜力最大的国家。中亚和中东区域目前还不具备货币金融合作的条件与潜力。