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The author comments on a paper by author Tina Faulkner entitled "Using G2 to measure inequality in Brazil" that used the modified Gini index (G2). He discusses the important contribution of the paper to income inequality discussion in the country and he argues that the characteristics of the traditional Gini index should have been mentioned in the paper. He suggests that the Gini coefficient can be used to measure not only the income distribution but also the concentration of any distribution.

An important feature of the outbreak of systemic financial risk is that the linkage and contagion of risk amongst the various sub-markets of the financial system have increased significantly. In addition, research on the prediction of systemic financial risk plays a significant role in the sustainable development of the financial market. Therefore, this paper takes China's financial market as its research object, considers the risks co-activity among major financial sub-markets, and constructs a financial composite indicator of systemic stress (CISS) for China, describing its financial systemic stress based on 12 basic indicators selected from the money market, bond market, stock market, and foreign exchange market. Furthermore, drawing on the decomposition and integration technology in the TEI@I complex system research methodology, this paper introduces advanced variational mode decomposition (VMD) technology and extreme learning machine (ELM) algorithms, constructing the VMD-DE-ELM hybrid model to predict the systemic risk of China's financial market. According to eRMSE, eMAE, and eMAPE, the prediction model's multistep-ahead forecasting effect is evaluated. The empirical results show that the China's financial CISS constructed in this paper can effectively identify all kinds of risk events in the sample range. The results of a robustness test show that the overall trend of China's financial CISS and its ability to identify risk events are not affected by parameter selection and have good robustness. In addition, compared with the benchmark model, the VMD-DE-ELM hybrid model constructed in this paper shows superior predictive ability for systemic financial risk. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to provide an empirical assessment of the economic efficiency of grape-producing farms in Armenia. Upon reviewing various field-related studies the frontier analysis was singled out as a methodological base of this study. More specifically two-stage empirical analysis was performed, which includes the measurement of efficiency levels of grape farms by implementing the DEA technique and then assessing the determinants of obtained efficiency scores by performing Tobit modeling. To obtain necessary data, 365 grape farms from the Armavir region were surveyed. The main findings of this paper suggest that the average efficiency score for grape farms is 0.72, and there is room for improvement in the economic performance of farms with 28%. The main determinants of farm efficiency were cultivated grape varieties, farm size, and selling prices of grapes. The obtained results mainly support the findings of similar studies carried out for various viticulture regions across the world. This study provides some methodology bases for further expansion of similar studies both in terms of including the other Armenian viticulture regions and different years to explore the changes in the efficiency of grape farms over time. This article provides a base of knowledge for policymakers, scholars, researchers, investors, and credit companies for their decision-making processes and other purposes. [ABSTRACT FROM AUTHOR]

The unbalanced development strategy makes the regional development unbalanced. Therefore, in the development process, resources must be effectively utilized according to the level and characteristics of each region. Considering the resource and environmental constraints, this paper measures and analyzes China's green economic efficiency and green total factor productivity. Moreover, by expounding the characteristics of high-dimensional data, this paper points out the problems of traditional clustering algorithms in high-dimensional data clustering. This paper proposes a density peak clustering algorithm based on sampling and residual squares, which is suitable for high-dimensional large data sets. The algorithm finds abnormal points and boundary points by identifying halo points, and finally determines clusters. In addition, from the experimental comparison on the data set, it can be seen that the improved algorithm is better than the DPC algorithm in both time complexity and clustering results. Finally, this article analyzes data based on actual cases. The research results show that the method proposed in this paper is effective. [ABSTRACT FROM AUTHOR]

In recent years there has been much debate about the need to supplement existing economic indicators, most notably gross domestic product (GDP), with a more rounded and balanced set of indicators that better reflect the complexity of today's economic, societal and environmental needs. To address this need, the United Nations Conference for Trade and Development (UNCTAD) in cooperation with the Eurasian Economic Commission (EEC) developed a first prototype composite index measuring inclusive growth tailored to that region. This paper summarises the conceptual objectives and some of key methodological challenges and considerations faced in compiling such an index. Many of the challenges and methodological considerations are universal and not specific to EEC. A brief outline of results and future work is also detailed. [ABSTRACT FROM AUTHOR]

Almost 2 years have passed since the launching of the 2030 Sustainable Development Goals but no big strides have been made especially on data compilation to track and measure these goals. Uganda, like the rest of the world has to base its development agenda on SDGs. However, the major constraint often lies on measuring the indicators and even compilation of data. The author discusses 7 SDG economic indicators based on the Ugandan context. This paper considered a baseline year of 2014 for the economic indicators of the SDGs in Uganda. A comparative review of data was obtained from UNSD SDG indicator global database and available national data in Uganda to update the Uganda country page constructed by the ACS survey in 2017. Metadata analysis was invoked following a 4-assessment check. Results showed that a lot of data from administrative sources could be used to feed into the Ugandan page given that censuses and surveys are not regularly updated. However, the paper shows some discrepancies in the ACS survey report data. There is need to explore more of the administrative data in government ministries and agencies in order to close the data gaps for the economic indicators of the SDGs. [ABSTRACT FROM AUTHOR]

The paper presents a fundamental parametric approach to simultaneous forecasting of a vector of functionally dependent random variables. The motivation behind the proposed method is the following: each random variable at interest is forecasted by its own model and then adjusted in accordance with the functional link. The method incorporates the assumption that models' errors are independent or weekly dependent. Proposed adjustment is explicit and extremely easy-to-use. Not only does it allow adjusting point forecasts, but also it is possible to adjust the expected variance of errors, that is useful for computation of confidence intervals. Conducted thorough simulation and empirical testing confirms, that proposed method allows to achieve a steady decrease in the mean-squared forecast error for each of predicted variables. [ABSTRACT FROM AUTHOR]

Fluctuations of the economy between periods of expansion and contraction have been subject to business cycle analysis for many years. However, the understanding of different cycles, such as business, growth and acceleration cycles, and their representation in an intuitive way is not straightforward. Moreover, users are confronted with large sets of economic indicators without having synthetic information on the current evolution of the economy. In this paper, we introduce the Eurostat business cycle clock (BCC), a visualization tool designed to complement traditional economic statistics. We will present the full methodological framework underlying the clock, including the statistical detection of turning points, empirical dating and assessment. The clock is available for the euro area and its member countries, although our presentation will focus on the euro area aggregate. Some brief considerations on the impact of the current COVID-19 crisis on the clock are included. [ABSTRACT FROM AUTHOR]

Financial analytics has been highly crucial in forecasting possible future economic scenarios. The relationship between a country's macroeconomic indicators and its stock market has been extensively studied in the literature. Stock prices should be used as leading indications of future economic activity if they accurately reflect the underlying fundamentals. On the contrary, if economic activity follows stock price movement, the outcomes should be the opposite, i.e., economic activity should lead stock price movement. The paper attempts to make use of financial descriptive analytics to explore the interconnection between prominent macroeconomic indicators and stock market activity post ten years of financial crisis 2008. The study's range is constrained to explore the aforementioned interconnection for the period from September' 2008 to August' 2018. The following factors have been found to be related over the long term: GDP, Production Index, Inflation, Exchange Rate, Money Supply, Imports, Exports, FDI, and Stock Market Returns. Shockingly FII has not shown any cointegrating equation. Also causality was observed between stock market and economic indicators. Impulse Response Function (IRF) and Variance Decomposition (VDC) techniques of VAR model are applied to decompose or fractionalize the variability caused by macroeconomic indicators on the BSE Sensex returns which has given some interesting results. [ABSTRACT FROM AUTHOR]

With the introduction of the information age, enterprise financial management has been challenged as never before, and the application of Internet of Things (IoT) technology can effectively improve the efficiency of financial accounting management and realize the informationization of financial management. In order to solve the problem of enterprise financial accounting data processing, a data mining algorithm is constructed, which uses data mining technology to obtain massive information data and cluster analysis processing to realize the fusion of multiple uncertainty information processing models. Firstly, the financial information cloud platform is designed by using the IoT technology. The financial risk index coefficient of the enterprise is judged by the association rules. Finally, the research sample is divided into the risk group and the normal group according to the ST classification standard, and the 296 financial indicators of the two groups are correlated. The research results show that if the enterprise with a score below 40 points has financial risk, the accuracy rate is 70.9%, which is slightly lower than the financial risk warning model of the decision tree. Through the research of this paper, it has enlightenment to the financial accounting management of IoT enterprises. The data mining technology is applied in the processing of massive data information of accounting, which is moreefficient. [ABSTRACT FROM AUTHOR]

Central Bank Digital Currency (CBDC) pledges to realize a vast array of new functionalities, such as frictionless consumer payment and money-transfer systems, as well as precise supervision of money circulation, thereby enabling a number of new financial instruments and monetary policy levers. This study proposes, from a system feedback loop and cybernetics perspective, a Dynamic Issuance Mechanism (DIM) for CBDC that can theoretically enhance the vitality of economic operations. In accordance with this mechanism, the central bank implements dynamic issuance by monitoring cash leakage in real-time, so as to maintain the stability of the amount of money circulating on the market, thereby boosting the currency turnover rate and financial vitality. To demonstrate the efficacy of the DIM, we employ the Agent-Based Modeling (ABM) tool to develop a macroeconomic simulation model for qualitative analysis that includes four entities: Central Bank, households, firms, and commercial banks. The multi-cycle operation process of the model includes a variety of economic indicators demonstrating that DIM has the potential to boost economic vitality and social production efficiency without exerting an adverse effect on citizens' incomes, commodity prices, or the stability of the macroeconomic system. Finally, the function principle and potential risks of DIM are explained from a systems perspective, which offers a novel perspective for the functional design of CBDC and highlights that the hierarchical structure is a meaningful domain as the developmental direction. [ABSTRACT FROM AUTHOR]

The proposed paper presents the analysis, design, implementation and evaluation of an ultra-short-term frequency trading system for the foreign exchange market (FOREX), which features all stages of the trading process (Pre-Trade Analysis, Trend Forecasting, Trade Execution). The system uses artificial intelligence techniques in an environment that is constantly changing according to the decisions of the participating trading simulators. Our goal is to simulate the judgment and decision-making of the human expert (technical analyst or broker) in a closed world trading system that constantly adjusts exchange rates. We examine the system in terms of its contribution to how exchange rates are affected in an environment which solely consists of traders, without any exogenous factors, and show that our system can outperform all conventional technical indicators, thereby indicating that it could also play the role of facilitating the stabilization of a market by squeezing out non-intelligent traders. We designed and implemented a self-adjusting trading environment whose exchange rate price is only affected by trading within the environment (closed world assumption). We based our work on a modified series of technical indicator simulators, which are fed to an artificial neural network architecture, to eventually generate the trend forecasting signal and a series of customizable ultra-short term automated trading machines, which perform real-time virtual transactions based on the generated forecasting signals. A comparative analysis of the results is carried out to confirm that the proposed architecture outperforms traders based only on the conventional technical indicators while we also document the behavior of the system towards facilitating the attainment of an equilibrium. [ABSTRACT FROM AUTHOR]

This paper introduces a special issue of the Journal of Economic and Social Measurement on U.S. Regional Economic Indicator Models. It describes recent research on such models, both specifically and in the context of general research on economic indicators, and provides some references. The six papers of this special issue represent a selection from the papers presented at a conference held in Braga, Portugal at the Universidad do Minho 7-10 June 1998, a purpose of which was to bring together scholars from Europe, North America, and South America to consider issues in the construction of indicator models. [ABSTRACT FROM AUTHOR]

There is an increased pressure for national and international statistical offices to provide more up-to-date information for monitoring sustainable development and providing timely evidence for policy makers. The Esteban et al. paper ( I On Model-based Nowcasting for Highly Disaggregated Levels i ) is a very innovative approach in that it repurposes traditional or existing small area estimation prediction techniques, to which most NSOs have some familiarity, to temporal prediction without surrendering disaggregation - which immediately makes this approach of interest from a SDG perspective. [Extracted from the article]

In order to effectively guarantee the effect of credit risk prediction of science and technology finance and improve the ability of risk prediction, a credit risk prediction algorithm of science and technology finance based on cloud computing is proposed. The logistic regression model is used to predict, and the financial indicators of science and technology credit are selected as the model covariates. According to the characteristics and strong correlation of many financial indicators of science and technology credit, this paper constructs the final index system of online supply chain technology credit risk evaluation based on SMEs. Then the principal component analysis method is used to select the principal component. Combined with the penalty method, the data space dimension of financial indicators is further reduced, and the unrelated principal components are obtained. On this basis, a logistic regression model is established to predict the credit risk by taking the selected main components as covariates. The experimental results show that the algorithm has a good fit to the credit risk of 16 science and technology credit enterprises, and the risk prediction ability is significantly improved, which can effectively guarantee the effect of science and technology credit risk prediction. [ABSTRACT FROM AUTHOR]

The adoption of AI is an ongoing phenomenon in today's economy in all the industries. The purpose of this paper is to examine the economic impact of AI adoption in the region of ASEAN. To achieve this objective, structural questionnaire was developed for the various industry experts in targeted region. A sample of 240 experts was finally obtained over a time span of 6 weeks through online structural questionnaire approach. For measuring AI adoption, twelve items, initial economic impact (seven items), and subsequent economic impact (six items) were finally added in the questionnaire. For analyses purpose, descriptive statistics, structural equation modelling, and regression analyseswereapplied, examining the both initial and subsequent economic impact of AI adoption. Findings through structural model indicates that overall both initial and subsequent impact are significantly determined by AI adoption in related industries. Additionally, in depth analyses for the individual AI items as their initial and subsequent economic impact indicate that Usage of the data for AI adoption, clear strategy for AI adoption, successful mapping for AI adoption and overall positive attitude towards AI adoption have their significant and positive influence on initial economic indicators. Whereas, as per subsequent economic impact, factors like effective usage of data for AI adoption, assessing the right skills of individuals for AI adoption and positive attitude towards AI adoption are significantly impacting on material investment, capital investment, increasing unemployment, higher economic output, higher return on capital and higher wages for the existing labor. These findings have provided an outstanding evidence in the field of AI and its economic impact in the region of ASEAN and can be considered as initial contribution in related fields. Both industry exports and macroeconomic decision makers can significantly utilize the findings to develop their conceptual framework and understanding for the integration between AI adoption and economy. Additionally, this study can work as reasonable justification for implementing the more adoption of AI in various industries as it has positive economic outcome (both initial and subsequent). However, one of the key limitations of this study is limited sample size and only 240 industry exports were targeted from selected industries in ASEAN. Future study could be reimplemented on similar topic with expanding the sample size for better findings and more generalization. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach to the bank ranking process based on the possibilistic theory is proposed. Through this new method, the sensitivity of the results can be improved. Several methods are applied in order to rank the financial performance of Spanish Banks. Methods such as the Fuzzy Analytic Hierarchy Process (FAHP) and fuzzy TOPSIS are integrated in the proposed model. Criteria and sub-criteria weights are computed based on the judgments of experts using FAHP. These weights and financial indicators are inputs of the fuzzy TOPSIS methods for ranking the banks. The financial ratios are based on the CAMEL rating system criteria. Moreover, the results from the application of several distance measurements (Vertex, Hamming and Euclidean) in fuzzy TOPSIS as well as a new measure based on the possibilistic theory are compared. Finally, the results obtained applying fuzzy TOPSIS show that they vary depending on the separate measure, so it is necessary to have different measures to be able to correct decision making. [ABSTRACT FROM AUTHOR]

A number of Central Banks use a measure of core inflation as an operational guide and analytical tool for monetary policy. But what is core inflation and why is it considered useful? This paper considers the conceptual basis underlying alternative measures of core inflation and why they are considered effective indicators of the underlying inflationary pressure in the economy. Existing research evaluating the various measures against a range of ideal properties is reviewed. The paper also gauges the value of the core inflation measures against traditional standard statistical approaches using seasonal adjustment and the estimation of the underlying trend. [ABSTRACT FROM AUTHOR]

This paper presents a theoretical perpetual inventory model for measuring both the quantity and price sides of fixed capital flows and stocks. Because conventional models only measure the quantity side of fixed capital flows and stocks, it is difficult to fit the economic depreciation variable into the models. This paper provides a clear picture of how physical depreciation and economic depreciation are applied to the quantity side and price side of the model. The new model presented in this paper also generalizes the conventional model. In any case, the conventional model which uses the deflator of the gross fixed capital formation as the deflator of all fixed capital flows and stocks is questionable. [ABSTRACT FROM AUTHOR]

The Consumer Expenditure (CE) Survey data are a collection of detailed consumer expenditures, income, and consumer unit characteristics. The data set is a rich source of information available to researchers conducting economic analysis. The purpose of this paper is to provide an overview of the projects being undertaken at the Bureau of Labor Statistics in which these data are being utilized and to suggest directions for future research. The paper focuses primarily on the estimation of aggregate statistics, index numer reseach, econometric modeling, and survey research methodology. Topics addressed include: weighting, moving-weight price indexes, price indexes for demographic subgroups, interarea price indexes, tax and price indexes, demand analysis, statistical and econometric techniques, the econometrics of panel data, income and consumption distributions, cognitive research, composite estimation, non-normal distribution testing, and data imputation. Noted are changes in the survey methodology, which were introduced with the Continuing Consumer Expenditure Survey, and topics, within statistical methods, which focus on improving the measurement of CE variables. [ABSTRACT FROM AUTHOR]

In this paper we measure the relationship between messages in the social media and the stock market prices. First, we measure the correlation and association between the amount of stock related tweets and different financial indicators such as prices, returns and transaction volume. Then, we analyze the content of the messages and test whether the tweets generated during different trends of price change (up, down or steady) can be distinguished by automatic classifiers. Our corpus consist on messages related to nine IT companies and also their daily prices and volume during trading hours for over a period of three months. Two textual representations were used, bag of words and word embeddings. The tweets were automatically tagged using two thresholds to bin the changes in price. We have found a correlation between the amount of daily messages and the volume of financial transactions. We also found negative association (more specifically, what we define as local trend association) between tweet volume and financial indicators that were not found by using only the correlation analysis. Our main contribution is that the messages generated during a positive, negative and neutral trend can be distinguished by state of the art classifiers. [ABSTRACT FROM AUTHOR]

Poverty is a complex phenomenon that cannot be reduced solely to monetary dimension. This leads to the need for a multidimensional approach that consists in extending the analysis to a variety of non-monetary indicators of living conditions. The axiomatic literature has proposed some measures of multidimensional poverty and explored the properties that are at the basis of these indices. The aim of this paper is to use a Fuzzy Subset Theory perspective to propose a class of counting measures of multidimensional poverty and the assignment of different weights to the considered dimensions. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive discussion on the compilation and the dissemination of Greek external sector statistics. We compare them with international statistical standards and examine their potential limitations. Our statistical analysis indicates increased consistency of the various trade statistics and improved comparability over time. The empirical findings vindicate the wide use of Greek external sector statistics by academics and in economic policy. [ABSTRACT FROM AUTHOR]

Most of the major cities in the United Arab Emirates (UAE) have witnessed a rapid growth rate in population during the past two decades, which was accompanied by the development of major transportation infrastructure. This fast growth of the infrastructure and the pressure of constructing more infrastructures in the UAE cities did not allow the local authorities to develop a suitable Bridge Management System (BMS) to maintain the nation's valuable asset at acceptable levels of safety and serviceability. This paper aims at developing a conceptual framework for a BMS that recognizes the local conditions and needs of the UAE. To achieve this objective, the paper provides a review of the BMS around the world and a summary of the local practices and experiences in this area. In addition, a proposed framework for the development of a comprehensive bridge management system for UAE and discussion of the main components of the proposed BMS are presented. [ABSTRACT FROM AUTHOR]

In the European Union, tens of billions of euros are spent on regional policy every year. A major part of this amount is allocated on the basis of regional gross domestic product per capita. Because of the administrative use of this statistical estimate, the requirements for the quality of these figures are very strict. This quality is, however, not easy to specify by means of a confidence interval. What can be done to monitor the quality as a basis for improvement? In this paper an inventory is drawn up of recent work on the quality of regional accounts estimates. Special attention is paid to the instrument of process tables. In the last five years process tables have been developed for the national accounts (GNI Committee) as well as the regional accounts. The focus of the regional accounts process tables is on the treatment of multi-regional companies and the sources for top-down methods. The regional accounts should be compiled in close cooperation with the national accounts. The quality of the national accounts estimates and – indirectly – the regional accounts estimates, could be improved by the findings of the regional accounts compilation process. [ABSTRACT FROM AUTHOR]

Price indices serve many different purposes and their definition, coverage and construction depends on the precise use for which they are being constructed. Once constructed however their use is frequently extended to other areas. For example, a consumer price index may be designed as an inflation target, a measure of price stability or a compensation index, but may also be used as a deflator. Usually the original use will determine which particular agents the index covers, which range of goods and services are covered and the way in which prices are measured and the index constructed. However key economic concepts such as productivity and welfare are residuals derived by subtracting a series from a first deflated number. Inappropriate use of price indices will render such measures meaningless. This paper reviews the systems of price indices and the supporting frameworks which facilitate the process of identifying and defining in statistical terms user needs for price indices and making best use of all the prices collected. Whilst the paper considers price indices in general its focus is on consumer price indices. [ABSTRACT FROM AUTHOR]

This paper has two aims. First, it shows how the time dummy method to adjust for quality changes fits into the methodology of statistical agencies applying a geometric mean price index formula at the elementary aggregation level. Second, the paper argues that the ordinary or direct time dummy approach cannot cope with systematic price effects of new and disappearing products. Several 'indirect' alternatives are discussed in which the time dummy coefficient serves as a common adjustment factor and in which systematic effects of new or disappearing products are explicitly taken into account. Special attention is paid to the role of the sampling design, in particular proportional to expenditure sampling. [ABSTRACT FROM AUTHOR]

Through many comprehensive revisions of the National Income and Product Accounts (NIPA) of the US, a significant discrepancy has persisted, namely that between the estimate of the headline total now called Gross Domestic Product (GDP) from the side of expenditures or from the side of income payments. This discrepancy is not trivial (now in the neighborhood of -$100+ bn.), it is not random; it is wrongly attributed exclusively to the income side estimates. There have been interesting proposals for systematically allocating it among NIPA entries according to some statistical rule. Students of the discrepancy, over the years, have noted systematic variation with respect to international trade, inventory investment, total output and other variables. In recent years, the income side total has given a different estimate of the historical rate of change of output per worker, obviously an extremely important statistic. In a fresh examination of the discrepancy through 1996 (quarterly), we find, in this paper, suggestive correlation with business earnings, itself a very important but difficult magnitude to measure. If the NIPA data are to be used in an important way for policy guidance, a more careful treatment based on economic and statistical analysis is called for. [ABSTRACT FROM AUTHOR]

Option pricing plays an important role in modern finance. Interest rate is an important economic indicator and always influenced by some uncertain factors. It is necessary to consider the floating interest rate when we explore the option pricing. This paper introduces a new uncertain stock model with floating interest rate. In this new stock model, the pricing formulas for European, American and Asian options are derived. Furthermore, some numerical examples are given. [ABSTRACT FROM AUTHOR]

A major part of Open Data concerns statistics such as financial and social indicators. Accurate and reliable statistics provide the solid ground for performing analyses that support businesses and governments in understanding the world and making better decisions. More importantly, the combination of statistical figures coming from disparate sources can unveil unexpected and unexplored insights. The adoption of the Linked Data principles and technologies has promised to facilitate data integration at a Web scale. In this paper, we describe the development of tools that support the whole lifecycle of linked statistical data including creation, expansion, and exploitation. Our approach is based on actively engaging organizations handling statistics as part of their everyday activities. The final technological outcome is the OpenCube Toolkit, a software platform that includes a set of relevant tools. [ABSTRACT FROM AUTHOR]

The widely used PRODY indicator by Hausmann et al. [1] [What you Export Matters; Journal of Economic Growth: 12, 1–25] allows the calculation of product sophistication and factor intensities at high levels of disaggregation. In this paper I deal with its measurement, its conceptional limitations, its sensitivity to subjective decision making, its alternatives, and how to reap the best possible benefit from its usage. I introduce a theory-based alternative and investigate the sensitivity of empirical results with respect to different measurements. In particular, I re-estimate the main results of Hausmann et al. [1] with 75 variants of measurement. Small changes to the sample, the aggregation level, or the construction of the indicator can make a difference. Moreover, I offer two STATA programs that ease the calculation of various PRODY variants. [ABSTRACT FROM AUTHOR]

When studying the interaction of multi-state regions in the US, regional economists typically use the regional divisions developed by the Bureau of Economic Analysis (BEA). The BEA's grouping of the 50 states into eight regions is based primarily on cross-sectional similarities in the states' socioeconomic characteristics. Since many economic studies of regions concentrate on similarities and differences in regional business cycles, it is also appropriate to group states into regions based on some common cyclical behavior. This paper groups states into regions based on common movements in state indexes of economic activity. Comparable indexes are estimated for the 48 contiguous states, and cluster analysis is applied to the monthly changes in these indexes to group the states into regions with similar business cycles. The cluster analysis identifies six distinct regions consisting of contiguous states with similar monthly changes in their economic activity indexes. These regions differ in varying degrees from the commonly used BEA regions. [ABSTRACT FROM AUTHOR]

This paper explores the dynamic relationship between stock market fluctuations and the business cycle. Presumably, stock market movements reflect positions taken by market participants based on their assessment about the current state of the economy. Given the forward-looking behavior of stock market investors, this paper explores the possibility of predicting business cycle turning points using promptly available financial variables. Stock market fluctuations and business cycles are represented by nonlinear dynamic factors at the monthly frequency. The proposed model generates predictions of business cycle turning points using the business cycle factor, and anticipation of these predicted turns using the stock market factor. The findings indicate that the extracted stock market factor is a leading indicator of the state of the business cycle and can be used to anticipate turning points in real time. [ABSTRACT FROM AUTHOR]

Forecasts of real GNP are based on preliminary estimates of National Income and Product Accounts data which will subsequently be revised. The purpose of this paper is to estimate the decrease in forecast accuracy which results from the use of preliminary rather than revised data. It is found that forecasts of the level of real GNP are very sensitive to data revision, but forecasts of the rate of growth of real GNP are relatively impervious to data revision. Over the period 1986-1991, the period during which real GNP was reported in constant 1982 dollars, the variance of forecast errors of the level of real GNP was four times what it would have been if the finally revised data had been available at the time the forecasts were made. The error variance of forecasts of the rate of growth of real GNP was only about 5 percent greater using preliminary data. These empirical results for the 1986-1991 period are consistent with theoretically determined error bounds for forecasts from trend stationary and difference stationary models with autoregressive disturbances. [ABSTRACT FROM AUTHOR]

The article presents a paper on a proposed index of human development which is not only dependent on economic indicators. The paper presents two alternative indices of human development and their respective development rankings. The work of the United Nations Development Program (UNDP) is improved by not considering the methodological errors of the UNDP index. Indicators are improved by considering income differences between the literacy and life expectancy of males and females. Alternative indicators of human development such as respect for human rights and measures of environment quality are included in the index.

Official statistical agencies such as the Census Bureau and the Bureau of Labor Statistics collect enormous quantities of microdata in statistical surveys. These data are valuable for economic research and market and policy analysis. However, the data cannot be released to the public because of confidentiality commitments to individual respondents. These commitments, coupled with the strong research demand for microdata, have led the agencies to consider various proposals for releasing public use microdata. Most proposals for public use microdata call for the development of surrogate data that disguise the original data. Thus, they involve the addition of measurement errors to the data. In this paper, we examine disclosure issues and explore alternative masking methods for generating panels of useful economic microdata which can be released to researchers. While our analysis applies to all confidential microdata, applications using the Census Bureau's Longitudinal Research Data Base (LRD) are used for illustrative purposes throughout the discussion. [ABSTRACT FROM AUTHOR]

In this paper, we construct and compare various service price indexes of capital input, inventories, and financial working capital, using complete sets of variants of the Jorgensonian user cost formula. We demonstrate that variations in the construction of the series yield substantially different results. We also find that proper measures of service prices should incorporate a complete set of variables suggested by economic theory. In addition, Divisia indexes of financial working capital and its service price are superior to the direct aggregates. [ABSTRACT FROM AUTHOR]

It is shown in this paper that attention to data issues is able to shed additional light on a much studied market using data, which while not the best, is frequently the type of data available for study. The appropriate use of heating degree day data for such analysis is treated in detail. Regression diagnostics are used as tools for analysis. The examination of the interaction between data issues, the historical context of the data, and residual analysis can result in new and informative ways of specifying a relationship. Results in this study and in a similar study are also compared. [ABSTRACT FROM AUTHOR]

This paper evaluates whether information needed for the hedonic estimation of housing equations is missing from the annual housing survey. Information of such characteristics as dwelling unit size (ava), neighborhood characteristics, and public services availability are missing from the survey, for example. This article assesses the importance of these and other such omissions in the estimation of hedonic equations. However, another aspect of the paper is its consideration of how the housing survey can be supplemented by data from other sources, such as the Housing Allowance Demand Experiment. The paper evaluates the extent to which such supplemental data can be used together with the Housing Survey data to provide an integrated database. [ABSTRACT FROM AUTHOR]

The Database of Global Economic Indicators (DGEI) from the Federal Reserve Bank of Dallas aims to standardize and disseminate economic indicators for policy analysis and scholarly work on the role of globalization. Its main purpose is to offer a broad perspective on a number of global factors affecting the U.S. economy. DGEI indicators are based on a core sample of 40 countries with aggregates for the rest of the world (ex. the U.S.) and by level of development attainment and openness to trade. DGEI indicators currently include real GDP, industrial production (IP), Purchasing Managers Index (PMI), merchandise exports and imports, headline CPI, core CPI (ex. food and energy), PPI/WPI inflation, nominal and real exchange rates, and short-term interest rates. Here we describe our methodology to transform and combine different time series, for temporal and cross-country aggregation, and to highlight the importance of using representative data in international macroeconomics research. Our paper makes a related contribution to the literature by providing a formal assessment of conventional interpolation methods used to adjust the data frequency. A selection of the DGEI-derived global indicators - to be updated monthly - can be accessed at the following URL: http://www.dallasfed.org/institute/dgei/index.cfm. [ABSTRACT FROM AUTHOR]

One of the central issues in credit risk measurement and management is modeling and predicting correlated defaults. In this paper we introduce a novel model to investigate the relationship between correlated defaults of different industrial sectors and business cycles as well as the impacts of business cycles on modeling and predicting correlated defaults using the Probabilistic Boolean Network (PBN). The key idea of the PBN is to decompose a transition probability matrix describing correlated defaults of different sectors into several BN matrices which contain information about business cycles. An efficient estimation method based on an entropy approach is used to estimate the model parameters. Using real default data, we build a PBN for explaining the default structure and making reasonably good predictions of joint defaults in different sectors. [ABSTRACT FROM AUTHOR]

An introduction to the March 2013 issue of the "Journal of Economic and Social Measurement" is presented, which focuses on the economy of Greece.

Aim of this paper is to explore poverty patterns and differentials across Italian provinces for several objective dimensions of life-style deprivation according to a multidimensional and fuzzy approach. We propose a joint analysis of monetary and supplementary deprivation to point out the extent to which the two aspects of poverty overlap for the population concerned, to look into their potential background determinants and to sketch a territorial poverty profile. Since traditional direct estimators, based on ECHP data, cannot provide adequate precision due to smallness of domain-specific sub-sample, we test Rao-Yu models, as extension of Fay-Herriot estimator, to handle time-series data. In addition to a diversity of deficiencies found throughout Italy, empirical evidence clearly emphasize higher degrees of overlap in "poorer" southern provinces and lower degrees in "richer" northern ones, justifying the implementation of different approaches to poverty measurement to identify those areas which, more than others, need structural interventions. [ABSTRACT FROM AUTHOR]

Previous research has used short, 2-year panels constructed from matched CPS files to examine a number of topics. This paper shows how researchers can examine labor market transitions using March CPS data without matching, demonstrates that separation rates from these panels are comparable to those generated using the more traditional approach of comparing two points in time, and describes the adjustments required to ensure that the series are consistent over time. The main advantage to using unmatched data, compared with matched data, is the absence of attrition bias and an unbroken time series dating back to 1976. I provide two examples of how these short panels can be used to address issues that would normally require longitudinal data. [ABSTRACT FROM AUTHOR]

This paper considers the recent boom in IT-driven offshoring in India and examines the potential for this to become a major source of economic growth in the future. Currently there is much optimism about the benefits that the new trend can bring to India and other similarly placed countries, as growth in services is seen as more likely to deliver employment, income and export revenue increases than growth in the commodity producing sectors. But such optimism may be misplaced given the relatively small share of this sector in total output and employment, as well as the difficulties of sustaining growth in the area. Most importantly, focus on this potential should not allow the government to ignore jobless growth in other sectors. [ABSTRACT FROM AUTHOR]

The index number problem is known as that of decomposing an aggregate change into symmetric factors, the number of which usually is two. This paper discusses the generalization to more than two factors, reviews the proposals from the literature, and adds a simple solution. [ABSTRACT FROM AUTHOR]

This article deals with the interpretation of elementary and aggregate quantity and real value indices. Real value indices are defined as the product of quantity and real price indices. In national accounts, deflated (constant prices) values are measures of quantities and, consequently, their widespread interpretation as real values is criticized. Aggregate indices of quantities based on modern index number theory are measures that involve real prices and are interpreted here as real value indices. Their interpretation as "volume" indices is criticized. Real values of distinct commodities are always trivially additive similarly to nominal values while quantities of these commodities never are except under restrictive assumptions. The paper argues, furthermore, that additivity is a somewhat irrelevant issue in economics. Finally, real shares are shown to be equal to nominal shares and, accordingly, the habit of using constant prices shares is also criticized. [ABSTRACT FROM AUTHOR]

The paper considers the use of a Consumer Price Index (CPI) for three possible purposes: (1) as a Cost of Living Index (COLI); i.e., as a measure of the relative cost of achieving the standard of living when facing two different sets of prices for the same group of commodities; (2) as a consumption deflator; i.e., the price change component for a decomposition of a value ratio into price and quantity components and (3) as a measure of general inflation. The theoretical concepts suitable for the first two purposes are laid out and the problems involved in finding practical approximations to the unobservable theoretical constructs are discussed. The concept of a conditional cost of living index is also discussed; this type of index holds constant various environmental factors. The problems involved in aggregating over groups of consumers are also discussed. Finally, the differences between the harmonized index of consumer prices used in the European Union to measure general inflation and a COLI are discussed. [ABSTRACT FROM AUTHOR]