Boosting diffusion indices

In forecasting and regression analysis, it is often necessary to select predictors from a large feasible set. When the predictors have no natural ordering, an exhaustive evaluation of all possible combinations of the predictors can be computationally costly. This paper considers ‘boosting’ as a methodology of selecting the predictors in factor‐augmented autoregressions. As some of the predictors are being estimated, we propose a stopping rule for boosting to prevent the model from being overfitted with estimated predictors. We also consider two ways of handling lags of variables: a componentwise approach and a block‐wise approach. The best forecasting method will necessarily depend on the data‐generating process. Simulations show that for each data type there is one form of boosting that performs quite well. When applied to four key economic variables, some form of boosting is found to outperform the standard factor‐augmented forecasts and is far superior to an autoregressive forecast. Copyright © 2009 John Wiley & Sons, Ltd.     

Seasonality in industry specific indices

This study examines seaonality in three industry specific stock market indices; the Standard and Poor's (S&P) 20 Transportation, the S&P 40 Utilities, and the S&P 40 Financial. The results support the existence of a weekend effect in the transportation index. There is no evidence of a weekend effect in the utilities and financial indices. Both the transportation and financial indices exhibit the January effect. The utilities index, however, shows no evidence of a January effect. The finding of a weekend and a January effect at the industry level is significant because select mutual funds enhance the ability of investors to capitalize on such anomalies.     

Ethnic polarization orderings and indices

An ethnic polarization index is a summary statistic of ethnic diversity in a population. Reynal-Querol (J Confl Resolut 46:29–54, 2002) suggested an index of ethnic polarization, the RQ index, and discussed its properties. In this paper we develop two ethnic polarization orderings that can rank ethnic distributions in terms of all ethnic polarization indices satisfying certain intuitively reasonable postulates. Some of these postulates and some additional ones taken from the earlier literature are employed to develop some axiomatic characterizations of the RQ index. In the process, a generalized form of the RQ index is also characterized.     

On the cointegration of international stock indices

This paper investigates the cointegration relationship among a group of international stock indices in light of new developments of econometric methods. Kasa (1992) first documented strong evidence for cointegration relations among five national stock indices, which suggests that there exists a common trend among those stock indices. Using Johansen multivariate cointegration test, we find that his findings are persistent in a sample of longer periods and more countries. In order to investigate whether these results are driven by statistical biases related to the sample size, we apply to our tests the Johansen’s small sample correction factor. The results still point toward the existence of a cointegration relationship but the evidence becomes much weaker. We next examine the empirical patterns emerged from different lag specifications and argue that Kasa’s findings are more likely due to the size distortion in extreme long lag VAR models. Indeed, when we employ a newly developed non-parametric test that does not require estimation VAR models, the null hypothesis of no cointegration cannot be rejected for the original sample of Kasa’s five-country stock indices from 1974 to 1990, nor for the extended period from 1970 to 2003.     

Parametric Value-at-Risk analysis: Evidence from stock indices

We evaluate the performance of several volatility models in estimating one-day-ahead Value-at-Risk (VaR) of seven stock market indices using a number of distributional assumptions. Because all returns series exhibit volatility clustering and long range memory, we examine GARCH-type models including fractionary integrated models under normal, Student-t and skewed Student-t distributions. Consistent with the idea that the accuracy of VaR estimates is sensitive to the adequacy of the volatility model used, we find that AR (1)-FIAPARCH (1,d,1) model, under a skewed Student-t distribution, outperforms all the models that we have considered including widely used ones such as GARCH (1,1) or HYGARCH (1,d,1). The superior performance of the skewed Student-t FIAPARCH model holds for all stock market indices, and for both long and short trading positions. Our findings can be explained by the fact that the skewed Student-t FIAPARCH model can jointly accounts for the salient features of financial time series: fat tails, asymmetry, volatility clustering and long memory. In the same vein, because it fails to account for most of these stylized facts, the RiskMetrics model provides the least accurate VaR estimation. Our results corroborate the calls for the use of more realistic assumptions in financial modeling.     

Multivariate volatility forecasts for stock market indices

Volatility forecasts aim to measure future risk and they are key inputs for financial analysis. In this study, we forecast the realized variance as an observable measure of volatility for several major international stock market indices and accounted for the different predictive information present in jump, continuous, and option-implied variance components. We allowed for volatility spillovers in different stock markets by using a multivariate modeling approach. We used heterogeneous autoregressive (HAR)-type models to obtain the forecasts. Based an out-of-sample forecast study, we show that: (i) including option-implied variances in the HAR model substantially improves the forecast accuracy, (ii) lasso-based lag selection methods do not outperform the parsimonious day-week-month lag structure of the HAR model, and (iii) cross-market spillover effects embedded in the multivariate HAR model have long-term forecasting power.     

The communion bridge to Six Sigma and process capability indices

Six Sigma has already become an efficient improvement technique adopted by a great number of enterprises. Numbers of Sigma has become a tool of measuring process capability in some enterprises. But some of enterprises still use process capability indices (PCIs) to measure the process capability. So numbers of Sigma and PCIs both can be used to measure the process capability. The paper will research the relationship between PCIs and numbers of Sigma. In bilateral specifications, the paper will research the relationship between the PCIs which are C_p, C_pk, C_pm and C_pmk, S_pk and numbers of Sigma. In unilateral specifications, the paper will research the relationship between the PCIs which are C_pu and C_pl and numbers of Sigma. If supplier and buyer use different tools to measure the process capability, then the communion bridge to Six Sigma and PCIs can decrease the communicate noise.     

Unification of some multivariate process capability indices for asymmetric specification region

In manufacturing industries, it is often seen that the bilateral specification limits corresponding to a particular quality characteristic are not symmetric with respect to the stipulated target. A unified superstructure of univariate process capability indices was specially designed for processes with asymmetric specification limits. However, as in most of the practical situations a process consists of a number of inter‐related quality characteristics, subsequently, a multivariate analogue of , which is called C_M(u,v), was developed. In the present paper, we study some properties of C_M(u,v) like threshold value and compatibility with the asymmetry in loss function. We also discuss estimation procedures for plug‐in estimators of some of the member indices of C_M(u,v). Finally, the superstructure is applied to a numerical example to supplement the theory developed in this article.     

Estimating global frontier shifts and global Malmquist indices

The Malmquist index is a measure of productivity changes, of which an important component is the frontier shift or technological change. Often technological change can be viewed as a global phenomenon, and therefore individual or local measures of technological changes are aggregated into an overall measure, traditionally using geometric means. In this paper we propose a way of calculating global Malmquist indices and global frontier shift indices which provides a better estimation of the true frontier shift and furthermore is easy to calculate. Using simulation studies we show how this method outperforms the traditional aggregation approach, especially for sparsely populated production possibility sets and for frontiers that also change shape over time. Furthermore, our global indices can be used for unbalanced panels without disregarding any information. Finally, we show how the global indices are meaningful for calculating differences between frontiers from different groups rather than different time periods as illustrated in a small case study of bank branches in different countries.      

Bayesian modification indices for IRT models

Bayesian modification indices are presented that provide information for the process of model evaluation and model modification. These indices can be used to investigate the improvement in a model if fixed parameters are re-specified as free parameters. The indices can be seen as a Bayesian analogue to the modification indices commonly used in a frequentist framework. The aim is to provide diagnostic information for multi-parameter models where the number of possible model violations and the related number of alternative models is too large to render estimation of each alternative practical. As an example, the method is applied to an item response theory (IRT) model, that is, to the two-parameter model. The method is used to investigate differential item functioning and violations of the assumption of local independence.     

Axiomatic properties of geo-logarithmic price indices

In the axiomatic approach to composite index numbers, a list of properties is given that both price and quantity indices should satisfy in order to ensure consistent comparisons. Usually, the price index is selected first and its cofactor is consequently adopted as the (implicit) quantity index. Unfortunately, even if the price index has good axiomatic properties, its cofactor need not, so the implicit quantity comparison may be axiomatically inconsistent. In this paper, we give a comprehensive study of a family of price indices sharing good axiomatic properties (proportionality, commensurability, and homogeneity) together with their cofactors. This family, called geo-logarithmic, is relevant also because of the empirical circumstance that all known price indices sharing such properties with their cofactors belong to it or can be obtained from geo-logarithmic index numbers through simple transformations. Thus, the geo-logarithmic family seems to play a central role when the joint consistency of price and quantity comparisons is concerned.     

Divisia price and quantity indices: 80 years after

This paper reviews and extends the theory of price and quantity indices which are defined as line integrals, the two types being those of Divisia and Montgomery. The properties of these indices are systematically explored, whereby in particular attention is paid to the path-(in)dependency issue. Two sections discuss the problem of how to approximate the line integral indices when only data at discrete points of time are available. It appears that every bilateral index can be conceived as the outcome of a line integral index over a particular curve in price–quantity space. The classical position is that chained bilateral indices are rationalized by Divisia indices. This rationalization is only interesting when the Divisia indices themselves possess a proper foundation in economic theory. The final sections are therefore devoted to the role played by the Divisia indices in micro-economic theory.     

Reducing revisions in hedonic house price indices by the use of nowcasts

National Statistical Institutes (NSIs) must balance between timeliness and accuracy of the indicators they publish. Because some of the house sales transactions are reported several months after they occur, many countries that include Israel, publish provisional house price indices (HPIs) that are subject to large revisions as further transactions are reported. This happens because the late-reported transactions behave differently from the transactions reported on time. In this paper, we propose a novel methodology to minimize the size of the revisions, with illustrations from Israel, but the method can be applied to other countries with appropriate modifications. The proposed methodology consists of nowcasting three types of variables at a subdistrict level and adding them as input data to an extended hedonic model used for the computation of the HPI: (1) the average characteristics of the late-reported transactions such as the average number of rooms and the area size of the sold apartments; (2) the average price of the late-reported transactions; and (3) the number of late-reported transactions. The three variables are nowcasted based on models fitted to data from previous months. Evaluation of our methodology shows more than 50% reduction in the magnitude of the revisions.     

Data envelopment analysis and Malmquist indices for measuring group performance

This paper develops measures, based on the Malmquist index, that enable the decision making units’ internal inefficiencies to be distinguished from those associated with their group (or program) characteristics. The applicability of these measures is illustrated with the assessment of bank branches’ performance. The analysis involves the construction of an index reflecting the relative performance of branches in four different regions, which can be decomposed into an index for the comparison of within-group efficiency spread, evaluating internal managerial efficiencies, and an index for the comparison of frontier productivity, reflecting the impact of environmental factors and regional managerial policies on branches’ productivity.     

Chain indices of the cost-of-living and the path-dependence problem: An empirical solution

This paper proposes a new method for estimating true cost-of-living (Konüs) indices, for large numbers of commodities, using data only on prices, aggregate budget shares and aggregate expenditure. Conventional chain indices are path-dependent unless income elasticities are (implausibly) all equal to 1. The method allows this difficulty to be overcome. I show that to estimate a Konüs index, only income and not price elasticities are required. The method is applied to estimate a Konüs price index for 70 products covering nearly all the UK's Retail Prices Index over 1974–2004, using the Quadratic Almost Ideal Demand System. The choice of base year for utility has a significant effect on the index.     

Predicting the portfolio risk of high-dimensional international stock indices with dynamic spatial dependence

The spatial dependence of assets, which relates to similarities in economic, political, or cultural systems and other aspects, has been confirmed through empirical research; however, spatial dependence has rarely been applied to financial risk measurement. To fill this gap in the literature, a dynamic spatial GARCH-copula (sGC) model is proposed in this paper to evaluate the portfolio risk of international stock indices. In this model, a spatial GARCH is used as the marginal distribution and vine copula is adopted as the joint distribution of indices. Then, the proposed model is applied empirically to assess portfolio risk. Results show that, first, the proposed risk prediction model with spatial dependence outperforms a model neglecting spatial effects per the Kupiec test, Z test and Christoffersen test. Risk prediction during periods of economic stability is also more accurate than during times of crisis. Second, risk measures for models with spatial dependence are higher than those without such dependence but lower than for vine copula models. Third, models including either spatial dependence or vine copulas alone exhibit relatively poor performance. Fourth, the model involving extreme value theory (EVT) generates the greatest value at risk to pass the Kupiec test, Z test and Christoffersen test; however, this model is not suitable for characterizing international indices with EVT based on negative values of the shape parameters of estimates. Findings offer important implications for personal investors, institutional investors, and national regulatory authorities.     

Between groups and total income inequality indices; U.S. 19601

The study contains a theoretical analysis on the behavior of a between groups Lorenz Index. The variables affecting this index are utilized to construct between groups indices for white-non-white; rural-urban; above 65 yr old-younger; population groups by state for 1960. The constructed indices were then used as explanatory variables for the state total Lorenz Index. It was found that they explained over 80 per cent of the variance. Adding other socio-economic variables enabled us to explain over 90 per cent of the variance of state Lorenz indices.     

The conditional dependence structure of insurance sector credit default swap indices

This study assesses the dependence structure of insurance sector credit default swap indices, using a copula-GARCH approach. We use daily data of the US, EU, and UK insurance sectors, covering the period from January 2004 to June 2013. We find substantial increases in dependence during the financial crisis periods. Prior to the crises, various copulas are found to best fit each pair; specifically, asymmetric tail dependence is found for the UK–US pair, suggesting the possibility of large simultaneous losses. However, during the crisis periods, the Frank copula fits best, with no significant tail dependence detected, implying low systemic risks.     

Dynamics of productivity in higher education: cross-european evidence based on bootstrapped Malmquist indices

This study examines patterns of productivity change in a large set of 266 public higher education institutions (HEIs) in 7 European countries across the time period 2001–2005. We adopt consistent bootstrap estimation procedures to obtain confidence intervals for Malmquist indices of HEI productivity and their components. Consequently, we are able to assess the statistical significance of changes in HEI productivity, efficiency and technology. Our results suggest that, assessed vis-à-vis a common ‘European’ frontier, HEI productivity rose on average by 4 % annually. Statistically significant changes in productivity were registered in 90 % of observations on the institutions in our sample, but statistically significant annual improvements in overall productivity took place in only 56 % of cases. There are considerable national differences, with German, Italian and Swiss HEIs performing better in terms of productivity change than HEIs from the other countries examined.