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The purpose of this paper is to find the reason which the results of grey variable weight clustering method do not correspond with the reality. It proposes reconstructing the whitenization weight function, outlining why and how inconsistency is avoided. The study aims to improve the model of grey clustering method based on the whitenization weight function and list the steps of the new clustering model so that analysis and application of innovation capacity in a broader range is normally found.

First the reason for the problem that the clustering results of grey variable weight clustering do not correspond with the reality is analyzed in two existing literature. And then a new whitenization weight function is reconstructed, two properties of the whitenization weight function are proved. The solution of the new grey variable weight clustering based on the whitenization weight function is built by following six steps.

The paper provides a new whitenization weight function which satisfies the normative and non-triplecrossing. It suggests that successful clustering results of innovation capacity act on two levels: integrating the elements of innovation capacity indexes, and following steps of grey variable weight clustering.

This paper improves the existing method of grey variable weight clustering and fulfills an identified need to study how cities’ innovation capacity can be clustered.

This paper presents a behavioral economics model with bounded rationality to describe an individual's food consumption choices that lead to weight gain and dieting. Using a physiological relationship determining calories needed to maintain weight, we simulate the food consumption choices of a representative female over a 30-year period. Results show an individual will periodically choose to diet, but that diet will reduce weight only temporarily. Recurrence of weight gain leads to cyclical dieting, which reduces the trend rate of weight increase. Dieting frequency is shown to depend on decision period length, dieting costs, and habit persistence.

Purpose – Accounting research has long shown the effect of subjectivity in performance evaluation. This study investigates one form of subjectivity in performance evaluation: flexibility in weighting performance measures examining decisions made by supervisors about weighting. Empirical studies show that the performance-measure weights are only partially consistent with the predictions of the agency theory and they are a still outstanding issue.

Methodology/approach – We develop an experiment to analyse supervisor decision-making, manipulating two factors: internal organisational interdependence and the level of managerial performance. We derive hypotheses along with both economic and behavioural approaches. The economic approach is based on agency theory predictions and the controllability principle while the behavioural approach is drawn on the organisational justice theory. We argue that in low interdependence contexts the supervisor's decision confirms the agency theory predictions, while in high interdependence conditions weighting decisions could be driven by behavioural considerations of fairness perceptions of the evaluation process and the level of managerial performance.

Findings – We find that in low interdependence contexts the supervisor's decision confirms the agency theory predictions, while in high interdependence contexts it does not. The results indicate that the supervisor's decision stems from the integration of economic and behavioural perspectives.

Research and social implications – The theoretical framework can be useful for interpreting the supervisor decision-making and the weighting process.

Originality – The economic and behavioural approaches allow us to understand flexibility in weighting performance measures suggesting that, in addition to economic considerations, a behavioural perspective may also be relevant in explaining subjective weighting.

The recent development of the European debt sovereign crisis showed that sovereign debt is not “risk free.” The traditional index bond management used during the last two decades such as the market-capitalization weighting scheme has been severely called into question. In order to overcome these drawbacks, alternative weighting schemes have recently prompted attention, both from academic researchers and from market practitioners. One of the key developments was the introduction of passive funds using economic fundamental indicators.

In this chapter, the authors introduced models with economic drivers with an aim of investigating whether the fundamental approaches outperformed the other models on risk-adjusted returns and on other terms.

The authors did this by constructing five portfolios composed of the Eurozone sovereigns bonds. The models are the Market-Capitalization RP, GDP model RP, Ratings RP model, Fundamental-Ranking RP, and Fundamental-Weighted RP models. These models were created exclusively for this chapter. Both Fundamental models are using a range of 10 country fundamentals. A variation from other studies is that this dissertation applied the risk parity concept which is an allocation technique that aims to equalize risk across different assets. This concept has been applied by assuming the credit default swap as proxy for sovereign credit risk. The models were run using the Generalized Reduced Gradient (GRG) method as the optimization model, together with the Lagrange Multipliers as techniques and the Karush–Kuhn–Tucker conditions. This led to the comparison of all the models mentioned above in terms of performance, risk-adjusted returns, concentration, and weighted average ratings.

By analyzing the whole period between 2006 and 2014, it was found that both the fundamental models gave very appealing results in terms of risk-adjusted returns. The best results were returned by the Fundamental-Ranking RP model followed by the Fundamental-Weighting RP model. However, better results for the mixed performance and risk-adjusted returns were achieved on a yearly basis and when sub-dividing the whole period in three equal periods. Moreover, the authors concluded that over the long term, the fundamental bond indexing triumphed over the other approaches by offering superior return and risk characteristics. Thus, one can use the fundamental indexation as an alternative to other traditional models.

We take Cumulative Prospect Theory (CPT) seriously by rigorously estimating structural models using the full set of CPT parameters. Much of the literature only estimates a subset of CPT parameters, or more simply assumes CPT parameter values from prior studies. Our data are from laboratory experiments with undergraduate students and MBA students facing substantial real incentives and losses. We also estimate structural models from Expected Utility Theory (EUT), Dual Theory (DT), Rank-Dependent Utility (RDU), and Disappointment Aversion (DA) for comparison. Our major finding is that a majority of individuals in our sample locally asset integrate. That is, they see a loss frame for what it is, a frame, and behave as if they evaluate the net payment rather than the gross loss when one is presented to them. This finding is devastating to the direct application of CPT to these data for those subjects. Support for CPT is greater when losses are covered out of an earned endowment rather than house money, but RDU is still the best single characterization of individual and pooled choices. Defenders of the CPT model claim, correctly, that the CPT model exists “because the data says it should.” In other words, the CPT model was borne from a wide range of stylized facts culled from parts of the cognitive psychology literature. If one is to take the CPT model seriously and rigorously then it needs to do a much better job of explaining the data than we see here.

We address an interesting case – the predictability of excess US asset returns from macroeconomic factors within a flexible regime-switching VAR framework – in which the presence of regimes may lead to superior forecasting performance from forecast combinations. After documenting that forecast combinations provide gains in predictive accuracy and that these gains are statistically significant, we show that forecast combinations may substantially improve portfolio selection. We find that the best-performing forecast combinations are those that either avoid estimating the pooling weights or that minimize the need for estimation. In practice, we report that the best-performing combination schemes are based on the principle of relative past forecasting performance. The economic gains from combining forecasts in portfolio management applications appear to be large, stable over time, and robust to the introduction of realistic transaction costs.

As a Data Envelopment Analysis (DEA) extension tool, cross-evaluation method was developed to evaluate Decision Making Units’ (DMUs) performances in a competitive situation with limited demand. It identifies DMUs with best performances and rank them by applying peer evaluation mode instead of self-evaluation mode. However, it has limitations in efficiency improvement. That is, it fails to give direct information on how to improve efficiencies of the inefficient DMUs. In this chapter, we propose an alternative way to apply cross-evaluation in efficiency improvement. First, an appropriate and feasible suggestion is proposed to minimize the variation between the weights of a DMU's own optimal Charnes-Cooper-Rhodes (CCR) efficiency and the weights guaranteeing its cross-efficiency score. We exploit several transformations to convert nonlinear programming into a linear one. As a result, an overall optimal set of the weights is obtained, which precisely illustrate the preferences of decision makers and exact characteristics of production process of the evaluated DMU. A further discussion is advanced to examine the existence of non-uniqueness of the weights and to differentiate various sets of the optimal weights by suggesting a unique feasible set of multipliers to best represent the alternative weights selection criterion. Moreover, we develop several models to reallocate the inputs and outputs of inefficient DMUs with minimum amelioration as well as consideration of the preference of decision makers. Finally, we apply our models to evaluate competitive advantages of Chinese cities.

If rational individuals pay the full costs of their decisions about food intake and exercise, economists, policy makers, and public health officials should treat the obesity epidemic as a matter of indifference. In this paper, we show that, as long as insurance premiums are not risk rated for obesity, health insurance coverage systematically shields those covered from the full costs of physical inactivity and overeating. Since the obese consume significantly more medical resources than the non-obese, but pay the same health insurance premiums, they impose a negative externality on normal weight individuals in their insurance pool.

To estimate the size of this externality, we develop a model of weight loss and health insurance under two regimes – (1) underwriting on weight is allowed and (2) underwriting on weight is not allowed. We show that under regime (1), there is no obesity externality. Under regime (2), where there is an obesity externality, all plan participants face inefficient incentives to undertake unpleasant dieting and exercise. These reduced incentives lead to inefficient increases in bodyweight, and reduced social welfare.

Using data on medical expenditures and bodyweight from the National Health and Interview Survey and the Medical Expenditure Panel Survey, we estimate that, in a health plan with a coinsurance rate of 17.5%, the obesity externality imposes a welfare cost of about $150 per capita. Our results also indicate that the welfare loss can be reduced by technological change that lowers the pecuniary and non-pecuniary costs of losing weight, and also by increasing the coinsurance rate.

The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.

Forecasts from dynamic factor models potentially benefit from refining the data set by eliminating uninformative series. This paper proposes to use prediction weights as provided by the factor model itself for this purpose. Monte Carlo simulations and an empirical application to short-term forecasts of euro area, German, and French GDP growth from unbalanced monthly data suggest that both prediction weights and least angle regressions result in improved nowcasts. Overall, prediction weights provide yet more robust results.