The theory of rational random behavior and its application to demand analysis

The author's theory of rational random behavior is developed, which distinguishes between the planning and the implementation stage of the decision-maker. Under the assumption of a quadratic loss function and decision variables with an unrestricted range, that theory implies normally distributed disturbances of behavioral equations with a covariance matrix that is known up to a scalar multiple. Those results have to be extended asymptotically by Barbosa for restricted decision variables and a more general loss function. The approach is applied to both the Rotterdam and the linear expenditure demand models: using U.S. postwar data for the former, the author finds that a likelihood ratio test accepts the theory at the 10 percent significance level.     

NONPARAMETRIC MODELS OF FINANCIAL LEVERAGE DECISIONS

This paper applies nonparametric decision tree models to the analysis of financial leverage decisions. This approach presents three appealing features: (i) the relationship between leverage and explanatory variables is not predetermined but is derived from information provided by the data, (ii) the models respect the fractional nature of leverage ratios, and (iii) each covariate is allowed to influence in different ways the financial leverage decisions of firms automatically assigned to different groups. Based on a data set of Portuguese firms, decision trees are used to tackle both classification (the decision to issue debt) and regression (the decision on the amount of debt to be issued, conditional on using debt) problems. It is found that: (i) two‐part models are the most appropriate specification for explaining the overall amount of debt used by firms, (ii) there are no drastic differences between the results produced by tree and parametric models, although some divergences may arise, and (iii) tree models suggest relationships between covariates and leverage that parametric models fail to capture, especially when the sample size is small.     

Evaluating the performance of a predictive modeling approach to identifying members at high-risk of hospitalization

Abstract

Aims: To evaluate the risk-of-hospitalization (ROH) models developed at Blue Cross Blue Shield of Louisiana (BCBSLA) and compare this approach to the DxCG risk-score algorithms utilized by many health plans.

Materials and Methods: Time zero for this study was December 31, 2016. BCBSLA members were eligible for study inclusion if they were fully insured; aged 80?years or younger; and had continuous enrollment starting on or before June 1, 2016, through time zero. Up to 2?years of historical claims data from time zero per patient was included for model development. Members were excluded if they had cancer, renal failure, or were admitted for hospice. The Blue Cross ROH models were developed using (1) regularized logistic regression and (2) random decision forests (a tree ensemble learning classification method). All models were generated using Scikit-learn: Machine Learning in Python. Prognostic capabilities of DxCG risk-score algorithms were compared to those of the Blue Cross models.

Results: When stratifying by the top 0.1% of members with the highest ROH, the Blue Cross logistic regression model had the highest area under the receiving operator characteristics curve (0.862) based on the result of 10-fold cross-validation. The Blue Cross random decision forests model had the highest positive predictive value (49.0%) and positive likelihood ratio (61.4), but sensitivity, specificity, negative predictive values, and negative likelihood ratios were similar across all four models.

Limitations: The Blue Cross ROH models were developed and evaluated using BCBSLA data, and predictive power may fluctuate if applied to other databases.

Conclusions: The predictability of the Blue Cross models show how member-specific, regional data can be used to accurately identify patients with a high ROH, which may allow healthcare workers to intervene earlier and subsequently reduce the healthcare burden for patients and providers.     

NONPARAMETRIC LIKELIHOOD: EFFICIENCY AND ROBUSTNESS*

Nonparametric likelihood is a natural generalization of parametric likelihood and it offers effective methods for analysing economic models with nonparametric components. This is of great interest, since econometric theory rarely suggests a parametric form of the probability law of data. Being a nonparametric method, nonparametric likelihood is robust to misspecification. At the same time, it often achieves good properties that are analogous to those of parametric likelihood. This paper explores various applications of nonparametric likelihood, with some emphasis on the analysis of biased samples and data combination problems.     

Intra-industry trade and business cycles in ASEAN

The innovation adoption literature has focused primarily on a producer's decision of whether and how much to adopt. An equally pertinent question is when to adopt, because in the case of new technologies it often ‘pays to wait’ for more information. We propose a double-limit hurdle model to analyse adoption intensity and inertia in the context of a divisible technology. The proposed framework incorporates probit or Tobit models as testable special cases. A maximum likelihood estimation framework is set out and generalized to account for heteroscedastic errors. The empirical analysis, which uses household-level data from India's semi-arid tropics, provides new insights into the factors influencing adoption inertia and intensity.     

The Hazards of Starting and Quitting Smoking: Some Australian Evidence

The empirical analysis employs individual level data from the Australian Health Survey combined with retrospective data on tobacco price matched to the age at which the individual started and quit smoking. Split-population hazard models are estimated for both starting and quitting smoking. The analysis suggests price plays a significant role in the decision to start smoking but not in the decision to quit. Further sensitivity analysis of different age groups and an alternative data source, questions the robustness of the significant role of price in the smoking initiation decision. From a policy perspective, the results indicate that increases in tobacco taxation can be an important instrument in reducing the incidence of smoking, but should be combined with other mechanisms such as mandating smoke-free environments and antismoking education. Our results strongly support the targeting of antismoking campaigns towards teenagers.     

Of maps and territories : The use and abuse of socioeconomic modeling in support of decision making

While the need for better data and models to support environmental decision making is generally recognized, the need for new approaches to how those data and models are used in the policy-making process has received less attention. Yet the relationship between analysis and policy is often characterized by problems of misunderstanding and mistrust between analysts and decision makers. The purpose of this paper is to examine the role of socioeconomic models in forecasting and decision making about environmental problems, and to suggest ways in which such models can be developed and used so as to increase the chance of their playing not only a scientifically but also a politically useful and desirable role.     

完善我国预算决策与管理制度的模式选择

预算决策与管理制度目标模式取决于财政制度模式。不同的财政制度模式下,预算决策与管理制度模式是不同的。而我国财政改革的目标模式,是建立与社会主义市场经济体制相适应的公共财政制度,因此,完善我国预算决策与管理制度的目标模式就是建立我国公共财政下的公共预算制度。     

Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models

The restaurant industry has been facing tough challenges because of the recent economic turmoil. Although different industries face different levels of competition and therefore the likelihood of financial distress can differ for firms in different industries, scant attention has been paid to predicting restaurant financial distress. The primary objective of this paper is to examine the key financial distress factors for publicly traded U.S. restaurants for the period from 1988 to 2010 using decision trees (DT) and AdaBoosted decision trees. The AdaBoosted DT model for the entire dataset revealed that financially distressed restaurants relied more heavily on debt; and showed lower rates of increase of assets, lower net profit margins, and lower current ratios than non-distressed restaurants. A larger proportion of debt in the capital structure ruined restaurants' financial structure and the inability to pay their drastically increased debt exposed restaurants to financial distress. Additionally, a lack of capital efficiency increased the possibility of financial distress. We recommend the use of the AdaBoosted DT model as an early warning system for restaurant distress prediction because the AdaBoosted DT model demonstrated the best prediction performance with the smallest error in overall and type I error rates. The results of two subset models for full-service and limited-service restaurants indicated that the segments had slightly different financial risk factors.     

Recursive robust estimation and control without commitment

In a Markov decision problem with hidden state variables, a posterior distribution serves as a state variable and Bayes’ law under an approximating model gives its law of motion. A decision maker expresses fear that his model is misspecified by surrounding it with a set of alternatives that are nearby when measured by their expected log likelihood ratios (entropies). Martingales represent alternative models. A decision maker constructs a sequence of robust decision rules by pretending that a sequence of minimizing players choose increments to martingales and distortions to the prior over the hidden state. A risk sensitivity operator induces robustness to perturbations of the approximating model conditioned on the hidden state. Another risk sensitivity operator induces robustness to the prior distribution over the hidden state. We use these operators to extend the approach of Hansen and Sargent [Discounted linear exponential quadratic Gaussian control, IEEE Trans. Automat. Control 40(5) (1995) 968-971] to problems that contain hidden states.     

Is individual environmental consciousness one of the determinants in transport mode choice?

This article models negative impact on the environment as one of the attributes of transport mode. By this modelling, we are able to examine whether individual environmental consciousness of this impact plays a significant role in his/her choice of transport mode. A survey data from Saito and Onohara Area in Northern Osaka of Japan is used to estimate the model with the Heteroscedastic Extreme Value specification. Both of the estimated and simulated results imply that individual environmental consciousness does influence his/her decision on transport mode choice in the sample. Furthermore, the likelihood ratio tests indicate that both the utility and scale parameters are not equivalent across sub-samples of university commuters, research-facility commuters and residents. The results of the comparison across sub-samples suggest that sometimes we may learn more from sub-dividing a whole sample into several sub-samples if we could distinguish them by their characteristics.     

Cost structure,technological change,and productivity growth in the greek banking sector

This paper first empirically investigates the cost structure of the Greek banking sector. Secondly, it provides measures of economies (diseconomies) of scale and quantifies technical change and its sources. Finally, this paper measures total factor productivity growth and identifies its sources. Bank production is presented with two different approaches (the intermediation and the production approach) which are used to specify a translog cost function. The two different translog cost models are estimated through the full information maximum likelihood method of estimation on pooled time series and cross sectional data. The results obtained are not significantly affected by model specification. Both models indicate significant economies of scale and negative annual rates of growth in technical change and in total factor productivity.     

A two-part model of treatment for benign prostatic hyperplasia and the impact of innovation

Benign prostatic hyperplasia (BPHs) affects more than half of men who are at least 60 years old. Transurethral resection of the prostate (TURP) is the most common treatment. This article estimates the average costs of providing a TURP to a patient that chooses the procedure and the expected cost of a TURP irrespective of the actual treatment decision. It utilizes a modified two-part model. It first estimates the likelihood of receiving the TURP given a BPH diagnosis using a logit regression. The second step is to estimate the (log of) TURP cost for the population that actually received the procedure. For the TURP decision, data were extracted from the National Health Interview Survey (NHIS) 1986–1994 on 502 men with a diagnosis of BPH. For the cost model, HCIA discharge abstract data were used from five states on 26640 men who actually received a TURP in 1994. TURP was identified via CPT codes in the NHIS data. The cost of TURP was constructed using patient hospital charges and hospital-specific cost to charge ratios. It was found that patient characteristics and comorbidities are important determinants of the decision to receive, and costs of, TURP. Pharmacological alternatives to TURP significantly affect the likelihood that a person will choose the procedure. The predicted unconditional cost of TURP ranged from US$8908 to US$3832 (by state); the predicted conditional cost of TURP ranged from US$1163 to US$750. The results suggest that the cost associated with providing a TURP for the average man with BPH would be US$492 to US$1163, depending on the state of residence. It was found that estimating the costs using simple regression or ANOVA techniques will lead to biased results due to sample selection (which in this case range from US$3832 to US$8908).     

Export market participation with sunk costs and firm heterogeneity

In this article we investigate the importance of sunk costs, firm characteristics and spillovers from nearby exporters on a firm's decision to participate in exporting. The empirical analysis involves the estimation of a nonstructural, discrete choice, dynamic model with firm heterogeneity. By using panel data for Estonian companies from 1994 to 1999 we find that: (i) both sunk costs and observable firm characteristics are important determinants of export market participation; (ii) previous history matters, in that, if a firm has been exporting the previous period or the period before, it significantly increases the likelihood of the firm exporting in the current period; (iii) larger firms with high capital intensity and foreign ownership are more likely to be exporters; (iv) operating in an export-oriented industry increases a firm's likelihood of exporting.     

The dynamics of responder behavior in ultimatum games: a meta-study

Using data aggregated from seven papers that study repeated play in standard ultimatum games with either stranger or absolute stranger matching, we show that the behavior of responders changes with experience. High offers are more likely to be accepted with experience and low offers are more likely to be rejected. At the individual level, there is a negative relationship between the likelihood that a given offer is accepted and the size of the preceding offer. We compare the results with predictions generated by static models of distributional preferences, implicitly dynamic models of preferences with reciprocity, and explicitly dynamic models of adaptive learning. The data is most consistent with models of preferences with reciprocity.     

Intersectoral wage linkages: the case of Sweden

The purpose of this study is to investigate whether wage-setting in certain sectors of the Swedish economy affects wage-setting in other sectors. The theoretical background is the Scandinavian model of inflation, which states that wage-setting in the sectors exposed to international competition lead wage-setting in the sheltered sectors of the economy. The Johansen maximum likelihood cointegration approach is applied to quarterly data on Swedish sector wages for the period 1980:1–2002:2. Different vector error correction (VEC) models are created, based on assumptions as to which sectors are exposed to international competition and which are not. Granger causality tests are then carried out in the different restricted/unrestricted VEC models to test for sector wage leadership. The Granger causality tests provide strong evidence for the presence of intersectoral wage causality, but no evidence of a wage-leading role for the internationally exposed manufacturing sector.      

Product classification to support approximate life-cycle assessment of design concepts

Many companies are beginning to change the way they develop products due to increasing awareness of sustainable development. Designers, who play a key role in product development, are being asked to incorporate environmental criteria into the design process. The need for analytically based conceptual design methods for integrated life-cycle assessment (LCA) has motivated the development of an approximate life-cycle assessment concept based upon learning algorithms. Although preliminary tests on general approximate models showed promise, it was observed that grouping products to create specialized learning surrogate LCA models for different classes of products might further improve results. This paper presents work to develop an automated classification system to support the specialization of surrogate LCA models for different groups of products. Hierarchical clustering is used to guide a systematic identification of product groups based upon environmental categories. These groupings are then used to create automated classification schemes using the C4.5 decision tree algorithm. Although further data are needed to induce good generalization performance, resulting product classification systems are considered to be a viable approach to support specialized learning surrogate LCA models for different classes of products.     

Spillovers and absorptive capacity in the decision to innovate of Spanish firms: the role of human capital

This paper investigates whether the existence of knowledge spillovers and the capacity of firms to assimilate them, which we relate with R&D intensity and some human resource management practices, are associated with the decision to innovate of Spanish firms. In order to do this, we employ data from the ‘Central de Balances’ database, which covers both manufacturing and services firms during the period 2003–2007, and use an estimator proposed by Wooldridge [2005. Simple solutions to the initial conditions problem in dynamic nonlinear panel data models with unobserved heterogeneity. Journal of Applied Econometrics 20, no. 1: 39–54] for dynamic random effects discrete choice models. The empirical exercise provides evidence on the positive link between spillovers and the innovative behaviour of companies, not just for the knowledge generated in the same industry, but also for that generated in the same region or by the public sector. Moreover, this link is stronger for those firms with a higher capacity to absorb those spillovers. This ability not only works through firms’ R&D capabilities, but also through factors such as the quality of the labour force, the share of temporary employment and the amount of resources spent in training. In addition to these factors, we find that innovation performance exhibits a high degree of inertia. Further, some other observed firm characteristics, such as size, sales growth, export behaviour, sector capital intensity or financial structure variables, are also found to be relevant determinants of the likelihood of innovation.     

Two-stage adoption of different types of pollution prevention (P2) activities

Pollution prevention (P2) remains the key pollution reduction strategy in the US despite its limited success in improving environmental performance. To aid the targeting of policies to promote the types of P2 that achieve environmental goals, this study investigates the intricate nature of P2 adoption by (1) distinguishing among three types of P2: procedural changes, input and material changes and equipment and product changes, (2) disentangling the adoption decision into a binary and a count decision, and (3) analyzing benefit- and cost-related factors. Using a sample of facilities of S&P 500 firms, I employ NB hurdle models to analyze how facilities respond to these factors in making P2 adoption decisions. I find that facilities that have lower cost of adoption due to past P2 experience have higher likelihood of adoption and higher rates of adoption of all types of P2. However, those exposed to greater threat of enforcement action find limited scope for P2 in achieving environmental compliance objectives. Some regulatory threat variables even have a negative effect. Facilities also adopt P2 to enjoy market-related benefits: final good producers are more likely to adopt P2 that appeal to consumers (input and material changes), while intermediate good producers are more likely to adopt P2 that is valued by its supply chain (procedural changes). Community characteristics and other knowledge sources are not always positively associated with the likelihood of adoption nor with the rate of adoption.     

Influence of decision styles on use of decision models

This article discusses how the “decision style” of an administrator influences the adoption and use of particular decision models. Several “interactive” and “analytical” decision models often used to guide decision making are described and critiqued to point out their virtues and deficiencies. Propositions are suggested that contend that “systematic,” “judicial”, “speculative”, and “intuitive” styles have clear-cut preferences for a particular decision model. This model seems to be used, even when another would be more suitable. Effective decision makers are postulated to adapt their styles, or at least to see the benefits of different styles. Mixed-mode models are proposed that seem to simulate the behavior of successful decision makers.