Situational analysis beyond 'single-exit' modelling

Situational analysis (SA) is currently the method of mainstreammicroeconomics, and this paper maintains that it may also performan important role in heterodox research programmes in economics.Its most elaborate version in economics, the ‘single-exit’modelling approach, is an epistemologically driven endeavourwhich does not take full account of the way our world works.In this paper, it is argued that a fully consistent SA thatis firmly grounded on sound human ontological foundations canbe pursued if it is detached from the Rationality Principle,if closed-system modelling is abandoned, and if a retroductivemode of inference is embraced.     

Subjective conditional probability modelling

Cross-impact analysis is a technique for investigating the effect of interaction of events in future oriented studies. A fundamental difficulty with cross-impact analysis is to determine what respondents mean when they answer the “conditional probability” questions normally posed. This paper offers a heuristic alternative to traditional cross-impact analysis which is applicable to cross-impact situations where the object is to generate scenarios for decision making.     

Scale and structure in economic modelling

In this paper an attempt is made to put together, within the same model, the supply and the demand side of the economic process. This is done by integrating the progress attained in two fields which have traditionally been considered separate: the input-output approach and the macroeconometric final demand approach. The composition of each traditional macroeconomic variable, determined within the model, is analysed into the functional headings which are peculiar for each variable (family budgets headings for personal consumption expenditures, investing sectors for investments, I-O sectors for output and so on). We can then identify for each variable a scale component - absolute level of the aggregated variable - and a structure component - its inner composition - which are mutually consistent. Two types of applications of the same model are then compared: simulation, where the parametric set is used to obtain the dynamic path of a specific scenario; and integrated impact multiplier analysis where scale and structure effects are evaluated with reference to all the potential behaviour of the system.     

A multilevel modelling approach to triple-bounded dichotomous choice contingent valuation

The use of dichotomous choice (DC) questions in the elicitation of willingness to pay (WTP) in contingent valuation studies is common practice at the present time. Recent research has shown that double-bounded DC questions provide statistically superior results to single-bounded questions, given an appropriate sampling design. This paper uses a relatively new multilevel modelling technique to analyze a triple-bounded DC design, which in addition includes an initial non-monetary question on whether an individual accepts, in principle, a WTP some unspecified amount. The theoretical basis of the multilevel model used is described, and some of the possibilities of this potentially powerful and versatile technique are discussed. The practical operation of the multilevel model is demonstrated using data from a contingent valuation study conducted in the Norfolk Broads, England, an internationally important wetland resource.     

A multivariate time series approach to modelling macroeconomic sequences

In this paper we discuss a multivariate generalization of autoregressive integrated moving average models. A methodology for constructing multivariate time series models is developed and the derivation of forecasts from such models is considered. A bivariate model for Austrian macroeconomic sequences is constructed. Furthermore it is discussed whether multivariate time series methods can be expected to lead to a significant increase in prediction accuracy when forecasting macroeconomic series.     

Multi-attribute preference modelling and regional land-use planning

Forest management policy decisions are complex due to the multiple-use nature of goods and services from forests, difficulty in monetary valuation of ecological services and the involvement of a large number of stakeholders. Multi-attribute decision techniques can be used to synthesise stakeholder preferences related to regional forest planning because it can accommodate conflicting, multidimensional, incommensurable and incomparable objectives. The objective of this paper is to examine how the Analytical Hierarchy Process (AHP) can be used to incorporate stakeholder preferences in determining optimal forest land-use choices. The Australian Regional Forest Agreement Programme is taken as an illustrative case for the analysis. The results show that the AHP can formalise public participation in decision making and increase the transparency and the credibility of the process.     

A framework for modelling economy-environment-development relationships based on dynamic carrying capacity and sustainable development feedback

A large literature exists that deals with economic issues of development and growth. It includes various approaches that can be classified as formal-theoretical, empirical-statistical and qualitative-verbal. Recently, the issue of sustainable development has been discussed much, which has given rise to some novel views on the relationship between development and environment. Most of these contributions utilize a more or less qualitative approach. A comprehensive inquiry of the relationship between economy, development, growth and environment may include an analytical approach as well. An analytical framework is proposed here for studies of environment-economy-development relationships that separates between economy, development, environment, and value system. A distinction is made between direct and indirect economy-environment interactions. To overcome disadvantages of assumptions of determinism in long term analysis a sustainable development feedback mechanism is proposed. It reflects anticipative behaviour to natural environmental changes in making long term decisions. This can be seen as a specific element of endogenous growth, namely one based on environmental factors. To illustrate the ideas a number of theoretical models are discussed that can be regarded as dynamic formal extensions of the concept of carrying capacity. Different cases include combinations of internal and external feedback mechanisms to an economy. The results demonstrate that in addition to behaviourial patterns that have been obtained by more complex studies other patterns may emerge.This study was supported by the Economic Research Foundation (Ecozoek), which resorts under the Netherlands Organization for Scientific Research (N.W.O.), project no. 450-230-007.This paper is based on reprocessed material from my Ph.D. dissertation. I am grateful to my thesis supervisors Peter Nijkamp and Hans Opschoor for comments at various stages in the research.     

Recycling and extending product-life: an evolutionary modelling

This paper presents a model-based analysis of firms’ economic incentives to extend product life and market recyclable products. We address this problem by developing an evolutionary simulation model to describe the behavior of business firms as interacting with consumers and recyclers. Market structure, business firms’ R&D strategies and consumers’ preferences are found to have an important influence here. The simulation results show that improvement in recycling is necessary but not sufficient to solve the issue of increase in waste. Recycling will need to be backed up with an extension in product-life to face such an unwanted increase. Adopting such a strategy could be positive for firms and for the environment because it could lead to high economic performance, both in terms of profits and market share, and to high environmental performance, both in terms of product recyclability and product lifetime. Our results suggest introducing regulation policies aimed at encouraging firms to invest in developing green products, i.e. those which are easily recyclable and which have a long lifetime, and getting consumers to buy them. It would also seem crucial to lend support to innovation outlay for such firms and to favor their seeking to improve product performance.

The econometric modelling of aggregate consumer behaviour

This paper examines several problems involved in modelling the structure of consumer behaviour. A new model of consumer demand is presented which encompasses the indirect translog, the CES and the LES as special cases, enabling nested comparisons to be made. Within this framework we show that acceptance of zero-degree homogeneity and/or symmetry of the Slutsky matrix is heavily dependent upon the structure within which inferential tests are carried out, and that great care must be utilised in formulating nested hypotheses and drawing inferences on the basis of χ² and F-statistics. We then show that conventional econometric work implies that consumer preferences must be homothetic if symmetry of the Slutsky matrix is imposed. An alternative procedure is suggested and utilised. Finally, we suggest that low Durbin–Watson statistics may arise from misspecification of functional form and may not be due solely to omitted dynamics, as many conjecture.     

A cost-benefit analysis of moose harvesting in Scandinavia. A stage structured modelling approach

A cost-benefit analysis of moose (Alces alces) harvesting in Scandinavia is presented within the framework of an age structured model with four categories of animals (calves, yearlings, adult females, and adult males). The paper aims to demonstrate the economic content of such a wildlife model and how this content may change under shifting economic and ecological conditions. Two different harvesting regimes are explored: landowner profit maximization, where the combined benefit of harvesting value and browsing damage is taken into account, and overall management, where the costs and damages of moose-vehicle collisions are taken into account as well. An empirical analysis of the Norwegian moose stock indicates that the present stock level is far too high compared with the overall management scenario, and that the composition of the harvest could be improved.     

Integrated hydro-economic modelling: Approaches, key issues and future research directions

Integrated hydro-economic models aim to capture the complexity of interactions between water and the economy. Three main approaches are distinguished: modular, holistic and computable general equilibrium models. The latter top-down models counterbalance the traditional emphasis on bottom-up water engineering approaches. Key issues and future research directions in integrated hydro-economic modelling are discussed and illustrated through a variety of case study applications worldwide. Although the interaction works both ways, feedback effects of water changes on the economy and changes in the economy on the water system are often missing in practice. The link between water and ecology is another important future research direction.     

Dynamic spatial modelling of regional convergence processes

Econometric analysis of convergence processes across countries or regions usually refers to a transition period between an arbitrary chosen starting year and a fictitious steady state. Panel unit root tests and panel cointegration techniques have proved to belong to powerful econometric tools if the conditions are met. When referring to economically defined regions, though, it is rather an exception than the rule that coherent time series are available. For this case we introduce a dynamic spatial modelling approach which is suitable to trace regional adjustment processes in space instead of time. It is shown how the spatial error-correction mechanism (SEC model) can be estimated depending on the spatial stationarity properties of the variables under investigation. The dynamic spatial modelling approach presented in this paper is applied to the issue of conditional income and productivity convergence across labour market regions in unified Germany.First version received: December 2002/Final version received: June 2003We would like to thank an anonymous referee for his helpful comments.     

A note on the ecological-economic modelling of marine reserves in fisheries

This paper gives an overview of the bioeconomic modelling of marine reserves, and illustrates how economists have responded to the modelling results found in the ecological literature. The economic analysis is shown to be far more pessimistic with regards to the potential of marine reserves as a fisheries management tool, than what one finds in the purely ecological analysis, the reason being the latter's neglect of issues such as discounting and economic incentive behaviour. However, economic analysis, despite some of it being relatively advanced with regards to spatiality, is still simplistic with regards to for instance ecosystem and habitat content. A simple expansion of the existing bioeconomic models with regards to positive habitat effects of area closures is presented and analysed, showing room for improved results from marine reserve implementation as compared to the existing analysis.     

Mathematical modelling as a tool to aid R&D investment decisions in a sustainable development policy climate

By its very nature, investment in R&D is a challenging proposition. It is a decision made today for an uncertain return in the future. Moreover, it has become more complex with the introduction of sustainable development considerations into the decision-making process. In this paper, the effect of environmental policy on the economic framework within which new technologies must compete is discussed, and analytical techniques which help assess these complexities are described. The return on an R&D investment is determined by the competitiveness of the resultant innovation at the time of its eventual application. A new technology is competitive relative to a given economic framework (which includes such factors as alternative technologies, availability of investment capital, trained human resources, etc).Government involvement in establishing goals and timetables for environmental protection signficantly affects the evolution of the economic framework in which new technologies must perform. To be successful in the 'sustainable development' age, the R&D investor must be able to anticipate the effect and development of government environmental and economic policy. Mathematical modelling is a valuable tool to help the investor and policy-maker appreciate the key risk factors and to understand the effect of environmental and economic policy on desired outcomes. For R&D, most model development has focused on policy development and is not very useful supporting the business decisions msociated with effective R&D investment. In this paper, the interaction bebeen government policy and business decision-making is discussed, and a methodology, supported by a model, is presented to aid R&D investors to take environmental and sustainabilip issues into account when assessing the future competitiveness of technology.     

Multivariate multiple regression modelling for technology analysis

Technology analysis is important for technology management areas such as research and development strategy and new product development. So many studies on technology analysis have been used across a diverse array of fields. Most of these were based on patent analysis, which analyses patent documents using text mining and statistics. The studies on conventional patent analyses constructed models consisting of various independent variables (technologies) and one dependent variable. But in reality, we have to consider a model that includes several dependent variables at the same time, because most technologies influence each other. In this paper, we propose a methodology for patent analysis that reflects the various response technologies simultaneously. We perform multivariate multiple regression modelling in order to efficiently conduct our technology analysis. To show how our modelling can be applied to realistic context, we carry out a case study using the patent documents related to three-dimensional printing technology.     

Cost effectiveness of prophylactic HPV 16/18 vaccination in Finland: results from a modelling exercise

Objectives: the study aims to estimate the clinical-impact and cost-effectiveness value of adding human papillomavirus 16/18 vaccination against cervical cancer among women currently undergoing organised screening in Finland.

Methods: A Markov cohort model evaluating high-risk HPV infections and cervical cancer (CC) cases combined with screening has been customised to the Finnish setting. The model outcome for a cohort of 30,000 girls aged 10 years was calibrated to age-specific annual number of Pap smears, CC incidence and mortality.

Results: The observed age-specific incidence and mortality rates of CC closely match the data replicated by the model. The model predicts that with a 90% vaccine coverage rate, CC cases and mortality would be reduced by 70%. In the base-case analysis with a discount rate of 3% the incremental cost per quality-adjusted life-years (QALY) gained, from a healthcare perspective, was €17,294. Without discounting this value is €2,591/QALY gained.

Conclusions: The analysis suggests that implementing prophylactic CC vaccination within the current screening system would substantially reduce CC cases and deaths, as well as the overall disease burden expressed in pre-cancer lesions averted. Vaccination could be a cost-effective intervention in Finland despite the fact that the number of CC cases and deaths are currently relatively low. Conservative estimates of the cost effectiveness of the vaccination were provided since it was not possible to assess herd protection induced by vaccination using this Markov model.     

Exploring the variability of patient costs in disease management programs: a hierarchical modelling approach

Disease management programs include a wide variation of patients with different chronic diseases and different health care utilization. The aim of this article was to identify factors on patient-level and organizational-level that explain the variability in costs of patients with different chronic diseases enrolled in a DMP by employing a rigorous analytical model. A generalized linear mixed model (GLMM) was specified to perform a multi-level analysis of cross-sectional hierarchical data from 16 DMPs in the Netherlands. Multiple imputation, sub-group analysis per disease and analysis from both the health care and the societal perspectives were also performed. Our model showed that age, the presence of cardiovascular disease, multi-morbidity and payments on top of the payment for the usual care had positive relation with costs, while better quality of life was associated with lower health care costs. In the COPD sample, physical activity and employment were associated with health care costs. Our study showed that there is great variability in health care costs among patients included in DMPs and identified patient and organizational explanatory factors. The findings are relevant to the design of future DMPs and their payment schemes.     

Long range dependence in an emerging stock market’s sectors: volatility modelling and VaR forecasting

This study evaluates the sector risk of the Qatar Stock Exchange (QSE), a recently upgraded emerging stock market, using value-at-risk models for the 7 January 2007–18 October 2015 period. After providing evidence for true long memory in volatility using the log-likelihood profile test of Qu and splitting the sample and dth differentiation tests of Shimotsu, we compare the FIGARCH, HYGARCH and FIAPARCH models under normal, Student-t and skewed-t innovation distributions based on in and out-of-sample VaR forecasts. The empirical results show that the skewed Student-t FIGARCH model generates the most accurate prediction of one-day-VaR forecasts. The policy implications for portfolio managers are also discussed.     

A new approach to modelling sigmoidal curves

Growth and diffusion phenomena have become of great interest to investigators in many disciplines, such as Biology, Demography, Economy, Agriculture, etc. These processes are generally analyzed by means of growth curves. As, in nature, it is not possible for any variable to continue growing indefinitely, we can consider any growth process to have an upper limit or saturation level. Thus, should a model represent a growth phenomenon, it will be described by a sigmoidal or S-shaped curve. There are a wide variety of growth models in general and specific literature. Of these, the logistic model is without doubt one of the most studied in practice, as well as some modifications of it, including recent investigations directed to the decomposition of a growth curve into various logistic components [Technol. Forecast. Soc. Change 47 (1994) 89; Technol. Forecast. Soc. Change 61 (1999) 247.]. In all the cases above, the adopted approach includes fitting the trend curve to the data by means of a well-known estimation procedure, such as least squares. We suggest a somewhat different approach, which consists of expressing the model through its differential equation and searching for a functional specification for the variable representing growth rate. Two series have been chosen from the recent literature in order to illustrate the methodology presented.     

Forecasting the probability of US recessions: a Probit and dynamic factor modelling approach

Abstract Quantifying the probability of U.S. recessions has become increasingly important since August 2007. In a data‐rich environment, this paper is the first to apply a Probit model to common factors extracted from a large set of explanatory variables to model and forecast recession probability. The results show the advantages of the proposed approach over many existing models. Simulated real‐time analysis captures all recessions since 1980. The proposed model also detects a significant jump in the next six‐month recession probability based on data up to November 2007, one year before the formal declaration of the recent recession by the NBER.