Data-based mechanistic modelling and forecasting globally averaged surface temperature

The main objective of this paper it to model the dynamic relationship between global averaged measures of Total Radiative Forcing (RTF) and surface temperature, measured by the Global Temperature Anomaly (GTA), and then use this model to forecast the GTA. The analysis utilizes the Data-Based Mechanistic (DBM) approach to the modelling and forecasting where, in this application, the unobserved component model includes a novel hybrid Box-Jenkins stochastic model in which the relationship between RTF and GTA is based on a continuous time transfer function (differential equation) model. This model then provides the basis for short term, inter-annual to decadal, forecasting of the GTA, using a transfer function form of the Kalman Filter, which produces a good prediction of the ‘pause’ or ‘levelling’ in the temperature rise over the period 2000 to 2011. This derives in part from the effects of a quasi-periodic component that is modelled and forecast by a Dynamic Harmonic Regression (DHR) relationship and is shown to be correlated with the Atlantic Multidecadal Oscillation (AMO) index.     

Change within purchasing and supply management organisations – Assessing the claims from maturity models

It is a wide-held assumption that professional development and change within purchasing and supply management (PSM) organisations can be explained and guided by a maturity model. In this paper the guidance which the maturity model concept offers to understand a PSM organisation's performance is assessed. The methodology is based on the outcomes of a literature review of PSM maturity models, development of an organisational change framework and the learning from three qualitative case studies. An alternative understanding of the development of the PSM organisation is offered through an organisational change framework, composing 1) movement transitions, 2) scalability of change, 3) acceptability of change, and 4) the substantive element of change. The research found that extant PSM maturity models are too rigid for PSM managers to apply, and although maturity models are commonly accepted in PSM literature, in practice, they may produce the opposite effect of what is promised. The PSM maturity models suggest that their application will lead to increased status and influence of PSM within the organisation; expectations that may not be met. PSM organisations’ change processes are subjected to a range of situational and contextual power relations which must be considered in order to advance the specific PSM organisation roles and responsibilities.     

Integrated bio-economic models as tools to support land-use decision making: a review of potential and limitations

Bio-economic modelling has become a useful tool for anticipating the outcomes of policies and technologies before their implementation. Advances in mathematical programming have made it possible to build more comprehensive models. In an overview of recent studies about bio-economic models applied to land-use problems in agriculture and forestry, we evaluated how aspects such as uncertainty, multiple objective functions, system dynamics and time have been incorporated into models. We found that single objective models were more frequently applied at the farm level, while multiple objective modelling has been applied to meet concerns at the landscape level. Among the objectives, social aspects are seldom represented in all models, when being compared to economic and environmental aspects. The integration of uncertainty is occasionally a topic, while stochastic approaches are more frequently applied than non-stochastic robust methods. Most multiple-objective models do not integrate uncertainty or sequential decision making. Static approaches continue to be more recurrent than truly dynamic models. Even though integrating multiple aspects may enhance our understanding of a system; it involves a tradeoff between complexity and robustness of the results obtained. Land-use models have to address this balance between complexity and robustness in order to evolve towards robust multiple-objective spatial optimization as a prerequisite to achieve sustainability goals.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

Transition towards and of sustainability—Understanding sustainability as performative

Our purpose is to explore the concept of “sustainability” when understood from a performative perspective, i.e. as a concept that is filled with meaning across time. Drawing on a 10 year-long study of the digital footprint of Stockholm Royal Seaport, claimed to be northern Europe's largest sustainable urban development district, we show that “sustainability” emerged as the project became associated with particular places, projects, histories, and technologies. This means that “sustainability” was local in that it was situated in the particular spatial context of the project; temporal in that it was situated in a particular time; and political in that it expressed particular values and perspectives. The study contributes to explaining why “sustainability” remains—and always will remain—a contested concept, which is why sustainability transitions are complex. Consequently, we suggest that the transition towards sustainability always involves the transition of sustainability, something that needs to be acknowledged in order for a transition to actually become sustainable.     

Real Incentive Effects of Soft Information

Both soft, noncontractible, and hard, contractible, information are informative about managerial ability and future firm performance. If a manager's future compensation depends on expectations of ability or future performance, then the manager has implicit incentives to affect the information. We examine the real incentive effects of soft information in a dynamic agency with limited commitment. When long-term contracts are renegotiated, the rewards for future performance inherent in long-term contracts allow the principal partial control over the implicit incentives. This is because the soft information affects the basis for contract renegotiation. With short-term contracts, the principal has no control over the basis for contract negotiation, and thus long-term contracts generally dominate short-term contracts. With long-term contracts, the principal's control over implicit incentives is characterized in terms of effective contracting on an implicit aggregation of the soft information that arises from predicting (forming expectations of) future performance. We provide sufficient conditions for soft information to have no real incentive effects. In general, implicit incentives not controllable by the principal include fixed effects, such as career concerns driven by labor markets external to the agency. When controllable incentives span the fixed effects of career concerns, the latter have no real effects with regard to total managerial incentives—they would optimally be the same with or without career concerns. Our analysis suggests empirical tests for estimating career concerns that should explicitly incorporate noncontractible information.     

Costs and potential funding of expanded public pension coverage in Asia

Public pension burdens in most emerging Asian economies are still relatively small. However, there are a number of reasons to believe that they will increase markedly in the coming years. First, many Asian economies will face rapidly ageing populations, which will raise pension and other old‐age‐related spending dramatically. Second, as economies develop, political pressures to expand the coverage of public pensions and raise pension benefits will likely increase. The first objective of this paper is to identify the potential fiscal burden of public pensions in 23 emerging Asian economies, based on econometric models and forecasts of GDP and demographic trends. Using two different methodologies yields estimated increases in the average share of public pension expenditures in GDP of 1.0 percentage point and 3.6 percentage points by 2030 compared with current levels. We believe the latter estimate is more realistic. The second objective is to recommend policies to provide adequate funding for public pension needs, including enhancing the efficiency of social insurance programs, improving the balance of revenues and expenditures, implementing more explicit fiscal rules and frameworks, and establishing stronger fiscal surveillance at the national and regional levels.     

Some implications for monetary policy of uncertain exchange rate pass-through

The paper uses MULTIMOD to examine the implications of uncertain exchange rate pass‐through for the conduct of monetary policy. From the policymaker's perspective, uncertainty about exchange rate pass‐through implies uncertainty about policy multipliers and the impact of state variables on stabilization objectives. When faced with uncertainty about the strength of exchange rate pass‐through, policymakers will make less costly errors by overestimating the strength of pass‐through rather than underestimating it. The analysis suggests that pass‐through uncertainty of the magnitude considered does not result in efficient policy response coefficients that are smaller than those under certainty.