Scenario-based Forecasting Research Articles

Forecast of mortgage-lending development in Russia subject to socioeconomic factors

Trends observed in mortgage lending in Russia between 2005 and 2012 are discussed in the paper, as well as their correlation with changes in the unemployment rate, one of the major indicators of socioeconomic development. The analysis is carried out at two levels, national and regional. The nationwide analysis employed an approach developed to reconcile scenario-based forecasts of the development of mortgage lending with different scenarios of unemployment forecast. It is argued that, going forward, there is a possibility of a pessimistic scenario with growing unemployment and a slowdown of growth in the mortgage debt levels. The differences in the degree of correlation between mortgage and unemployment rate by the regions of the Russian Federation, as well as factors behind them, are discussed at the regional level.

Urban Water Demand Forecasting: Review of Methods and Models

AbstractThis paper reviews the literature on urban water demand forecasting published from 2000 to 2010 to identify methods and models useful for specific water utility decision making problems. Results show that although a wide variety of methods and models have attracted attention, applications of these models differ, depending on the forecast variable, its periodicity and the forecast horizon. Whereas artificial neural networks are more likely to be used for short-term forecasting, econometric models, coupled with simulation or scenario-based forecasting, tend to be used for long-term strategic decisions. Much more attention needs to be given to probabilistic forecasting methods if utilities are to make decisions that reflect the level of uncertainty in future demand forecasts.

Using Global VAR Models for Scenario-Based Forecasting and Policy Analysis

This chapter demonstrates the usefulness of the GVAR modelling framework as a tool for scenario-based forecasting and counterfactual analysis. Working with the GVAR model developed by Greenwood-Nimmo, Nguyen and Shin (2010, J. Appl. Econometrics), we first show how probabilistic forecasting can be applied to the analysis of global imbalances. Probabilistic forecasting involves evaluating the conditional probability that a given event or combination of events will occur over a defined horizon by means of model-based simulations. To illustrate the usefulness of this approach, we develop a simple four-way probabilistic classi ficatory system built around the notions of balancing improvement in the trade balance, unbalancing improvement, balancing deterioration and unbalancing deterioration. We then extend a similar approach in a counterfactual context by constructing a range of scenarios as linear combinations of generalised impulse responses. We conclude that GVAR models are particularly well-suited to scenario-based analyses such as ours as they have the potential to analyse singularly rich datasets that allow the modeller to construct a wide range of policy-relevant scenarios.

A Scenario-Based Hydrocarbon Production Forecast for Louisiana

Conventional oil and gas productions in Louisiana has been in decline for four decades, but in recent years, new technology and capital investment have opened up a significant new resource play in the Haynesville shale, reversing Louisiana’s gas production decline. The need for long-term forecasting has become more important for state planning and for facilitating efficient regulatory development and incentive programs, as the largest oil and gas fields diminish in productivity and the promise of unconventional resources are realized. The purpose of this article is to present a hydrocarbon production forecast for Louisiana using disaggregate resource classes and a transparent analytic framework. A field-level evaluation is employed for producing fields categorized by primary product, resource category, geographic area, and production class. Undiscovered fields are classified according to conventional and unconventional categories and are modeled using a probabilistic and scenario-based forecast. The analytic framework is described along with a discussion of the model results and limitations of the analysis. Louisiana is in the early stages of transitioning to a primarily gas-producing state, and the manner in which the Haynesville shale develops will play a critical role in deliverability and economic prospects in the future.

Scenario-Based Forecast of Regional Water Demands in Northeastern Illinois

This paper reviews the principles of scenario-based planning and illustrates an application of this approach in a study of a regional water supply and demand in northeastern Illinois. The scenario-based planning has been adopted over the traditional approaches because the scenario approach facilitates an explicit consideration of uncertainty in both supply and demand. An important component of the study was the development of scenario-based forecasts of water demand for major user sectors and geographical subareas within the 11-county region which, includes the city of Chicago and 250 municipalities. The study revealed that the increase in total withdrawals in 2050, under baseline scenario, would be 530 mgd (millions of gallons per day) (36 %) and could reach as high as 949 mgd (64 %) under high demand growth scenario. Thus, meeting these additional demands would require large capital outlays for water infrastructure and would likely have significant impacts on some of the regional sources of water supply...

Stress-testing retail loan portfolios with dual-time dynamics

Stress-testing has become an important topic in retail lending since the introduction of the new Basel II guidelines. Here we use a scenario-based forecasting approach developed explicitly for retail lending in order to provide a suitable stress-testing approach. We first decompose the historical vintage performance data into a maturation function of months-on-books, a quality function of vintage origination date and an exogenous function of calendar date. In a second step, the exogenous function is modeled with macroeconomic data or factors representing portfolio management impacts. Stress tests are performed by extrapolating the exogenous function using externally provided scenarios for extreme macroeconomic events. The resulting scenario is combined with the known maturation and quality functions. This process is repeated for each member of a key set of rates, such as default rate, exposure at default and loss given default in the context of Basel II. These key rate forecasts are combined to create total portfolio forecasts and stress tests. This approach is demonstrated in an analysis of the US mortgage markets.

Prisoners of the proximate: loosening the constraints on epidemiology in an age of change.

"Modern epidemiology" has a primary orientation to the study of multiple risk factors for chronic noncommunicable diseases. If epidemiologists are to understand the determinants of population health in terms that extend beyond proximate, individual-level risk factors (and their biological mediators), they must learn to apply a social-ecologic systems perspective. The mind-set and methods of modern epidemiology entail the following four main constraints that limit engagement in issues of wider context: 1) a preoccupation with proximate risk factors; 2) a focus on individual-level versus population-level influences on health; 3) a typically modular (time-windowed) view of how individuals undergo changes in risk status (i.e., a life-stage vs. a life-course model of risk acquisition); and 4) the, as yet, unfamiliar challenge of scenario-based forecasting of health consequences of future, large-scale social and environmental changes. The evolution of the content and methods of epidemiology continues. Epidemiologists are gaining insights into the complex social and environmental systems that are the context for health and disease; thinking about population health in increasingly ecologic terms; developing dynamic, interactive, life-course models of disease risk acquisition; and extending their spatial-temporal frame of reference as they perceive the health risks posed by escalating human pressures on the wider environment. The constraints of "the proximate" upon epidemiology are thus loosening as the end of the century approaches.