Methods and Tools for the Microsimulation and Forecasting of Household Expenditure

This paper reviews potential methods and tools for the microsimulation and forecasting of household expenditure. It begins with a discussion of a range of approaches to the forecasting of household populations via agent-based modelling tools. Then it evaluates approaches to the modelling of household expenditure. A prototype implementation is described and the paper concludes with an outline of an approach to be pursued in future work

Methods and Tools for the Microsimulation and Forecasting of Household Expenditure - A Review

This paper reviews potential methods and tools for the microsimulation and forecasting of household expenditure. It begins with a discussion of a range of approaches to the forecasting of household populations via agent-based modelling tools. Then it evaluates approaches to the modelling of household expenditure. A prototype implementation is described and the paper concludes with an outline of an approach to be pursued in future work

An Agent-based Model of Household Spending Using a Random Assignment Scheme

This paper describes a way to model household expenditure using an agent-based microsimulation approach. Work in this area is dominated by econometric models, which attempt to estimate the parameters of an equation linking household characteristics with expenditure patterns. However, as Klevmarken (1997) notes, the representation of behaviour in these models is limited by the current state of the art in economics. A way to overcome these limitations is proposed based on a random assignment scheme where data is obtained from a donor case that is in some way similar to the receiving unit. The paper begins with a brief account of methods for modelling behaviour currently used in microsimulation. It then introduces the random assignment scheme in an example describing how household incomes can be projected over time. This is illustrated by describing two models that have been implemented using the approach. One of them is to forecast changes in household expenditure in response to variations in income. The other models the effect of the level of unemployment on household spending patterns. These examples provide the basis for a discussion of the strengths and weaknesses of the random assignment met

Orcutt’s Vision, 50 years on

Fifty years have passed since the seminal contribution of Guy Orcutt [Orcutt,1957],which gave birth to the field of Microsimulation. We survey, from a methodological perspective, the literature that followed, highlighting its relevance,its pros and cons vis-`a-vis other methodologies and pointing out the main open issues.

Low fertility and long run growth in an economy with a large public sector

There is plenty of evidence that growth has a negative relation to fertility and dependency ratios. Recently it has been suggested that low fertility countries may be caught in a trap that is hard to get out of. One important mechanism in such a trap would be social interaction and its effect on the ideal family size. Such social interaction mechanisms are hard to capture in formal models, therefore we use an agent based simulation model to investigate the issue. In our experimental setup a stable growth and population path is provoked into a fertility trap by rising relative child costs linked to positive growth. Even rather large increases in child benefits are then insufficient to get out of the trap. However, the small number of children temporarily enables the economy to grow faster for several decades. Removing the adaptation of social norms turns out to disarm the trap.low fertility trap; social norms relative income; economic growth

IFSIM Handbook

This handbook explains the simulation model IFSIM. IFSIM is an agent based simulation model written in JAVA. The model is constructed for analyzing demographic and economic issues. The aim of the model is to include the main consumption and production patterns over the life-cycle and thus being able to test demo-economic interactions.agent-based modelling; simulation model; JAVA; demogrphy; economy; demo-economic interactions

Application of geographic information systems and simulation modelling to dental public health: Where next?

Public health research in dentistry has used geographic information systems since the 1960s. Since then, the methods used in the field have matured, moving beyond simple spatial associations to the use of complex spatial statistics and, on occasions, simulation modelling. Many analyses are often descriptive in nature; however, and the use of more advanced spatial simulation methods within dental public health remains rare, despite the potential they offer the field. This review introduces a new approach to geographical analysis of oral health outcomes in neighbourhoods and small area geographies through two novel simulation methods-spatial microsimulation and agent-based modelling. Spatial microsimulation is a population synthesis technique, used to combine survey data with Census population totals to create representative individual-level population datasets, allowing for the use of individual-level data previously unavailable at small spatial scales. Agent-based models are computer simulations capable of capturing interactions and feedback mechanisms, both of which are key to understanding health outcomes. Due to these dynamic and interactive processes, the method has an advantage over traditional statistical techniques such as regression analysis, which often isolate elements from each other when testing for statistical significance. This article discusses the current state of spatial analysis within the dental public health field, before reviewing each of the methods, their applications, as well as their advantages and limitations. Directions and topics for future research are also discussed, before addressing the potential to combine the two methods in order to further utilize their advantages. Overall, this review highlights the promise these methods offer, not just for making methodological advances, but also for adding to our ability to test and better understand theoretical concepts and pathways

Potential Economic Consequences of Local Nonconformity to Regional Land Use and Transportation Plans Using a Spatial Economic Model

To achieve the greenhouse gas (GHG) reduction targets that are required by California’s global warming legislation (AB32), the state of California has determined that recent growth trends in vehicle miles traveled (VMT) must be curtailed. In recognition of this, Senate Bill 375 (SB375) requires regional governments to develop land use and transportation plans or Sustainable Community Strategies (SCSs) that will achieve regional GHG targets largely though reduced VMT. Although the bill requires such a plan, it does not require local governments to adopt general plans that conform to this plan. In California, it is local, not regional, governments that have authority over land development decisions. Instead, SB375 relies on democratic participatory processes and relatively modest financial and regulatory incentives for SCS implementation. As a result, it is quite possible that some local governments within a region may decide not to conform to their SCS. In this study, a spatial economic model (PECAS) is applied in the Sacramento region (California, U.S.) to understand what the economic and equity consequences might be to jurisdictions that do and do not implement SCS land use plans in a region. An understanding of these consequences provides insight into jurisdictions’ motivations for compliance and thus, strategies for more effective implementation of SB375