The REMI Economic-Demographic Forecasting and Simulation Model

Treyz, George I.; Rickman, Dan S.; Shao, Gang

doi:10.1177/016001769201400301

Cited by 109 publications

(63 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In order to arrive at an estimation 1 For a full description of the REMI model see Treyz et al 1992 andTreyz 1993. The REMI model is an o¤-the-shelf regional econometric (CGE-type) model with developed forecasting and policy simulation capabilities.…”

Section: Methods and Datamentioning

confidence: 99%

Do high technology agglomerations encourage urban sprawl?

Felsenstein¹

2002

The Annals of Regional Science

View full text Add to dashboard Cite

Abstract. This paper looks at the impact of high technology employment concentrations on urban sprawl. A methodology for translating spatial employment patterns, into place of residence patterns, is presented. On this basis, the consumption of land at the urban fringe due to both residential and nonresidential uses, is estimated. The method is tested empirically using data relating to the two main outer suburban agglomerations of high technology activity in the Chicago metropolitan area. Two counter-factual situations are simulated. The first relates to a spatial counter-factual whereby the high tech concentrations develop in the city of Chicago or within the inner suburbs. The second presents an industry counter-factual that estimates the land consumption impacts arising from the development of an alternative industrial concentration in the same location. The results of the actual and hypothetical cases are compared. They point to a considerable saving in acreage in all alternative scenarios. Some policy implications are highlighted.

show abstract

Section: Methods and Datamentioning

confidence: 99%

Do high technology agglomerations encourage urban sprawl?

Felsenstein¹

2002

The Annals of Regional Science

View full text Add to dashboard Cite

show abstract

“…The methodology was first initiated in the mid-1970s by G. I. Treyz, A. F. Friedlander, and B. H. Stevens (Economics Department, University of Massachusetts) (50,51). A core version of the model was then developed for the National Academy of Sciences.…”

Section: Remi Policy Insightmentioning

confidence: 99%

Socioeconomic Forecasting

Xiong¹,

Fricker²,

McNamara³

2012

View full text Add to dashboard Cite

JOINT TRANSPORTATION RESEARCH PROGRAMThe Joint Transportation Research Program serves as a vehicle for INDOT collaboration with higher education institutions and industry in Indiana to facilitate innovation that results in continuous improvement in the planning, design, construction, operation, management and economic efficiency of the Indiana transportation infrastructure. https://engineering.purdue.edu/JTRP/index_html Published reports of the Joint Transportation Research Program are available at: http://docs.lib.purdue.edu/jtrp/ NOTICEThe contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views and policies of the Indiana Department of Transportation or the Federal Highway Administration. The report does not constitute a standard, specification or regulation. AbstractThe role of the REMI Policy Insight+ model in socioeconomic forecasting and economic impact analysis of transportation projects was assessed. The REMI PI+ model is consistent with the state of the practice in forecasting and impact analysis. REMI PI+, like its competitors, is vulnerable to the trends contained in the historical data it uses, especially recent trends. After the most recent periodic update in data, the performance of the REMI PI+ model improved, that is, it produced long-term forecasts that were more credible. Zonal-level population and employment forecasts for direct input to the Indiana Statewide Travel Demand Model (ISTDM) can be achieved by applying disaggregation regression methods. Indiana University's Center for Econometric Model Research (CEMR) model is also a sound forecasting model. Because of the knowledge of in-state economists, the CEMR-IBRC model could provide forecasts of the Indiana economy that reflect characteristics not known to out-of-state forecasters. The researchers also examined economic impact analysis models that are possible alternatives to REMI PI+. Acquiring a new economic impact analysis package does not seem necessary for INDOT, if REMI forecasts can be adjusted to (a) accommodate recent and reasonable expected trends in the Indiana economy, and (b) meet the geographic (TAZ) needs of the ISTDM. MCIBAS-which is currently used by the Indiana Department of Transportation-is a good hybrid system to use in the economic impacts analysis of transportation projects. Indiana University's CEMR is capable of conducting economic impact analyses, with local knowledge of the Indiana economy, at a cost lower than REMI's. However, INDOT would have to decide whether these potential advantages justify changing the present relationship with REMI. In cases where the credibility of data, forecasts, and/or impact analyses needs to be verified, an Because the traffic forecasts produced by the Indiana Statewide Travel Demand Model (ISTDM) are driven by the demographic and socioeconomic inputs to the model, particular attention must be given to obtaining the most accurate demographic an...

show abstract

“…Although simulation modeling is a well established technique for projecting future emissions, there are comparatively few examples of its use to generate longrun forecasts for regions of the U.S. The best example is the REMI economic-demographic model and its antecedents (Stevens and Treyz 1986;Treyz et al 1992;Treyz 1993), which are demand-driven simulations of the spatial economy. 2 This may be contrasted with an intertemporal optimization framework used in multi-regional climate policy simulations (e.g., Manne and Richels 1992; Nordhaus and Boyer 1999) and dynamic rural-urban computable general equilibrium (CGE) models (e.g., Kelly and Williamson 1984;Becker et al 1992), in which representative agents choose trajectories of investment to maximize their present value of discounted utility from consumption, looking forward over the entire simulation horizons.…”

Section: The Within-period Spatial General Equilibrium Modelmentioning

confidence: 99%

“…Our preferred approach is the econometrically-calibrated investment accelerator model of Treyz et al (1992) and Rickman et al (1993). We propose to adapt this specification to work at the level of individual industries, focusing on two sets of influences on investment:…”

Section: The Dynamic Process Of the Economymentioning

confidence: 99%

“…To keep things simple, we avoid explicit characterization of the demographic structure of either state populations or migrants. Instead, we propose to model state population growth using crude birth and death rates by state (b and d, respectively), which are assumed to follow exogenous trends: Greenwood et al (1991) and Treyz et al (1992). We specify gross immigration and emigration at the state level, as well as population redistribution within-state migration (M R ), as functions of states' populations, average population densities (ρ N ), and their wage and employment rates:…”

Section: E N S T N S T G S T M S T M S Tmentioning

confidence: 99%

See 1 more Smart Citation

Modeling Small Area Economic Change in Conjunction with a Multiregional CGE Model

Wing

Anderson

Globalization and Regional Economic Modeling

View full text Add to dashboard Cite

Regional economic projections are often made at the level of spatial aggregation that is most convenient from political, data and computational perspectives. For example, in the US projections are often made at the level of 50 states. From a policy perspective this makes some sense since disbursements of federal funds for infrastructure are generally made to state governments and sub-state governments have little political representation at the federal level. Economic data is much richer at the state level than at any sub-state level. Also, for many models a larger number of regions than the 50 implies excessive computational burden.There are, however, a number of economic issues that cannot be adequately addressed at the state level. A few examples include: Economically depressed regions which seldom coincide with state borders. Even a state as small as New Hampshire has significant differences in economic well being from north to south and large states like Texas may simultaneously contain some of the most affluent and some of the most depressed regions in the country. Land use change such as the transformation of agricultural land and the general phenomenon of urban sprawl cannot be realistically addressed at the state level. Environmental pollution studies benefit greatly from spatial disaggregation. It may be possible to estimate aggregate emissions at the state level but not to estimate exposed populations or to provide input to atmospheric models. In transportation studies , origins and destinations of trips are points rather than areas, so greater spatial detail generates more useful results. Furthermore, the spatial impacts of transportation infrastructure do not necessarily respect state borders.Our purpose in this paper is to specify a comprehensive and rigorous framework for sub-state economic analysis. In order to do this we must first define a set of substate spatial units. There have been attempts in the past to develop models from economically functional regions rather than political jurisdictions. These include the BEA regions and National Transportation Analysis Regions. From the perspective of data availability it is easier to work with regional definitions that have long histories

show abstract

The REMI Economic-Demographic Forecasting and Simulation Model

Cited by 109 publications

References 11 publications

Do high technology agglomerations encourage urban sprawl?

Do high technology agglomerations encourage urban sprawl?

Socioeconomic Forecasting

Modeling Small Area Economic Change in Conjunction with a Multiregional CGE Model

Contact Info

Product

Resources

About