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on Forecasting |
| By: | Pavithra Parthasarathi; David Levinson (Nexus (Networks, Economics, and Urban Systems) Research Group, Department of Civil Engineering, University of Minnesota) |
| Abstract: | This research evaluates the accuracy of demand forecasts using a sample of recently-completed projects in Minnesota and identiÞes the factors inßuencing the inaccuracy in forecasts. The forecast traffic data for this study is drawn from Environmental Impact Statements(EIS), Transportation Analysis Reports (TAR) and other forecast reports produced by the Minnesota Department of Transportation (Mn/DOT) with a horizon forecast year of 2010 or earlier. The actual traffic data is compiled from the database of traffic counts maintained by the Office of Traffic Forecasting and Analysis section at Mn/DOT. Based on recent research on forecast accuracy, the (in)accuracy of traffic forecasts is estimated as a ratio of the forecast traffic to the actual traffic. The estimation of forecast (in)accuracy also involves a comparison of the socioeconomic and demographic assumptions, the assumed networks to the actual in-place networks and other travel behavior assumptions that went into generating the traffic forecasts against actual conditions. The analysis indicates a general trend of underestimation in roadway traffic forecasts with factors such as highway type, functional classiÞcation, direction playing an inßuencing role. Roadways with higher volumes and higher functional classiÞcations such as freeways are subject to underestimation compared to lower volume roadways/functional classiÞcations. The comparison of demographic forecasts shows a trend of overestimation while the comparison of travel behavior characteristics indicates a lack of incorporation of fundamental shifts and societal changes. |
| Keywords: | Minnesota, Minneapolis, Travel Demand Model, Transportation Planning, Forecasting |
| JEL: | R41 R48 D63 |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:nex:wpaper:forecastaccuracy&r=for |
| By: | Wolfgang Reichmuth; Samad Sarferaz |
| Abstract: | We present a new way to model age-specific demographic variables with the example of age-specific mortality in the U.S., building on the Lee-Carter approach and extending it in several dimensions. We incorporate covariates and model their dynamics jointly with the latent variables underlying mortality of all age classes. In contrast to previous models, a similar development of adjacent age groups is assured allowing for consistent forecasts. We develop an appropriate Markov Chain Monte Carlo algorithm to estimate the parameters and the latent variables in an efficient one-step procedure. Via the Bayesian approach we are able to asses uncertainty intuitively by constructing error bands for the forecasts. We observe that in particular parameter uncertainty is important for long-run forecasts. This implies that hitherto existing forecasting methods, which ignore certain sources of uncertainty, may yield misleadingly sure predictions. To test the forecast ability of our model we perform in-sample and out-of-sample forecasts up to 2050, revealing that covariates can help to improve the forecasts for particular age classes. A structural analysis of the relationship between age-specific mortality and covariates is conducted in a companion paper. |
| Keywords: | Demography, Age-specific, Mortality, Lee-Carter, Stochastic, Bayesian, State Space Models, Forecasts |
| JEL: | C11 C32 C53 I10 J11 |
| Date: | 2008–07 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2008-052&r=for |
| By: | Graefe, Andreas; Armstrong, J. Scott |
| Abstract: | Ideally, presidential elections should be decided based on how the candidates would handle issues facing the country. If so, knowledge about the voters’ perception of the candidates should help to forecast election outcomes. We make two forecasts of the winner of the popular vote in the U.S. Presidential Election. One is based on voters’ perceptions of how the candidates would deal with issues (problems facing the country) if elected. We show that this approach would have correctly picked the winner for the three elections from 1996 to 2004. The other is based on voters’ preference for policies and their perceptions of which policies the candidates are likely to pursue. Both approaches lead to a forecast that Democrat candidate Barack Obama will win the popular vote. |
| Keywords: | forecasting methods; regression models; index method; experience tables; accuracy |
| JEL: | C5 |
| Date: | 2008–08–04 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:9829&r=for |