Advances in Business and Management Forecasting

Inbunden, Engelska, 1998

Del 2 i serien Advances in Business and Management Forecasting

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Beskrivning

This study of business management and forecasting covers: forecasting in transportation; forecasting for water and energy; and the applications of forecasting. It includes a case study of the Greek airport system.

Produktinformation

Utgivningsdatum:1998-04-23
Mått:156 x 234 x 15 mm
Vikt:551 g
Format:Inbunden
Språk:Engelska
Serie:Advances in Business and Management Forecasting
Antal sidor:266
Förlag:Emerald Publishing Limited
ISBN:9780762300020

Utforska kategorier

Innehållsförteckning

Part 1 Forecasting in transportation: information based estimation of spatial interaction - the calibration and forecasting of commuting in the state of Indiana, Daniel K. Knudsen and Nicholas Heyman; a combined mode choice Hayman - a combined mode choice and traffic assignment model for evaluating committee travel options, Maria Boile and Lazar Spasovic; the use of nonlinear time series in airport forecasting - a case study of the Greek airport system, Matthew G. Karlaftis et al. Part 2 Forecasting for water and energy: water demand estimates in Texas, Michael Nieswiadonny; demand and revenue elasticities and increasing block rate pricing of residential water services, Julie A. Hewitt; a state model of water demand, Robert Hordon et al; an analysis of the effectiveness of price as a water conservation/demand management tool, Michael Nieswiadonny; demand systems for energy forecasting - practical consideration for estimating generalized logit model, Weifeng Weng and Timothy Mount. Part 3 Applications of forecasting: forecasting state tax revenues - a new approach, Kenneth D. Lawrence et al; forecasting and monitoring state tax revenue throughout the budget cycle, Ray D. Nelson et al; forecasting migration flows, Sheila M. Lawrence and Charles A. Nanry; deterministic and stochastic forecasting of high tech market place competition, Eileen Bridges et al; a logit modelling approach to forecasting sales using self reported consumer purchase intentions, David Whitlark et al.