Seemingly unrelated intervention time series models for effectiveness evaluation of large scale environmental remediation.
Large scale environmental remediation projects applied to sea water always involve large amount of capital investments. Rigorous effectiveness evaluations of such projects are, therefore, necessary and essential for policy review and future planning. This study aims at investigating effectiveness of environmental remediation using three different Seemingly Unrelated Regression (SUR) time series models with intervention effects, including Model (1) assuming no correlation within and across variables, Model (2) assuming no correlation across variable but allowing correlations within variable across different sites, and Model (3) allowing all possible correlations among variables (i.e., an unrestricted model). The results suggested that the unrestricted SUR model is the most reliable one, consistently having smallest variations of the estimated model parameters. We discussed our results with reference to marine water quality management in Hong Kong while bringing managerial issues into consideration.
Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong, China. Electronic address: ryanhlip@hku.hk.
Ryan H L
RH
Ip
W K
WK
Li
Kenneth M Y
KM
Leung
D003247:Conservation of Natural Resources; D004784:Environmental Monitoring; D052918:Environmental Restoration and Remediation / Q000191:economics / Q000379:methods* / Q000706:statistics & numerical data; D006723:Hong Kong; D008962:Models, Theoretical; D014876:Water Pollution / Q000517:prevention & control
Environmental remediation; HATS; Intervention analysis; Seemingly unrelated regression; Time series