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U.S. DEMAND FOR EDIBLE FATS AND OILS: A DYNAMIC SYSTEM WITH ARMA ERRORS
Steven T Yen,Wen S Chern People&Global Business Association 2002 Global Business and Finance Review Vol.7 No.1
This study investigates the effects of prices and total expenditure on U.S. household consumption of edible fats and oils that are strictly used for salad and cooking. A flexible dynamic demand system which nests the Translog and Almost Ideal Demand Systems is estimated using annual time-series data in the U.S. Results suggest that correction for serial correlation is important. With dynamic specification and correction for serial correlation. the generalized model outperforms the two restrictive specifications in terms of simple model adequacy but generates similar demand elasticities. Demands for fats and oils are found to be price inelastic. We also find a mix of gross substitutes and complements among the products considered. Findings on the effects of prices are useful for private edible oil companies in formulating their pricing strategies and for policy makers in designing effective domestic and trade policies.
A Novel Optimization-Based Approach for Minimum Power Multicast in Wireless Networks
Yen, Hong-Hsu,Lee, Steven S.W.,Yap, Florence G.H. The Korea Institute of Information and Commucation 2011 Journal of communications and networks Vol.13 No.1
In this paper, we formulate the minimum power multicast problem in wireless networks as a mixed integer linear programming problem and then propose a Lagrangean relaxation based algorithm to solve this problem. By leveraging on the information from the Lagrangean multiplier, we could construct more power efficient routing paths. Numerical results demonstrate that the proposed approach outperforms the existing approaches for broadcast, multicast, and unicast communications.
A Novel Optimization-Based Approach for Minimum Power Multicast in Wireless Networks
Hong-Hsu Yen,Steven S. W. Lee,Florence G. H. Yap 한국통신학회 2011 Journal of communications and networks Vol.13 No.1
In this paper, we formulate theminimumpowermulticast problem in wireless networks as a mixed integer linear programming problem and then propose a Lagrangean relaxation based algorithm to solve this problem. By leveraging on the information from the Lagrangean multiplier, we could construct more power efficient routing paths. Numerical results demonstrate that the proposed approach outperforms the existing approaches for broadcast,multicast, and unicast communications.