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Andrew Muhammad
Researcher at University of Tennessee
Publications - 105
Citations - 1114
Andrew Muhammad is an academic researcher from University of Tennessee. The author has contributed to research in topics: Tariff & Consumption (economics). The author has an hindex of 15, co-authored 103 publications receiving 982 citations. Previous affiliations of Andrew Muhammad include Mississippi State University & Southern University and A&M College.
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Price Risk and Exporter Competition in China's Soybean Market
TL;DR: In this paper, an import allocation model is used to examine the effects of price risk on exporter competition in China's soybean market, showing that price risk is an important determinant of China's imports across sources (Argentina, Brazil, and the United States).
Posted ContentDOI
U.s. demand for imported lamb by country: a two-stage differential production approach
TL;DR: In this article, the authors derived empirical estimates of derived demand for imported lamb and mutton differentiated by source country of production and type (frozen and chilled) with respect to frozen and chilled prices in Australia and New Zealand, U.S. wholesale lamb price, and the total amount of frozen/chilled lamb imported.
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The end of the trade war? Effects of tariff exclusions on U.S. forest products in China
Andrew Muhammad,Keithly G. Jones +1 more
TL;DR: In this article, the authors estimate China's lumber and log import demand and assess how tariffs affect the competitiveness of U.S. products compared to other exporting countries using a dynamic framework.
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Export Tax Reform and the Competitiveness of Imported Soybeans in China
Andrew Muhammad,Constanza Valdes +1 more
TL;DR: In this article, the authors examined the factors that determine China's demand for imported soybean products and how export taxes could affect exporting countries such as Argentina, Brazil and the United States.
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Estimating a Demand System with Seasonally Differenced Data
TL;DR: In this article, the authors show how to obtain consistent and asymptotically efficient estimates of a demand system using seasonally differenced data using Monte Carlo simulations and empirical application to the estimation of the U.S. meat demand.