Until recently, most interest in econometrics was directed toward the data analysis problem. However, as opportunities for controlled experimentation in economics and business become more common, econometricians' interest in the statistical theory of experimental design is a natural consequence. Design techniques have and are being applied in marketing research, and in the contexts of such policy areas as the negative income tax and manpower experiments, the housing subsidy and health insurance subsidy experiments, and others. Experimentation in economics and business is an extremely complex research undertaking compared to the experimentation in other fields (such as agriculture, physics or chemistry) for which most of the design theory was developed. So, control experimentation in business and economics tends to be expensive in dollars, time and research talent. All this suggests the importance of carefully designing experiments to maximize their information output (efficient design) and the need for econometricians to extend the design theory in a number of directions of particular interest to economists and business researchers. This paper provides a solution to the problem of experimental designs for various multiple equation regression models frequently encountered by business and economic researchers.