The name is absent

NLMS model and form (15), related to each other by a nonlinear transformation, should be
clearly distinguished.

Despite this, it is impossible to make a distinction between the two MS functions in
an empirical approach called “joint analysis of risk preference structure and technology” that
has been recently employed in the field of production economics under uncertainty (e.g.,
Saha 1997; Abdulkadri, Langemeier and Featherstone 2003; Nakashima 2006). This is
discussed below. Joint analysis utilizes the first-order conditions resulting from the
optimization of production model to estimate the structural parameters that indicate agent
risk preference and production technology. The first-order conditions based on MS approach
are generally written as

(16)                            ^dμ~ - S(σ, μ^d = 0 (Xi = 1,2,∙,n),

∂x_i        ∂x_i

where χ_i (i = 1,2, ∙∙∙,n) denote the endogenous variables of the underlying economic model.

Then, the specifications of S (σ, μ), ^dμ and ^dσ- follow. The specification of S (σ, μ) is, of
∂x_i        ∂x_i

course, determined by the form of MS function that represents agent’s attitude toward
random payoff, while the specifications of ^dμ- and ^dσ- depend on the remaining factors of
∂x_i      ∂x_i

the model such as a random factor involved in the model (e.g., price uncertainty or yield
uncertainty) and a functional form chosen to represent technological constraint (e.g.,
production function or cost function). In the procedure for developing a joint analysis model,
special attention needs to be paid to the specification of S (σ, μ). Because MS function is
represented merely by the slope of indifference curve, the difference of MS functions such as

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