摘要：The general method to solve the fixed point problem is a discretization
of observed state variables. When the observed state
variable is continuous, the required fixed point is in fact an infinite
dimensional object. Therefore, in order to solve the fixed point
problem, it is necessary to discretize the state space so that the
state variable takes on only finitely many values. But there are
limits regarding this method: (i) “curse of dimensionality”; (ii) the
limits it places on our ability to solve high-dimensional DP problems.
Despite these limits, this method have been used in many
literature. However, The discretization method may not be applicable
to computer replacement research to solve the fixed point
problem, because of the aforementioned problems. Using a detailed
data set on computer holdings by one of the world’s largest
telecommunication companies, this paper shows the effectiveness
of Parametric Approximation procedure by comparison with the
discretization method, which converts the contraction fixed-point
problem into a nonlinear least squares problem with combining
maximum likelihood estimation method to estimate the unknown
parameters.
关键词：Parametric Approximation; Discretization; Continuous
State Variables; Optimal replacement and Upgrade; DP
model; Nonlinear-Nested Fixed Point Algorithm (NLS-NFXP)
