Emissions Trading, Electricity Industry Restructuring and Investment in Pollution Abatement

Conditional on β_n, the probability that a manager of a facility with T_n regulated

units makes the observed T_n compliance choices is:

_eβn'^xnU

Jnt
∑_eβJ^χ_njt
J=l

where i is a T_n * 1 dimensional vector denoting the sequence of observed choices.

Unconditional choice probabilities are derived by integrating conditional choice prob-
abilities over the distribution of unobserved random parameters (Train, 2003). The
θ vector of unknown parameters describes the distribution of β. The parameter esti-
mates are those that maximize the following log likelihood function:

ɪ 7 jⁿ   _eβ'χn<t

LL(θ) = ∑ln ∏_j ------f (β ∖θ)dβ,

n=l    t t=l       я'^

—ɔo     ∖ e^{β χ}njt

J=l

J_nt is the number of viable compliance alternatives available to unit t operated by

manager n. Because this integral does not have a closed form solution, the uncon-
ditional probabilities are approximated numerically through simulation. For each
decision maker, R draws of β are taken from the density f (β ∣θ)jone for each decision
maker. For each draw, the value of [7] is calculated for each decision maker. The
results are averaged across draws. Simulated maximum likelihood estimates of the
parameters maximize the following:.

To increase the accuracy of the simulation, 1000 pseudo-random Halton draws are

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