英语翻译Alternative specifications of qualitative choice modelsa

英语翻译
Alternative specifications of qualitative choice models
are based on the probit and the logit models (Greene,
1998).These models are used to denote a regression model
in which the dependent variable is a binary variable taking
the value of 1 if the event occurs and 0 otherwise.The
distinctiveness of the probit and logit models is that the
predicted value of the dependent variable is interpreted as
the probability that the event happens.The difference
between both models can be found in the distribution
function associated with each model.
In principle,because logit uses the binomial distribution
and probit uses the cumulative normal distribution,one
should use logit if one assumes the categorical dependent
variable reflects an underlying qualitative variable and use
probit if one assumes the dependent reflects an underlying
quantitative variable.However,in practice,these alternative
assumptions rarely make a difference in the
conclusions,which will be the same for both logit and
probit under most circumstances (Ham,Brown,& Jang,
2004).
In any case,when the dependent variable can take more
than one value (not only 0 and 1),but it remains discrete
and bounded,the generalization of the simple logit and
probit models is required.In this way,Greene (1998)
proposes two alternatives.The first one is the so-called
multinomial model which main distinctiveness is that the
dependent variable is a discrete and bounded variable
taking values of 1,2,3,4,5,etc.However,although
multinomial models are applicable to categorical data,
they cannot capture the order that the dependent variable’s
alternative choice follows.For this reason in this study
the second alternative that proposes ordered logit and
probit models has been chosen because,as well as
taking the bounded,discrete nature of the dependent
variable into account,they also take its order into
consideration.
With these models,the implicit initial supposition is that
the latent variable of opinion ðOiÞ is a linear function of
explanatory variables xi and di:
where \2 is a random error and bx and bd are vectors of
parameters to be estimated.
The observed category Li is based on Oi,in accordance
with the following rule:

定性选择模型的其它的说明基于probit 和logit 模型(格林,1998).这些模型用来指示一个回归模型,如果事件发生,因变数是花费1的价值的一个二进制的变量和0不然.probit 和logit 模型的区别性是因变数的被预言的价值被解释为事件发生的可能性.在两个模型之间的差别可能被在与每个模型相关的分布函数里发现.原则上,因为logit使用二项分布,probit使用累积的正规分布,如果一个人以为无条件因变数反映出一个基础的定性变量,一个人应该使用logit 并且如果一个人以为依靠反映出一个基础的定量变量,使用probit.不过,实际上,这些其它的假定很少在结论里产生影响,这将对logit和在大多数情形(火腿,布朗和Jang,2004)下的probit是一样的.
无论如何,当因变数能花费超过一价值(不仅0和1)时,但是它保持分离并且被束,简单的logit 和probit 模型的概括被要求.以这种方法,格林(1998)提出两个选择.第一个所谓哪主要区别性是是那些因变数的多项模特一分离和镶几变量花费价值 1,2,3,4,5,等等 不过,虽然多项的模型适用于分类数据,但是他们不能捕获因变数的其它的选择跟随的命令.因此在这研究内,提议预订的logit 和probit 模式的第2 选择已经被选择因为,以及带被凝固,分离的自然 进账户的因变数,他们也把它的订货考虑进去.随着这些模特,暗示最初想象是潜在意见：
在哪里在随机误差和bx 和bd在要被估计的矢量的参数.根据下列规章,被观察的种类李基于Oi：