Linear probability model: Difference between revisions

Revision as of 03:18, 6 March 2016

Revision as of 01:40, 7 August 2016

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For this model,

:<math> E[Y|X] = \Pr(Y=1|X) =x'\beta,</math>

and hence the vector of parameters β can be estimated using [[least squares]]. This method of fitting would be inefficient.,<ref name=Cox /> This method of fittingand can be improved by adopting an iterative scheme based on [[weighted least squares]],<ref name=Cox/> in which the model from the previous iteration is used to supply estimates of the conditional variances, <math>\operatorname{Var}(Y|X=x)</math>, which would vary between observations. This approach can be related to fitting the model by [[maximum likelihood]].<ref name=Cox/>

A drawback of this model is that, unless restrictions are placed on <math> \beta </math>, the estimated coefficients can imply probabilities outside the [[unit interval]] <math> [0,1] </math>. For this reason, models such as the [[logit model]] or the [[probit model]] are more commonly used.