Linear probability model: Difference between revisions

Revision as of 08:56, 8 December 2007

Revision as of 08:25, 9 December 2007

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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, the [[logit model]] or the [[probit model]] are more commonly used.

One situation where the linear probability model is commonly used, is when the data set is so large that [[maximum likelihood]] estimation of a logit or probit model is computationally difficult. For the linear probability model, <math> E[Y|X] = \Pr(Y=1|X) =x'\beta</math>, so the parameter <math> \beta </math> can be estimated using [[lvvvvvvvvvveastleast squares]].

[[Category:Regression analysis]]