Linear probability model
In statistics, a linear probability model is a special case of a binomial regression model. Here the dependent variable for each observation takes values which are either 0 or 1. The probability of observing a 0 or 1 in any one case is treated as depending on one or more explanatory variables. For the "linear probability model", this relationship is a particularly simple one, and allows the model to be fitted by simple linear regression.
The model assumes that, for a binary outcome (Bernoulli trial), , and its associated vector of explanatory variables, ,^{[1]}
For this model,
and hence the vector of parameters β can be estimated using least squares. This method of fitting would be inefficient,^{[1]} and can be improved by adopting an iterative scheme based on weighted least squares,^{[1]} in which the model from the previous iteration is used to supply estimates of the conditional variances, , which would vary between observations. This approach can be related to fitting the model by maximum likelihood.^{[1]}
A drawback of this model is that, unless restrictions are placed on , the estimated coefficients can imply probabilities outside the unit interval . For this reason, models such as the logit model or the probit model are more commonly used.
References
Further reading
- Amemiya, Takeshi (1985). "Qualitative Response Models". Advanced Econometrics. Oxford: Basil Blackwell. pp. 267–359. ISBN 0-631-13345-3.
- Wooldridge, Jeffrey M. (2013). "A Binary Dependent Variable: The Linear Probability Model". Introductory Econometrics: A Modern Approach (5th international ed.). Mason, OH: South-Western. pp. 238–243. ISBN 978-1-111-53439-4.