Likelihood function of a Linear probability model What is the Likelihood function of a linear probability model?
I know the likelihood function is the joint probability density, but how to construct the likelihood function when we only have the probability $P(Y_i=1|X_i)$ and $P(Y_i=0|X_i)$?
 A: In your example you simply don't have a likelihood function, because you defined just a probability model rather than a statistical one. If you know the probabilities 
$$
P(Y_i = 1 | X_i) = p_i \quad \mbox{and} \quad P(Y_i =0| X_i) = 1-p_i, 
$$ then you have a "unique" conditional probability model. First you have to understand the differences between probability and statistical models. Please, see this post.
In order to have a statistical model, the above probabilities must be unknown. Typically, some relation is imposed: 
$$
P_\theta(Y_i = 1 | X_i=x_i) = p_i(\theta),$$
where $\theta$ is the unknown parameter vector and $p_i(\theta) \in [0,1]$ for $i=1, \ldots, n$. Now, we have a parametric statistical model, since for each $\theta$ we have a probability model.
The likelihood function is
$$
L(\theta) = \prod_{i=1}^n P_\theta(Y_i = y_i | X_i=x_i) = \prod_{i=1}^n p_i(\theta)^{y_i}(1-p_i(\theta))^{1-y_i}.
$$ 
Usually, the shape of $p_i(\theta)$ is commonly specified as:


*

*$p_i(\theta) = \frac{\exp(\eta_i(\theta))}{1 + \exp(\eta_i(\theta))},$

*$p_i(\theta) = F(\eta_i(\theta)),$


where $\eta_i(\theta) = \alpha + \beta x_i$ and $F$ is a cumulative distribution function. You can find a good shape for $p_i$ by looking at the data behavior (plots, data dispersion and so forth).
A: The linear probability model (LPM) is $$\mathbb{P}(Y_i=1|X_i)=\beta_0+\beta_1X_i$$ with $Y_i$ a dummy random variable, and $X_i$ a random variable.  If $X_i$ is instead a vector of random variables, the LPM is $$\mathbb{P}(Y_i=1|X_i)=\beta_0+X'_i\beta.$$ But let me continue with the simple case where $X_i$ is a random variable. It follows that $$\mathbb{P}(Y_i=0|X_i)=1-\mathbb{P}(Y_i=1|X_i)=1-\beta_0-\beta_1X_i.$$ Thus, for $y_i\in\{0,1\}$,$$\begin{align*}\mathbb{P}(Y_i=y_i|X_i)&=\mathbb{P}(Y_i=1|X_i)^{y_i}\mathbb{P}(Y_i=0|X_i)^{1-y_i}\\&=(\beta_0+\beta_1X_i)^{y_i}(1-\beta_0-\beta_1X_i)^{1-y_i}.\end{align*}$$ Now, if $\{(Y_i, X_i)\}_{i=1}^n$ is a random sample of $n\geq 1$ observations, the likelihood function is $$\begin{align*}\mathbb{P}(Y_1=y_1,Y_2=y_2,\ldots,Y_n=y_n|X_1,X_2,\ldots,X_n)&=\prod_{i=1}^n\mathbb{P}(Y_i=y_i|X_i)\\&=\prod_{i=1}^n(\beta_0+\beta_1X_i)^{y_i}(1-\beta_0-\beta_1X_i)^{1-y_i}.\end{align*}$$ Note that since probabilities are in $[0,1]$ we assume that $\beta_0+\beta_1 X_i$ is in $[0,1]$ for $i=1,\ldots,n$. In practice difficulties may occur when fitting the LPM because during the fitting process, fitted values may fall outside $[0,1]$ for some observed $X_i=x_i$. 
