Truncated regression model

Truncated regression models' commonly arise in econometrics in cases where where the variable of interest is bounded (Normally, bounded below by zero). The most common example is labor supply. A labor supply model estimates the relationship between hours worked and characteristics of employees such as age, education and family status. However, such estimates, undertaken using linear regression will include negative values, while the observed values are bounded below by zero. The coefficient estimates derived from linear regression are biased, and the problem is not resolved by excluding the zero values, or by setting negative values to zero.

Methods used to resolve this problem are referred to as truncated regression model. The most commonly used truncated regression model in econometrics is the Tobit model due to James Tobin.

A related case is that of censored regression models where data for the zero observations is not available.