When confronting an economic model with data, one usually encounters a situation where some important variables, such as tastes and productivity that appear in the model in nonadditive, nonseparable ways, are unobserved. Rather than transforming the model into one where the unobservables enter in separable ways, the nonseparable approach considers identification and estimation of the original model. The original model satisfies the economic restrictions of the model, which aid in identification and estimation. These restrictions are often lost in separable transformations.
This masterclass will cover identification and estimation methods for nonseparable models, with emphasis on nonparametric methods. First, some key econometric techniques used in nonseparable models will be presented. Next, it will be shown how these techniques have been used and extended to study particular econometric models.