This paper proposes a semiparametric approach to control for unobserved heterogeneity in linear regression models, based on an artificial neural network extremum estimator. We present a procedure to specify the model and use simulations to evaluate its finite sample properties in comparison to alternative methods. The simulations show that our approach is less sensitive to increases in the dimensionality and complexity of the problem. We also use the model to study convergence of per capita income across Brazilian municipalities.
|Original language||English (US)|
|Journal||Brazilian Review of Econometrics|
|State||Published - May 2015|
- Semiparametric models
- sieve extremum estimators
- neural networks
- unobserved components