Current Research Projects
Learning about Selection: Improved Estimation through AI
Machine learning techniques are utilized in this paper to improve upon the selection correction procedure of Dahl (2002).Variable selection methods and prediction using Neural Network modeling are used to relax the strong assumption required for identification in Dahls' contribution and to improve the empirical implementation of the procedure. Through a Monte Carlo experiment I show that this method is preferable except where researchers have strong a priori reasons to suspect that the single index sufficiency assumption holds. Machine learning methods combined with the insights of Lee (1983) can therefore be used to effectively solve for the curse of dimensionality problem without the imposition of overly strong distributional assumptions.
Entrepreneurship, Outside Options and Constrained Efficiency joint with João Fonseca
Since the seminal paper of Pissarides (1985), the literature on search frictions has often adopted the assumption of free entry. In this paper we forgo of this restriction by proposing a more realistic framework in which individuals are constantly making the decision whether or not to open a firm. Namely, firms are created through endogenous choices and business-owners and workers are drawn from the same pool. We show that in this framework, the Nash bargaining parameter is crucial for internal dynamics. In particular, workers and business owners share the same outside-options. As a result, the wage is no longer unambiguously positively related to the value of unemployment. The constrained effcient solution to this model takes the same form as the standard search model implying the same form for the Hosios condition. However, at this efficient solution changes in the rate of unemployment are either exacerbated or muted conditional on the value of the match elasticity parameter.