The standard approach to the estimation of unemployment persistence assumes that quantile parameter heterogeneity does not matter. Using panel quantile autoregression techniques on state-level data for the United States (1980-2010), we suggest that it does.

We use Machine Learning (ML) predictive tools to propose a policy-assignment rule designed to increase the effectiveness of public guarantee programs. This rule can be used as a benchmark to improve targeting in order to reach the stated policy goals. Public guarantee schemes should target firms...

Machine Learning (ML) can be a powerful tool to inform policy decisions. Those who are treated under a programme might have different propensities to put into practice the behaviour that the policymaker wants to incentivize. ML algorithms can be used to predict the policy-compliers; that is,...

The standard approach to the estimation of unemployment persistence assumes that quantile parameter heterogeneity does not matter. Using panel quantile autoregression techniques on state-level data for the United States (1980-2010), we suggest that it does.