We use a regression discontinuity design and difference-in-differences estimators to estimate the impact of a one-shot hiring subsidy for low-educated unemployed youths during the Great Recession recovery in Belgium. The subsidy increases job-finding in the private sector by 10 percentage points...

This paper proposes a novel semiparametric time-varying model for long-horizon predictive regressions in which the coefficients are allowed to change over time with unspecified functional forms. A linear projection method is employed to deal with the embedded endogeneity issue. We pursue an...

Machine learning and deep learning (DL)-based forecasting models have shown excellent predictive performances, but they require a large amount of data for model construction. Insufficient data can be augmented using generative adversarial networks (GANs), but these are not effective for...

In this paper, we propose the Fama-Macbeth (FM) regression with sparsity, which is named FM-LASSO. We impose a l2 penalty across time to make sure that the factors are sparse. The factors in our model are shrinked with consistency. If a factor is 0 once, then it is always 0. If a factor is not...

This study re-examines the effects of monetary policy using Japanese data and a new approach, the shift-share regressor and instrumental variables, which has not been used in previous vector autoregression analyses. We find that consumption and investment respond negatively to interest rates,...

A correlation between regressors and disturbances presents challenging problems in linear regression. Issues like omitted variables, measurement error and simultaneity render ordinary least squares (OLS) biased and inconsistent. In the context of heteroscedastic linear regression, this note...

South East Asia’s forests are not only important for global carbon storage and biodiversity conservation but also for the local socio-economy. Forest management decisions require information about the economic values of the forest ecosystem services, relating to matters of human welfare and...

We develop a distribution regression model under endogenous sample selection. This model is a semi-parametric generalization of the Heckman selection model. It accommodates much richer effects of the covariates on outcome distribution and patterns of heterogeneity in the selection process, and...