The current economic crisis requires fast information to predict economic behavior early, which is difficult at times of structural changes. This paper suggests an innovative new method of using data on internet activity for that purpose. It demonstrates strong correlations between keyword...

We propose various semiparametric estimators for nonlinear selection models, where slope and intercept can be separately identifed. When the selection equation satisfies a monotonic index restriction, we suggest a local polynomial estimator, using only observations for which the marginal...

Nowcasting has been a challenge in the recent economic crisis. We introduce the Toll Index, a new monthly indicator for business cycle forecasting and demonstrate its relevance using German data. The index measures the monthly transportation activity performed by heavy transport vehicles across...

Unemployment is notoriously difficult to predict. In previous studies, once country and year fixed effects are added to panel estimates, few variables predict changes in unemployment rates. Using panel data for 29 European countries collected by the European Commission over 444 months between...

This paper is the first attempt to directly explore the long-run nonlinearity of the shadow economy. Using a dataset of 158 countries over the period from 1996 to 2015, our results reveal a robust U-shaped relationship between the shadow economy size and GDP per capita. Our results imply that...

We use income satisfaction data in order to estimate equivalence scales. Our method differs from previous attempts to use satisfaction data for this purpose in that it can be used to estimate or evaluate any given parametric equivalence scale. It can also be employed to investigate specific...

This study demonstrates that nonlinearities, coupled with worker heterogeneity, make it possible to reconcile the Diamond-Mortensen-Pissarides model with the labor market dynamics observed in the United States. Nonlinearities, induced by firings and downward real wage rigidities, magnify...