This paper considers robust inference in threshold regression models when the practitioners
do not know whether at the threshold point the true specification has a kink or a jump …

Group size and treatment intensity are understudied topics in mathematics intervention
research. This study examined whether the treatment intensity and overall intervention …

This paper proposes new nonparametric diagnostic tools to assess the asymptotic validity of
different treatment effects estimators that rely on the correct specification of the propensity …

The paper develops a systematic estimation and inference procedure for quantile regression
models where there may exist a common threshold effect across different quantile indices …

We propose design-based inference with finite mixture models (FMM) in settings where
heterogeneity cannot be addressed by more conventional modelling. In FMM, a model is …

This paper develops a threshold regression model where an unknown relationship between
two variables nonparametrically determines the threshold. We allow the observations to be …

Economic theory often predicts a “tipping point” effect due to multiple equilibria. Linear
threshold regressions estimate the “tipping point” by assuming at the same time that the …

We consider estimating multiple structural changes occurring at unknown common dates in
a panel data regression model with restrictions imposed on the coefficients. We establish the …

We study the link between the distribution of extreme operational losses and the economic
context, a fundamental task to compute adequate risk measures over time. In particular, we …

Group size and treatment intensity are understudied topics in mathematics intervention
research. This study examined whether the treatment intensity and overall intervention …