This paper examines inference on regressions when interval data are available on one variable, the other variables being measured precisely. Let a population be characterized by a distribution "P"("y", "x", "v", "v"-sub-0, "v"-sub-1), where "y" is an element of "R"-super-1, "x" is an element of...

We propose two semiparametric versions of the debiased Lasso procedure for the model $Y_{i}=X_{i}\beta_{0} g_{0}(Z_{i}) \varepsilon_{i}$, where the parameter vector of interest $\beta_{0}$ is high dimensional but sparse (exactly or approximately) and $g_{0}$ is an unknown nuisance function. Both...

We consider the problem of constructing confidence intervals (CIs) for a linear functional of a regression function, such as its value at a point, the regression discontinuity parameter, or a regression coefficient in a linear or partly linear regression. Our main assumption is that the...

We consider the problem of constructing honest confidence intervals (CIs) for a scalar parameter of interest, such as the regression discontinuity parameter, in nonparametric regression based on kernel or local polynomial estimators. To ensure that our CIs are honest, we derive and tabulate...

We consider the problem of constructing confidence intervals (CIs) for a linear functional of a regression function, such as its value at a point, the regression discontinuity parameter, or a regression coefficient in a linear or partly linear regression. Our main assumption is that the...

We consider the problem of constructing honest confidence intervals (CIs) for a scalar parameter of interest, such as the regression discontinuity parameter, in nonparametric regression based on kernel or local polynomial estimators. To ensure that our CIs are honest, we derive novel critical...