One of the most cited studies within the field of binary choice models is that of Klein and Spady (1993), in which the authors propose an estimator that is not only non-parametric with respect to the choice density but also asymptotically efficient. However, while theoretically appealing, the...

One of the most cited studies within the field of binary choice models is that of Klein and Spady (1993), in which the authors propose an estimator that is not only non-parametric with respect to the choice density but also asymptotically efficient. However, while theoretically appealing, the...

This paper introduces and analyses a setting with general heterogeneity in regression modelling. It shows that regression models with fixed or time-varying parameters can be estimated by OLS or time-varying OLS methods, respectively, for a very wide class of regressors and noises, not covered by...

In this paper, we explore a static setting for the assessment of risk in the context of mathematical finance and actuarial science that takes into account model uncertainty in the distribution of a possibly infinite-dimensional risk factor. We study convex risk functionals that incorporate a...

This paper explores a semiparametric version of a time-varying regression, where a subset of the regressors have a fixed coefficient and the rest a time-varying one. We provide an estimation method and establish associated theoretical properties of the estimates and standard errors in extended...

In this paper, we explore a static setting for the assessment of risk in the context of mathematical finance and actuarial science that takes into account model uncertainty in the distribution of a possibly infinite-dimensional risk factor. We study convex risk functionals that incorporate a...