background variables are introduced into a semiparametric model. A partially constrained Johnson SB distribution allowing …

parametric and semiparametric estimation methods are used to estimate the association of these factors with team adoption. A … assumption of the parametric probit model. The semiparametric estimates show that trade union density is not associated with team …

This paper formulates a likelihood-based estimator for a double index, semiparametric binary response equation. A novel … heteroscedasticity that depends on an index different from that underlying the “mean-response”. We show that such (multiplicative …) heteroscedasticity, whose form is not parametrically specified, effectively induces exclusion restrictions on the outcomes equation. The …

semiparametric general trimmed estimator (GTE) of truncated and censored regression, which is highly robust but relatively imprecise …

This paper is concerned with developing a semiparametric panel model to explain the trend in UK temperatures and other …

partof the model, we obtain the semiparametric efficiency bound. Our method isapplied to a bivariate stock index series. We …

used in applied econometric work. Nonparametric and semiparametric estimation methods are proposed to estimate the varying … the conduct of semiparametric regression with nonstationary data. The results include some new asymptotic theory for …

robust estimation of both long-run and short-run volatilities. Our estimation is semiparametric since the long-run volatility …