This note attempts to approximate the distribution function for the number of innovation activities (NIA) in the manufacturing
sector using the dataset of 2002 Korean Innovation Survey. The mixture model applied here can easily capture the bimodality
feature of the NIA distribution and provide some useful information such as the mean of NIA and the effect of a firm’s characteristic
on whether the firm will undertake innovation activity.
Modeling firms' R&D expenditures often become complicated due to the zero values reported by a significant number of firms.
The maximum likelihood (ML) estimation of the Tobit model, which is usually adopted in this case, however, is not robust to
heteroscedastic and/or non-normal error structure. Thus, this paper attempts to apply symmetrically trimmed least squares
estimation as a semi-parametric estimation of the Tobit model in order to model firms' R&D expenditures with zero values.
The result of specification test indicates the semi-parametric estimation outperforms the parametric ML estimation significantly.