Summary
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.
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| Scientometrics | |
|---|---|
| Language | English |
| Size | B5 |
| Year of Foundation |
1978 |
| Volumes per Year |
1 |
| Issues per Year |
12 |
| Founder | Akadémiai Kiadó |
| Founder's Address |
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245. |
| Publisher | Akadémiai Kiadó Springer Nature Switzerland AG |
| Publisher's Address |
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245. CH-6330 Cham, Switzerland Gewerbestrasse 11. |
| Responsible Publisher |
Chief Executive Officer, Akadémiai Kiadó |
| ISSN | 0138-9130 (Print) |
| ISSN | 1588-2861 (Online) |
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