Abstract
Others have developed average derivative estimators of the parameter β in the model E(Y|X = x) = G(xβ), where G is an unknown function and X is a random vector. These estimators are noniterative and easy to compute but require that X be continuously distributed. This article develops a noniterative, easily computed estimator of β for models in which some components of X are discrete. The estimator is n½ consistent and asymptotically normal. An application to data on product innovation by German manufacturers illustrates the estimator's usefulness.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1632-1640 |
| Number of pages | 9 |
| Journal | Journal of the American Statistical Association |
| Volume | 91 |
| Issue number | 436 |
| DOIs | |
| State | Published - Dec 1 1996 |
Funding
Joel L. Horowitz is Professor, Department of Economics, University of Iowa, Iowa City, IA 52242. Wolfgang Hardle is Professor, Institute for Statistics and Econometrics, Humboldt University, 10178 Berlin, Germany. This research was supported in part by Deutsche Forschungsgemeinschaft, Sonderforschungsbereich 373, "Quantifikation und Simulation Oknonomischer Prozesse." The research of Joel L. Horowitz was supported in part by National Science Foundation grants DMS·9208820 and SBR-9307677. The authors thank N. E. Savin for comments on this research.
Keywords
- Average derivative estimation
- Index model
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
