Simple Estimators for Invertible Index Models
Bibliographic Data
| ID | 19420204 |
|---|---|
| Authors | Hyungtaik Ahn (Dongguk University), Hidehiko Ichimura (0000-0003-3525-3510, Faculty of Economics, University of Tokyo, Bunkyo, Tokyo 113-8654, Japan ()), James L Powell (0000-0002-8362-6863, Department of Economics, University of California, Berkeley, CA 94720 ()), Paul A Ruud (Vassar College) |
| Year | 2018 |
| Volume | 36 |
| Issue | 1 |
| Pages | 1-10 |
| Publication date | 2018-01-02 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2017.1379405 |
| OpenAlex | W2757797474 |
| Language | EN |
| References cited | 18 |
This article considers estimation of the unknown linear index coefficients of a model in which a number of nonparametrically identified reduced form parameters are assumed to be smooth and invertible function of one or more linear indices. The results extend the previous literature by allowing the number of reduced form parameters to exceed the number of indices (i.e., the indices are “overdetermined” by the reduced form parameters. The estimator of the unknown index coefficients (up to scale) is the eigenvector of a matrix (defined in terms of a first-step nonparametric estimator of the reduced form parameters) corresponding to its smallest (in magnitude) eigenvalue. Under suitable conditions, the proposed estimator is shown to be root-n-consistent and asymptotically normal, and under additional restrictions an efficient choice of a “weight matrix” is derived in the overdetermined case
Eigenvalues and eigenvectors · Estimator · Invertible matrix · Nonparametric statistics · Overdetermined system · Pure mathematics · Statistics · Mathematics · Matrix Theory and Algorithms · Random Matrices and Applications · Spatial and Panel Data Analysis · Applied Mathematics
| Citation velocity | historical |
|---|---|
| Highly cited | No |