Testing semiparametric conditional moment restrictions using conditional martingale transforms
This paper studies conditional moment restrictions that contain unknown nonparametric functions, and proposes a general method of obtaining asymptotically distribution-free tests via martingale transforms. Examples of such conditional moment restrictions are single index restrictions, partially parametric regressions, and partially parametric quantile regressions. This paper introduces a conditional martingale transform that is conditioned on the variable in the nonparametric function, and shows that we can generate distribution-free tests of various semiparametric conditional moment restrictions using this martingale transform. The paper proposes feasible martingale transforms using series estimation and establishes their asymptotic validity. Some results from a Monte Carlo simulation study are presented and discussed.
Year of publication: |
2010
|
---|---|
Authors: | Song, Kyungchul |
Published in: |
Journal of Econometrics. - Elsevier, ISSN 0304-4076. - Vol. 154.2010, 1, p. 74-84
|
Publisher: |
Elsevier |
Keywords: | Semiparametric models Conditional moment restrictions Martingale transform Asymptotically distribution-free tests |
Saved in:
Saved in favorites
Similar items by person
-
Bootstrapping semiparametric models with single-index nuisance parameters
Song, Kyungchul, (2010)
-
Efficient estimation of average treatment effects under treatment-based sampling
Song, Kyungchul, (2010)
-
Testing semiparametric conditional moment restrictions using conditional martingale transforms
Song, Kyungchul, (2010)
- More ...