Production Function Estimation without Invertibility: Imperfectly Competitive Environments and Demand Shocks
Fuente:
arXiv
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909705017753600 |
|---|---|
| author | Doraszelski, Ulrich Li, Lixiong |
| author_facet | Doraszelski, Ulrich Li, Lixiong |
| contents | We advance the proxy variable approach to production function estimation. We show that the invertibility assumption at its heart is testable. We characterize what goes wrong if invertibility fails and what can still be done. We show that rethinking how the estimation procedure is implemented either eliminates or mitigates the bias that arises if invertibility fails. In particular, a simple change to the first step of the estimation procedure provides a first-order bias correction for the GMM estimator in the second step. Furthermore, a modification of the moment condition in the second step ensures Neyman orthogonality and enhances efficiency and robustness by rendering the asymptotic distribution of the GMM estimator invariant to estimation noise from the first step. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13520 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Production Function Estimation without Invertibility: Imperfectly Competitive Environments and Demand Shocks Doraszelski, Ulrich Li, Lixiong Econometrics We advance the proxy variable approach to production function estimation. We show that the invertibility assumption at its heart is testable. We characterize what goes wrong if invertibility fails and what can still be done. We show that rethinking how the estimation procedure is implemented either eliminates or mitigates the bias that arises if invertibility fails. In particular, a simple change to the first step of the estimation procedure provides a first-order bias correction for the GMM estimator in the second step. Furthermore, a modification of the moment condition in the second step ensures Neyman orthogonality and enhances efficiency and robustness by rendering the asymptotic distribution of the GMM estimator invariant to estimation noise from the first step. |
| title | Production Function Estimation without Invertibility: Imperfectly Competitive Environments and Demand Shocks |
| topic | Econometrics |
| url | https://arxiv.org/abs/2506.13520 |