Estimating installed-base effects in product adoption: Borrowing IVs from the dynamic panel data literature
- 주제(키워드) Dynamic panel data models , Installed-base effects , Product adoption
- 등재 SSCI, SCOPUS
- 발행기관 Elsevier Ltd
- 발행년도 2020
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000172276
- 본문언어 영어
- Published As https://dx.doi.org/10.1016/j.jocm.2020.100247
- 저작권 이화여자대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
Estimating installed-base effects for product adoption in the presence of unobserved heterogeneity is challenging since the typical solution of including fixed effects leads to inconsistent estimates in models with installed base. Narayanan and Nair (2013) highlight this problem and propose a bias correction method as a solution to the problem. This research note proposes an alternative solution: Borrowing IVs from the dynamic panel data literature. As lags and lagged differences of the installed base are used as instruments after first-differencing, this approach does not require external instruments and therefore has the key advantage of being easily accessible in many settings. I present Monte Carlo results to demonstrate the performance of the proposed approach. © 2020 Elsevier Ltd
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