TY - JOUR
T1 - Estimating installed-base effects in product adoption
T2 - Borrowing IVs from the dynamic panel data literature
AU - Park, Minjung
N1 - Funding Information:
Park gratefully acknowledges the support provided by the National Research Foundation of Korea (NRF) Grant 2018S1A5A2A01029529 . The work was partly conducted when the author was an assistant professor at the Haas School of Business, University of California, Berkeley. I greatly thank three anonymous reviewers and the associate editor for their insightful comments and highly constructive suggestions. The usual disclaimer applies.
Funding Information:
Park gratefully acknowledges the support provided by the National Research Foundation of Korea (NRF) Grant 2018S1A5A2A01029529. The work was partly conducted when the author was an assistant professor at the Haas School of Business, University of California, Berkeley. I greatly thank three anonymous reviewers and the associate editor for their insightful comments and highly constructive suggestions. The usual disclaimer applies.
Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/12
Y1 - 2020/12
N2 - 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.
AB - 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.
KW - Dynamic panel data models
KW - Installed-base effects
KW - Product adoption
UR - http://www.scopus.com/inward/record.url?scp=85089269530&partnerID=8YFLogxK
U2 - 10.1016/j.jocm.2020.100247
DO - 10.1016/j.jocm.2020.100247
M3 - Article
AN - SCOPUS:85089269530
SN - 1755-5345
VL - 37
JO - Journal of Choice Modelling
JF - Journal of Choice Modelling
M1 - 100247
ER -