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.

在存在未观察到的异质性的情况下估计产品采用的已安装基准效果具有挑战性，因为包括固定效果的典型解决方案会导致已安装基准模型中的估计不一致。Narayanan和Nair（2013）强调了这个问题，并提出了一种偏倚校正方法作为该问题的解决方案。本研究报告提出了另一种解决方案：从动态面板数据文献中借用IV。由于先差分后将已安装基础的滞后和滞后差异用作仪器，因此该方法不需要外部仪器，因此具有在许多设置中均可轻松访问的关键优势。我介绍了蒙特卡洛的结果，以证明所提出方法的性能。