An Iterative Approach to Estimation with Multiple High-Dimensional Fixed Effects

نویسندگان

  • Siyi Luo
  • Wenjia Zhu
  • Randall P. Ellis
  • Iván Fernández-Val
چکیده

We develop a new estimation algorithm for models with multiple high-dimensional fixed effects and unbalanced panels. By Frisch-Waugh-Lovell Theorem, our algorithm absorbs fixed effects iteratively until they are asymptotically eliminated. Monte Carlo simulations show that our approach matches results from estimation with fixed effect dummies. Applying the algorithm to US employer-based health insurance data, we analyze health care utilization of 63 million individual-months with fixed effects for 1.4 million individuals, 150,000 primary care physicians, 3,000 counties, 465 employer*year*single/family coverage types and 47 months. We find that narrow network plans reduce the probabilities of monthly visits relative to preferred provider organizations.

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تاریخ انتشار 2017