State Investments in Psychiatric Innovation: Investigating Unmeasured State Factors.
We apply three separate panel data estimation methods to examine the diffusion of technologies at the state-level. These methods include the Hausman-Taylor random effects model, the fixed effects vector decomposition (FEVD), and generalized estimating equations (GEE). We discuss the assumptions required of each and assess the stability of our policy results across the three models for a longitudinal study of the diffusion of newer psychotropic technologies. We find a reasonable level of consistency among marginal effects for time varying independent variables between our three estimation methods but some discrepancy in the estimated measure of precision in our empirical application. We find a number of policy conclusions are quite stable across estimation methods and may be of interest to state-level mental health policy decision makers.
Department of Health Policy and Management, UNC Gillings School of Global Public Health ; Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill.
Marisa Elena
ME
Domino
Department of Health Policy and Management, UNC Gillings School of Global Public Health ; Center for Health Services Research in Primary Care, Department of Veterans Affairs.
Christopher Alan
CA
Beadles
Fixed effects; Innovation; Panel data; Random effects; State factors