Panel Dataset Analysis with Fixed Effects and Lags

Richard J. Butler, Matthew J. Butler and Barbara L. Wilson

Chapter 12 in Advanced Statistics for Health Research, 2023, pp 203-214 from World Scientific Publishing Co. Pte. Ltd.

Abstract: Both papers considered in this chapter (Mark et al., 2004; Aakvik and Holmas, 2006) focus on the impact of healthcare providers on patient outcome, measured as hospital mortality rates (and various morbidity rates) in the first paper and as community mortality rates in the second paper. The treatment variables — right-hand side variables of interest — are, respectively, the nurse to patient ratio in hospitals, and the general healthcare provider/practitioner to municipal population ratio in the second paper. Among the possible techniques to get causal estimates given the authors’ data, random assignments of treatments are not feasible (from several politico-economic perspectives), neither are regression discontinuity designs. Matching on the basis of medical professional/exposed population is also rather difficult for these datasets. Hence, these papers rely heavily on fixed effects in panel datasets, and lagged outcomes in panel datasets as control variables, and instrumental variables to handle the problem using the lagged outcome as a predictor variable. The Arellano and Bond (1991) method is the generalized method of moments estimator employed for this purpose in each of the papers reviewed in this chapter…

Keywords: Nursing; Physician; Healthcare; Public Health; Regression; Orthogonal Projection; Geometric View of Causal Inference; 2SLS; Two-Stage Least Squares; Instrumental Variables; Probits; Logits; Proportional Hazards; Cox Regressions; Quantile Regression; Random Forest Regression; Randomization; Matching; Propensity-Score; Differences-in-Differences; Regression Discontinuity; Fixed Effects; R; SAS; STATA; Research Examples; Empirical Rule; Applied Statistics; Confidence Intervals; VIF; Standard Beta; Histograms; Scatterplots; Regression Gini Index; COVID-19; Gender Wage Differentials; LASSO; Area Under The Curve; AUC; ROC; Decision Trees; Maximum Likelihood; GMM; Data Generating Process; Split Sample Instrumental Variables; Local Average Treatment Effects (LATE); Data Visualization; Omitted Variable Bias; Simultaneous Equations; Measurement Error; Supervised Machine Learning; Panel Data; RGI; Health Professionals (search for similar items in EconPapers)
JEL-codes: C1 I11 I15 (search for similar items in EconPapers)
Date: 2023
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