Advertisement

Before and After Study Designs

  • Heather Brown
Chapter

Abstract

In this chapter, we investigate three methods for estimating quasi-experimental models: (1) Interrupted Time Series; (2) Regression Discontinuity Approach; (3) Difference in Difference. We provide examples and a step-by-step guide to show how to estimate these different types of model specifications. We outline how to interpret the results from these different models in light of the underlying assumptions, and what this means for drawing conclusions on causality.

Keywords

Interrupted Time Series Regression Discontinuity Approach Difference in Difference 

References and Further Reading

Interrupted Time Series

  1. Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: A tutorial. International Journal of Epidemiology, 46(1), 348–355.Google Scholar
  2. Kontopantelis, E., Doran, T., Springate, D. A., Buchan, I., & Reeves, D. (2015). Regression based quasi-experimental approach when randomisation is not an option: Interrupted time series analysis. BMJ, 350, h2750.CrossRefGoogle Scholar

Regression Discontinuity

  1. Imbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142(2), 615–635.CrossRefGoogle Scholar
  2. Jacob, R., Zhu, P., Somers, M. A., & Bloom, H. (2012). A practical guide to regression discontinuity. MDRC.Google Scholar
  3. Lee, D. S., & Lemieux, T. (2010). Regression discontinuity designs in economics. Journal of Economic Literature, 48, 281–355.CrossRefGoogle Scholar

Difference in Difference

  1. Cameron, A. C., & Miller, D. L. (2015). A practitioner’s guide to cluster-robust inference. Journal of Human Resources, 50(2), 317–372.CrossRefGoogle Scholar
  2. Hall, P., Horowitz, J. L., & Jing, B. Y. (1995). On blocking rules for the bootstrap with dependent data. Biometrika, 82(3), 561–574.CrossRefGoogle Scholar
  3. Heckman, J. J., & Smith, J. A. (1999). The pre-programme earnings dip and the determinants of participation in a social programme. Implications for simple programme evaluation strategies. The Economic Journal, 109(457), 313–348.CrossRefGoogle Scholar
  4. Kloek, T. (1981). OLS estimation in a model where a microvariable is explained by aggregates and contemporaneous disturbances are equicorrelated. Econometrica: Journal of the Econometric Society, 49(1), 205–207.CrossRefGoogle Scholar
  5. Moulton, B. R. (1986). Random group effects and the precision of regression estimates. Journal of Econometrics, 32(3), 385–397.CrossRefGoogle Scholar
  6. Rosenbaum, P. R. (1987). Sensitivity analysis for certain permutation inferences in matched observational studies. Biometrika, 74(1), 13–26.CrossRefGoogle Scholar
  7. Wooldridge, J. (2007). What’s new in econometrics? Lecture 10 difference-in-differences estimation. NBER Summer Institute. Retrieved October 9, 2017, from www.nber.org/WNE/Slides7–31–07/slides_10_diffindiffs.pdf

Copyright information

© The Author(s) 2018

Authors and Affiliations

  • Heather Brown
    • 1
  1. 1.Newcastle UniversityNewcastle upon TyneUK

Personalised recommendations