References
- Akaike, H. 1969. Fitting autoregressive models for prediction. Annals of the institute of Statistical Mathematics 21 (1):243–47. doi: 10.1007/BF02532251.
- Berger, R. L., D. D. Boos, and F. M. Guess. 1988. Tests and confidence sets for comparing two mean residual life functions. Biometrics 44 (1): 103–15. doi: 10.2307/2531899.
- Bureau of Labor Statistics, U. S. D. o. L. 2016. Employment status of the civilian noninstitutional population, 1946 to date. https://www.bls.gov/cps/cpsaat01.xlsx (Accessed January 25, 2017).
- Cattaneo, M. D., B. R. Frandsen, and R. Titiunik. 2015. Randomization inference in the regression discontinuity design: An application to party advantages in the us senate. Journal of Causal Inference 3(1):1–24. doi: 10.1515/jci-2013-0010.
- Dimmery, D. 2016. rdd: Regression Discontinuity Estimation. R package version 0.57.
- Greene, W. H. 2003. Econometric analysis. 5th ed. Upper Saddle River, N.J.: Prentice Hall.
- Hansen, B. B. and A. Sales. 2015. Comment on cochran’s “observational studies”. Introduction to Observational Studies and the Reprint of Cochran’s paper “Observational Studies” and Comments, 184. doi: 10.1353/obs.2015.0017.
- Imbens, G. and K. Kalyanaraman. 2011. Optimal bandwidth choice for the regression discontinuity estimator. The Review of Economic Studies rdr043.
- Ivanov, V., L. Kilian, et al. 2005. A practitioner’s guide to lag order selection for var impulse response analysis. Studies in Nonlinear Dynamics and Econometrics 9 (1):1–34.
- Lee, D. S. and T. Lemieux. 2010. Regression discontinuity designs in economics. Journal of Economic Literature 48 (2):281–355. doi: 10.1257/jel.48.2.281.
- Li, F., A. Mattei, F. Mealli, et al. 2015. Evaluating the causal effect of university grants on student dropout: evidence from a regression discontinuity design using principal stratification. The Annals of Applied Statistics 9 (4):1906–31. doi: 10.1214/15-AOAS881.
- Liew, V. 2004. Which lag length selection criteria should we employ? Economics Bulletin 3 (33):1–9.
- Lindo, J. M., N. J. Sanders, and P. Oreopoulos. 2010. Ability, gender, and performance standards: Evidence from academic probation. American Economic Journal: Applied Economics 2 (2):95–117. doi: 10.1257/app.2.2.95.
- Lütkepohl, H. 2005. New introduction to multiple time series analysis. Springer-Verlag Berlin Heidelberg.
- Mallik, A., B. Sen, M. Banerjee, and G. Michailidis. 2011. Threshold estimation based on a p-value framework in dose-response and regression settings. Biometrika 98 (4):887. doi: 10.1093/biomet/asr051.
- McQuarrie, A. D. and C.-L. Tsai. 1998. Regression and time series model selection. Singapore: World Scientific.
- Nylund, K. L., T. Asparouhov, and B. O. Muthén. 2007. Deciding on the number of classes in latent class analysis and growth mixture modeling: A monte carlo simulation study. Structural equation modeling: A multidisciplinary Journal 14 (4):535–69. doi: 10.1080/10705510701575396.
- Pfaff, B. 2008. Analysis of Integrated and Cointegrated Time Series with R. 2nd ed. New York, NY: Springer.
- R Core Team. 2016. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
- Rao, C., Y. Wu, S. Konishi, and R. Mukerjee. 2001. On model selection. Lecture Notes-Monograph Series 1–64.
- Rosenbaum, P. R. 2008. Testing hypotheses in order. Biometrika 95 (1):248–52. doi: 10.1093/biomet/asm085.
- Sales, A. C. and B. B. Hansen. 2020. Limitless regression discontinuity. Journal of Educational and Behavioral Statistics 45 (2):143–74. doi: 10.3102/1076998619884904.
- Schwarz, G. et al. 1978. Estimating the dimension of a model. The Annals of Statistics 6 (2):461–64. doi: 10.1214/aos/1176344136.
- Shao, C., J. Li, and Y. Cheng. 2016. Detection of test speededness using change-point analysis. Psychometrika 81 (4):1118–41. doi: 10.1007/s11336-015-9476-7.
- Thistlethwaite, D. L. and D. T. Campbell. 1960. Regression-discontinuity analysis: An alternative to the ex post facto experiment. Journal of Educational Psychology 51 (6):309–17. doi: 10.1037/h0044319.
- Vogt, M. and H. Dette. 2015. Detecting gradual changes in locally stationary processes. The Annals of Statistics 43 (2):713–40. doi: 10.1214/14-AOS1297.
- Vuong, Q. H. 1989. Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica: Journal of the Econometric Society 307–33. doi: 10.2307/1912557.
- Zeileis, A. 2004. Econometric computing with HC and HAC covariance matrix estimators. Journal of Statistical Software 11 (10):1–17. doi: 10.18637/jss.v011.i10.
- Zeileis, A. 2006. Object-oriented computation of sandwich estimators. Journal of Statistical Software 16 (9):1–16. doi: 10.18637/jss.v016.i09.
- Zeileis, A., S. Köll, and N. Graham. 2020. Various versatile variances: An object-oriented implementation of clustered covariances in R. Journal of Statistical Software 95 (1):1–36. doi: 10.18637/jss.v095.i01.