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Sample Fit Reliability

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  • Gabriel Okasa
  • Kenneth A. Younge

Abstract

Researchers frequently test and improve model fit by holding a sample constant and varying the model. We propose methods to test and improve sample fit by holding a model constant and varying the sample. Much as the bootstrap is a well-known method to re-sample data and estimate the uncertainty of the fit of parameters in a model, we develop Sample Fit Reliability (SFR) as a set of computational methods to re-sample data and estimate the reliability of the fit of observations in a sample. SFR uses Scoring to assess the reliability of each observation in a sample, Annealing to check the sensitivity of results to removing unreliable data, and Fitting to re-weight observations for more robust analysis. We provide simulation evidence to demonstrate the advantages of using SFR, and we replicate three empirical studies with treatment effects to illustrate how SFR reveals new insights about each study.

Suggested Citation

  • Gabriel Okasa & Kenneth A. Younge, 2022. "Sample Fit Reliability," Papers 2209.06631, arXiv.org.
    • Handle: RePEc:arx:papers:2209.06631
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    1. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2009. "Dealing with limited overlap in estimation of average treatment effects," Biometrika, Biometrika Trust, vol. 96(1), pages 187-199.
    2. Joshua D. Angrist & Jörn-Steffen Pischke, 2010. "The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 24(2), pages 3-30, Spring.
    3. Robert J. LaLonde, 1984. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," Working Papers 563, Princeton University, Department of Economics, Industrial Relations Section..
    4. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2008. "Nonparametric Tests for Treatment Effect Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 389-405, August.
    5. Jan H. Höffler, 2017. "Replication and Economics Journal Policies," American Economic Review, American Economic Association, vol. 107(5), pages 52-55, May.
    6. Nikolas Kuschnig & Gregor Zens & Jesús Crespo Cuaresma, 2021. "Hidden in Plain Sight: Influential Sets in Linear Models," CESifo Working Paper Series 8981, CESifo.
    7. Lechner, Michael, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," IZA Discussion Papers 12040, Institute of Labor Economics (IZA).
    8. Paul Goldsmith-Pinkham & Peter Hull & Michal Kolesár, 2024. "Contamination Bias in Linear Regressions," American Economic Review, American Economic Association, vol. 114(12), pages 4015-4051, December.
    9. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    10. Steven Lehrer & Tian Xie, 2017. "Box Office Buzz: Does Social Media Data Steal the Show from Model Uncertainty When Forecasting for Hollywood?," The Review of Economics and Statistics, MIT Press, vol. 99(5), pages 749-755, December.
    11. Matias Busso & John DiNardo & Justin McCrary, 2014. "New Evidence on the Finite Sample Properties of Propensity Score Reweighting and Matching Estimators," The Review of Economics and Statistics, MIT Press, vol. 96(5), pages 885-897, December.
    12. Hugo Bodory & Lorenzo Camponovo & Martin Huber & Michael Lechner, 2020. "The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 183-200, January.
    13. Leamer, Edward E, 1983. "Let's Take the Con Out of Econometrics," American Economic Review, American Economic Association, vol. 73(1), pages 31-43, March.
    14. JAMES G. MacKINNON, 2006. "Bootstrap Methods in Econometrics," The Economic Record, The Economic Society of Australia, vol. 82(s1), pages 2-18, September.
    15. Dean Karlan & John A. List, 2007. "Does Price Matter in Charitable Giving? Evidence from a Large-Scale Natural Field Experiment," American Economic Review, American Economic Association, vol. 97(5), pages 1774-1793, December.
    16. Manuela Angelucci & Dean Karlan & Jonathan Zinman, 2015. "Microcredit Impacts: Evidence from a Randomized Microcredit Program Placement Experiment by Compartamos Banco," American Economic Journal: Applied Economics, American Economic Association, vol. 7(1), pages 151-182, January.
    17. Gustavo Canavire-Bacarreza & Luis Castro Peñarrieta & Darwin Ugarte Ontiveros, 2021. "Outliers in Semi-Parametric Estimation of Treatment Effects," Econometrics, MDPI, vol. 9(2), pages 1-32, April.
    18. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    19. Michael Lechner & Anthony Strittmatter, 2019. "Practical procedures to deal with common support problems in matching estimation," Econometric Reviews, Taylor & Francis Journals, vol. 38(2), pages 193-207, February.
    20. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    21. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Causal Machine-Learning Approach," Papers 2103.10251, arXiv.org, revised Sep 2021.
    22. Iacus, Stefano M. & King, Gary & Porro, Giuseppe, 2011. "Multivariate Matching Methods That Are Monotonic Imbalance Bounding," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 345-361.
    23. Manuel Koller & Werner A. Stahel, 2017. "Nonsingular subsampling for regression S estimators with categorical predictors," Computational Statistics, Springer, vol. 32(2), pages 631-646, June.
    24. X Nie & S Wager, 2021. "Quasi-oracle estimation of heterogeneous treatment effects [TensorFlow: A system for large-scale machine learning]," Biometrika, Biometrika Trust, vol. 108(2), pages 299-319.
    25. Olive, David J. & Hawkins, Douglas M., 2007. "Behavior of elemental sets in regression," Statistics & Probability Letters, Elsevier, vol. 77(6), pages 621-624, March.
    26. James Berry & Lucas C. Coffman & Douglas Hanley & Rania Gihleb & Alistair J. Wilson, 2017. "Assessing the Rate of Replication in Economics," American Economic Review, American Economic Association, vol. 107(5), pages 27-31, May.
    27. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-620, September.
    28. Alexis Diamond & Jasjeet S. Sekhon, 2013. "Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 932-945, July.
    29. Rachael Meager, 2019. "Understanding the Average Impact of Microcredit Expansions: A Bayesian Hierarchical Analysis of Seven Randomized Experiments," American Economic Journal: Applied Economics, American Economic Association, vol. 11(1), pages 57-91, January.
    30. Meager, Rachael, 2019. "Understanding the average impact of microcredit expansions: a Bayesian hierarchical analysis of seven randomized experiments," LSE Research Online Documents on Economics 88190, London School of Economics and Political Science, LSE Library.
    31. Hubbard, Raymond & Vetter, Daniel E., 1996. "An empirical comparison of published replication research in accounting, economics, finance, management, and marketing," Journal of Business Research, Elsevier, vol. 35(2), pages 153-164, February.
    32. Richard Anderson & William Greene & B. D. McCullough & H. D. Vinod, 2008. "The role of data/code archives in the future of economic research," Journal of Economic Methodology, Taylor & Francis Journals, vol. 15(1), pages 99-119.
    33. Harrison, David Jr. & Rubinfeld, Daniel L., 1978. "Hedonic housing prices and the demand for clean air," Journal of Environmental Economics and Management, Elsevier, vol. 5(1), pages 81-102, March.
    34. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    35. Jason Abrevaya & Yu-Chin Hsu & Robert P. Lieli, 2015. "Estimating Conditional Average Treatment Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(4), pages 485-505, October.
    36. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    37. Abhijit Banerjee & Dean Karlan & Jonathan Zinman, 2015. "Six Randomized Evaluations of Microcredit: Introduction and Further Steps," American Economic Journal: Applied Economics, American Economic Association, vol. 7(1), pages 1-21, January.
    38. Leamer, Edward E, 1985. "Sensitivity Analyses Would Help," American Economic Review, American Economic Association, vol. 75(3), pages 308-313, June.
    39. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, July.
    40. Ankur Moitra & Dhruv Rohatgi, 2022. "Provably Auditing Ordinary Least Squares in Low Dimensions," Papers 2205.14284, arXiv.org, revised Jun 2022.
    41. DiCiccio, Cyrus J. & Romano, Joseph P. & Wolf, Michael, 2019. "Improving weighted least squares inference," Econometrics and Statistics, Elsevier, vol. 10(C), pages 96-119.
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