Inference on Winners (with Isaiah Andrews and Toru Kitagawa), conditionally accepted at Quarterly Journal of Economics
2019 Version (referenced in "Inference After Estimation of Breaks")
Hybrid Confidence Intervals for Informative Uniform Asymptotic Inference After Model Selection, accepted at Biometrika
Short and Simple Confidence Intervals when the Directions of Some Effects are Known (with Philipp Ketz), accepted at Review of Economics and Statistics
Stata code available from SSC archive: type "ssc install ssci"
Inference for Losers (with Isaiah Andrews, Dillon Bowen and Toru Kitagawa), American Economic Association Papers and Proceedings, 112 (2022), 635-640.
Inference After Estimation of Breaks (with Isaiah Andrews and Toru Kitagawa), Journal of Econometrics, 224 (2021), 39-59.
Asymptotically Uniform Tests After Consistent Model Selection in the Linear Regression Model, Journal of Business and Economic Statistics, 38 (2020), 810-825.
Estimation and Inference with a (Nearly) Singular Jacobian (with Sukjin Han), Quantitative Economics, 10 (2019), 1019-1068.
Bonferroni-Based Size-Correction for Nonstandard Testing Problems, Journal of Econometrics, 200 (2017), 17-35.
Parameter Estimation Robust to Low-Frequency Contamination (with Jonathan B. Hill), Journal of Business and Economic Statistics, 35 (2017), 598-610.
Memory Parameter Estimation in the Presence of Level Shifts and Deterministic Trends (with Pierre Perron), Econometric Theory, 29 (2013), 1196-1237.
Estimation of the Long-Memory Stochastic Volatility Model Parameters that is Robust to Level Shifts and Deterministic Trends, Journal of Time Series Analysis, 34 (2013), 285-301.
Critical Values Robust to P-hacking (with Pascal Michaillat)
(previously titled "Incentive-Compatible Critical Values")
P-hacking is prevalent in reality but absent from classical hypothesis testing theory. As a consequence, significant results are much more common than they are supposed to be when the null hypothesis is in fact true. In this paper, we build a model of hypothesis testing with p-hacking. From the model, we construct critical values such that, if the values are used to determine significance, and if scientists' p-hacking behavior adjusts to the new significance standards, significant results occur with the desired frequency. Such robust critical values allow for p-hacking so they are larger than classical critical values. To illustrate the amount of correction required by p-hacking, we calibrate the model using evidence from the medical sciences. In the calibrated model the robust critical value for any test statistic is the classical critical value for the same test statistic with one fifth of the significance level.
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