Hidehiko Ichimura

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Scholarly Contributions

Journals/Publications

• Ichimura, H., Newey, W. K., Chernozhukov, V., Escanciano, J. C., & Robins, J. M. (2022). Locally Robust Semiparametric Estimation. Econometrica.
• Ichimura, H., & Newey, W. (2022). The influence function of semiparametric estimators. Quantitative Economics, 13(1), 29–61. doi:https://doi.org/10.3982/QE826
• Ichimura, H., Fukai, T., & Kawata, K. (2021). Describing the impacts of COVID-19 on the labor market in Japan until June 2020. The Japanese Economic Review, 72(3), 439-482. doi:10.1007/s42973-021-00081-z
• , H. I., & , W. K. (2018). The Influence Function of Semiparametric Estimators.
  More info

  Often semiparametric estimators are asymptotically equivalent to a sampleaverage. The object being averaged is referred to as the influence function.The influence function is useful in formulating primitive regularity conditionsfor asymptotic normality, in efficiency comparions, for bias reduction, and foranalyzing robustness. We show that the influence function of a semiparametricestimator can be calculated as the limit of the Gateaux derivative of aparameter with respect to a smooth deviation as the deviation approaches apoint mass. We also consider high level and primitive regularity conditions forvalidity of the influence function calculation. The conditions involve Frechetdifferentiability, nonparametric convergence rates, stochastic equicontinuity,and small bias conditions. We apply these results to examples.[Journal_ref: ]
• , T. F., , H. I., & , K. K. (2018). Quantifying Health Shocks Over the Life Cycle.
  More info

  We first show (1) the importance of investigating health expenditure processusing the order two Markov chain model, rather than the standard order onemodel, which is widely used in the literature. Markov chain of order two is theminimal framework that is capable of distinguishing those who experience acertain health expenditure level for the first time from those who have beenexperiencing that or other levels for some time. In addition, using the modelwe show (2) that the probability of encountering a health shock first de-creases until around age 10, and then increases with age, particularly, afterage 40, (3) that health shock distributions among different age groups do notdiffer until their percentiles reach the median range, but that above themedian the health shock distributions of older age groups gradually start tofirst-order dominate those of younger groups, and (4) that the persistency ofhealth shocks also shows a U-shape in relation to age.[Journal_ref: ]
• , V. C., , J. C., , H. I., , W. K., & , J. M. (2018). Locally Robust Semiparametric Estimation.
  More info

  We give a general construction of debiased/locally robust/orthogonal (LR)moment functions for GMM, where the derivative with respect to first stepnonparametric estimation is zero and equivalently first step estimation has noeffect on the influence function. This construction consists of adding anestimator of the influence function adjustment term for first stepnonparametric estimation to identifying or original moment conditions. We alsogive numerical methods for estimating LR moment functions that do not requirean explicit formula for the adjustment term. LR moment conditions have reduced bias and so are important when the firststep is machine learning. We derive LR moment conditions for dynamic discretechoice based on first step machine learning estimators of conditional choiceprobabilities. We provide simple and general asymptotic theory for LRestimators based on sample splitting. This theory uses the additivedecomposition of LR moment conditions into an identifying condition and a firststep influence adjustment. Our conditions require only mean square consistencyand a few (generally either one or two) readily interpretable rate conditions. LR moment functions have the advantage of being less sensitive to first stepestimation. Some LR moment functions are also doubly robust meaning they holdif one first step is incorrect. We give novel classes of doubly robust momentfunctions and characterize double robustness. For doubly robust estimators ourasymptotic theory only requires one rate condition.[Journal_ref: ]
• Ichimura, H., & Arai, Y. (2018). Simultaneous Selection of Optimal Bandwidths for the Sharp Regression Discontinuity Estimator. Quatitative Economics, 9(1), 441-482. doi:https://doi.org/10.3982/QE590
• Ichimura, H., Ahn, H., Powell, J. L., & Ruud, P. A. (2018). Simple Estimators for Invertible Index Models. Journal of Business and Economic Statistics, 36(1), 1-10. doi:https://doi.org/10.1080/07350015.2017.1379405

Presentations

• Ichimura, H. (2023). Identification of LATE Revisited. Southern Methodist University Econometrics SeminarDepartment of Economics, Southern Methodist University.
• Ichimura, H. (2023). LATE with Covariates. UC San Diego, Econometrics SeminarDepartment of Economics, UC San Diego.
• Ichimura, H. (2021, January). Locally Robust Semiparametric Estimation. Econometrics Seminar at UC San Diego. Department of Economics, UC San Diego: Department of Economics, UC San Diego.
• Ichimura, H. (2020, February). Locally Robust Semiparametric Estimation. Econometrics Seminar at Emory University. Emory University, Atlanta, GA: Department of Economics, Emory University.
• Ichimura, H. (2020, September). Locally Robust Semiparametric Estimation. Econometrics Seminar at Rutgers University. Department of Economics, Rutgers University: Department of Economics, Rutgers University.
• Ichimura, H. (2019, April). General Discussion. Robustness in Economics and Econometrics. University of Chicago: Department of Economics, University of Chicago.
• Ichimura, H. (2019, April). Locally Robust Semiparametric Estimation. Econometrics Seminar at Johns Hopkins University. Baltimore, Maryland: Department of Economics, Johns Hopkins University.
• Ichimura, H. (2019, August). General Discussion. From Theory to Statistics to Empirics: An Econometric Conference in honor of James Heckman. University of Chicago: Department of Economics, University of Chicago.
• Ichimura, H. (2019, February). Locally Robust Semiparametric Estimation. Econometric seminat at Pennsylvania State University. University Park, PA: Department of Economics, Pennsylvania State University.
• Ichimura, H. (2019, January). Locally Robust Semiparametric Estimation. Econometric seminat at University of Texas, Austin. Department of Economics, University of Texas, Austin: Department of Economics, University of Texas, Austin.
• Ichimura, H. (2019, May). General Discussion. Whitney Newey's 65th Birthday Conference. MIT, Cambridge, MA: Department of Economics, MIT.
• Ichimura, H. (2019, October). Presidential Address: What Do We Learn from Panel Data Created UsingJapanese Census?. Japanese Economic Association, Fall meeting. Kobe University, Kobe, Hyogo, Japan: Kobe University.
• Ichimura, H. (2018, December). Quantifying Health Shocks Over the Life Cycle. 9th Annual APRU Research Conference on Population Aging. The Hong Kong University of Science and Technology: The Hong Kong University of Science and Technology.
• Ichimura, H. (2018, November). General discussion. Incomplete Models 2018. Department of Economics, Northwestern University: Northwestern University and Cemmap.
• Ichimura, H. (2018, November). Locally Robust Semiparametric Estimation. Econometric seminar at UCLA. Department of Economics, UCLA: Department of Economics, UCLA.
• Ichimura, H. (2018, October). A Progress Report on Japanese Study on Aging and Retirement. ELSA International Consultants Meeting. Institute of Fiscal Studies: Institute of Fiscal Studies and RAND.
• Ichimura, H. (2018, October). Comments on "The Effect of Education on Late-Life Cognition: A Cross-Country Comparison" By Marco Angrisani, Jinkook Lee, Erik Meijer. Conference on Cross-Country Analysis of Retirement, Health, and Well-Being. University of Southern California: University of Southern California.
• Ichimura, H. (2018, October). Locally Robust Semiparametric Estimation. Econometric seminar at Northwestern University. Department of Economics, Northwestern University: Department of Economics, Northwestern University.
• Ichimura, H. (2018, October). Locally Robust Semiparametric Estimation. Econometric seminar at University of Chicago. Department of Economics, University of Chicago: Department of Economics, University of Chicago.
• Ichimura, H. (2018, September). General discussion. Conference for Joe Altonji's 65th Birthday. Department of Economics, Yale University: Department of Economics, Yale University.
• Ichimura, H. (2018, September). Locally Robust Semiparametric Estimation. Econometric seminar at Georgetown University. Department of Economics, Georgetown University: Department of Economics, Georgetown University.
• Ichimura, H. (2018, September). Locally Robust Semiparametric Estimation. Econometric seminar at University of Maryland. Department of Economics, University of Maryland: Department of Economics, University of Maryland.