About

Hidehiko Ichimura is a Professor of Economics at the Eller College of Management, who joined the college in 2018 after teaching at the University of Tokyo. His research focuses on the uses of influence functions of estimators for finite dimensional parameters in semiparametric models. Additionally, he is working on constructing an econometric framework to link structural life-cycle models to the national transfer accounts via an overlapping generations model. He earned his PhD in Economics from the Massachusetts Institute of Technology in 1988. His teaching includes courses such as Theory of Quantitative Methods in Economics and Econometrics.

Research topics

• Artificial Intelligence
• Computer Science
• Mathematics
• Applied mathematics
• Econometrics
• Mathematical optimization
• Statistics

Selected publications

• Introduction to the Annals Issue in Honor of James Powell

  Journal of Econometrics · 2025-06-27

  articleCorresponding
  PublisherDOI

• Medical expenditures over the life-cycle: persistent risks and insurance

  Japanese Economic Review · 2025-03-06 · 9 citations

  articleOpen access

  Abstract This paper builds a life-cycle model of single and married households and evaluates the roles of the national health insurance system. We use the administrative data on nationwide health insurance claims in Japan to analyze medical expenditure risks and calibrate the model with the stochastic process that varies by age and gender. Economic and welfare effects of health insurance reform depend on household income levels and generosity of the welfare program. Without health insurance, high-income households turn to self-insurance, significantly increasing aggregate savings. Low-income households, especially low-skilled single men and women, reduce savings and many of them become welfare recipients. Raising copayment rates for the elderly increases household savings, but depletes wealth of low-income households and leads to a rise in the number of welfare recipients.

  PublisherOA PDFDOI

• National Transfer Accounts (NTA) in Japan: 1984−2014

  Japanese Economic Review · 2024-12-01 · 1 citations

  article
  PublisherDOI

• Wellbeing of the older individuals in East Asia

  Japanese Economic Review · 2024-10-24 · 10 citations

  articleOpen access1st author

  Abstract Rapid demographic transition in East Asia has resulted in “super” aging. Because of steadily decreasing fertility and increasing life expectancy, the proportion of older individuals in the population and the old-age dependency ratio are rising across all East Asian countries, particularly China, the Republic of Korea, and Japan. This study empirically investigated the well-being of older individuals in these three countries using comparable micro-level data from the China Health and Retirement Longitudinal Study, Korean Longitudinal Study on Aging, and Japanese Study of Aging and Retirement. Specifically, we examined the depressive symptoms scale as a measure of well-being and estimated the impact of four broad categories: demographic; economic; family-social; and health. The decomposition-and-simulation analyses reveal that although differences in the characteristics of older individuals in the three countries among countries explain many differences in mean depression rates, there remain significant differences across countries, which cannot be explained. Even after considering multiple factors, the study found that older individuals in Korea were more likely to be depressed than those in China or Japan.

  PublisherOA PDFDOI

• Well-Being of Older People in East Asia: The People’s Republic of China, Japan, and the Republic of Korea

  2024-10-01

  reportOpen access1st authorCorresponding

  This study examines the well-being of older people in the People’s Republic of China (PRC), Japan, and the Republic of Korea (ROK) using microlevel data. It focuses on depressive symptom scales and the impact of demographic, economic, social, and health factors. Although much of the differences of the results across the three countries is due to the differences in the characteristics of older people, significant unexplained differences remain. In particular, even after accounting for several factors, older people in the ROK are more likely to be depressed than in the PRC or Japan.

  PublisherDOI

• Well-Being of Older People in East Asia: The People’s Republic of China, Japan, and the Republic of Korea

  SSRN Electronic Journal · 2024-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Education-to-Work Transitions and Youth’s Psychological Well-Being in Taiwan

  2023-01-01

  book-chapter
  PublisherDOI

• Locally Robust Semiparametric Estimation

  Econometrica · 2022 · 122 citations

  • Computer Science
  • Artificial Intelligence
  • Mathematics

  Many economic and causal parameters depend on nonparametric or high dimensional first steps. We give a general construction of locally robust/orthogonal moment functions for GMM, where first steps have no effect, locally, on average moment functions. Using these orthogonal moments reduces model selection and regularization bias, as is important in many applications, especially for machine learning first steps. Also, associated standard errors are robust to misspecification when there is the same number of moment functions as parameters of interest. We use these orthogonal moments and cross‐fitting to construct debiased machine learning estimators of functions of high dimensional conditional quantiles and of dynamic discrete choice parameters with high dimensional state variables. We show that additional first steps needed for the orthogonal moment functions have no effect, globally, on average orthogonal moment functions. We give a general approach to estimating those additional first steps. We characterize double robustness and give a variety of new doubly robust moment functions. We give general and simple regularity conditions for asymptotic theory.

  PublisherOA PDFDOI

• Locally robust semiparametric estimation

  2022-09-06 · 38 citations

  report

  We give a general construction of debiased/locally robust/orthogonal (LR) moment functions for GMM, where the derivative with respect to first step nonparametric estimation is zero and equivalently first step estimation has no effect on the influence function. This construction consists of adding an estimator of the influence function adjustment term for first step nonparametric estimation to identifying or original moment conditions. We also give numerical methods for estimating LR moment functions that do not require an explicit formula for the adjustment term. LR moment conditions have reduced bias and so are important when the first step is machine learning. We derive LR moment conditions for dynamic discrete choice based on first step machine learning estimators of conditional choice probabilities. We provide simple and general asymptotic theory for LR estimators based on sample splitting. This theory uses the additive decomposition of LR moment conditions into an identifying condition and a first step influence adjustment. Our conditions require only mean square consistency and a few (generally either one or two) readily interpretable rate conditions. LR moment functions have the advantage of being less sensitive to first step estimation. Some LR moment functions are also doubly robust meaning they hold if one first step is incorrect. We give novel classes of doubly robust moment functions and characterize double robustness. For doubly robust estimators our asymptotic theory only requires one rate condition.

  PublisherDOI

• The influence function of semiparametric estimators

  Quantitative Economics · 2022-01-01 · 39 citations

  articleOpen access1st authorCorresponding

  There are many economic parameters that depend on nonparametric first steps. Examples include games, dynamic discrete choice, average exact consumer surplus, and treatment effects. Often estimators of these parameters are asymptotically equivalent to a sample average of an object referred to as the influence function. The influence function is useful in local policy analysis, in evaluating local sensitivity of estimators, and constructing debiased machine learning estimators. We show that the influence function is a Gateaux derivative with respect to a smooth deviation evaluated at a point mass. This result generalizes the classic Von Mises (1947) and Hampel (1974) calculation to estimators that depend on smooth nonparametric first steps. We give explicit influence functions for first steps that satisfy exogenous or endogenous orthogonality conditions. We use these results to generalize the omitted variable bias formula for regression to policy analysis for and sensitivity to structural changes. We apply this analysis and find no sensitivity to endogeneity of average equivalent variation estimates in a gasoline demand application.

  PublisherOA PDFDOI

Frequent coauthors

• Petra Todd

  26 shared

• James J. Heckman

  20 shared

• Whitney K. Newey

  17 shared

• Jeffrey Smith

  16 shared

• Joseph G. Altonji

  13 shared

• Taisuke Otsu

  13 shared

• Satoshi Shimizutani

  11 shared

• Yoichi Arai

  Tohoku University

  11 shared