Curriculum Vitaes

Hasebe Takuya

  (長谷部 拓也)

Profile Information

Affiliation
Associate Professor, Faculty of Liberal Arts, Department of Liberal Arts, Sophia University
Degree
Bacherlor of Arts(Rutgers University)
Doctor of Philosophy(City University of New York)

Researcher number
60748896
J-GLOBAL ID
201501011916812440
researchmap Member ID
7000013448

External link

2013-2014 Postdoctoral Research at University of California Davis
2014-present Assistant Professor at Sophia University


Education

 2

Papers

 18
  • Takuya Hasebe
    The Stata Journal: Promoting communications on statistics and Stata, 22(4) 734-771, Dec, 2022  Peer-reviewed
    In this article, I describe the commands that implement the estimation of three endogenous models of binary choice outcome. The command esbinary fits the endogenously switching model, where a potential outcome differs across two treatment states. The command edbinary fits the endogenous dummy model, which includes a dummy variable indicating the treatment state as one of the explanatory variables. After one estimates the parameters of these models, various treatment effects can be estimated as postestimation statistics. The command ssbinary fits the sample-selection model, where an outcome is observed in only one of the states. The commands fit these models using copula-based maximumlikelihood estimation.
  • Yoshihisa Shono, Kaoru Sugai, Takuya Hasebe
    RIETI Policy Discussion Paper Series, 21-P-021, Dec, 2021  
  • Wim Vijverberg, Takuya Hasebe
    Communications in Statistics - Simulation and Computation, 52(6) 2290-2309, Apr 12, 2021  Peer-reviewed
  • Takuya Hasebe
    Economics Letters, 200 109768-109768, Mar, 2021  Peer-reviewed
  • Takuya Hasebe
    The Stata Journal: Promoting communications on statistics and Stata, 20(3) 627-646, Sep, 2020  Peer-reviewed
    In this article, I describe the escount command, which implements the estimation of an endogenous switching model with count-data outcomes, where a potential outcome differs across two alternate treatment statuses. escount allows for either a Poisson or a negative binomial regression model with lognormal latent heterogeneity. After estimating the parameters of the switching regression model, one can estimate various treatment effects with the command teescount. I also describe the command lncount, which fits the Poisson or negative binomial regression model with lognormal latent heterogeneity.

Misc.

 1

Books and Other Publications

 1

Presentations

 24

Research Projects

 6

Social Activities

 2