TY - JOUR
T1 - Identification and estimation of time-varying nonseparable panel data models without stayers
AU - Ishihara, Takuya
N1 - Funding Information:
I would like to express my appreciation to the editor, the associate editor, and anonymous referees for their careful reading and comments. I also would like to thank Katsumi Shimotsu, Hidehiko Ichimura, and the seminar participants at the University of Tokyo, Hakodate Conference, Otaru University of Commerce, Nanzan University, Osaka University. This research is supported by Grant-in-Aid for JSPS Research Fellow (17J03043) from the JSPS, Japan.
Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2020/3
Y1 - 2020/3
N2 - This paper explores the identification and estimation of nonseparable panel data models. We show that the structural function is nonparametrically identified when it is strictly increasing in a scalar unobservable variable, the conditional distributions of unobservable variables do not change over time, and the joint support of explanatory variables satisfies some weak assumptions. To identify the target parameters, existing studies assume that the structural function does not change over time, and that there are “stayers”, namely individuals with the same regressor values in two time periods. Our approach, by contrast, allows the structural function to depend on the time period in an arbitrary manner and does not require the existence of stayers. In estimation part of the paper, we propose parametric and nonparametric estimators that implement our identification results. Monte Carlo studies indicate that our parametric estimator performs well in finite samples. Finally, we extend our identification results to models with discrete outcomes, and show that the structural function is partially identified.
AB - This paper explores the identification and estimation of nonseparable panel data models. We show that the structural function is nonparametrically identified when it is strictly increasing in a scalar unobservable variable, the conditional distributions of unobservable variables do not change over time, and the joint support of explanatory variables satisfies some weak assumptions. To identify the target parameters, existing studies assume that the structural function does not change over time, and that there are “stayers”, namely individuals with the same regressor values in two time periods. Our approach, by contrast, allows the structural function to depend on the time period in an arbitrary manner and does not require the existence of stayers. In estimation part of the paper, we propose parametric and nonparametric estimators that implement our identification results. Monte Carlo studies indicate that our parametric estimator performs well in finite samples. Finally, we extend our identification results to models with discrete outcomes, and show that the structural function is partially identified.
KW - Nonparametric identification
KW - Nonseparable models
KW - Panel data
KW - Unobserved heterogeneity
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U2 - 10.1016/j.jeconom.2019.08.008
DO - 10.1016/j.jeconom.2019.08.008
M3 - Article
AN - SCOPUS:85072715534
VL - 215
SP - 184
EP - 208
JO - Journal of Econometrics
JF - Journal of Econometrics
SN - 0304-4076
IS - 1
ER -