Weak identification in probit models with endogenous covariates

被引:1
|
作者
Dufour, Jean-Marie [1 ,2 ]
Wilde, Joachim [3 ]
机构
[1] McGill Univ, Dept Econ, Ctr Interuniv Rech Anal Org CIRANO, Leacock Bldg,Room 414,855 Sherbrooke St West, Montreal, PQ H3A 2T7, Canada
[2] McGill Univ, CIREQ, Leacock Bldg,Room 414,855 Sherbrooke St West, Montreal, PQ H3A 2T7, Canada
[3] Fachbereich Wirtschaftswissensch, Rolandstr 8, D-49069 Osnabruck, Germany
基金
加拿大自然科学与工程研究理事会;
关键词
Probit model; Weak identification; z-test; GENERALIZED-METHOD; GMM ESTIMATION; INFERENCE; ECONOMETRICS; INSTRUMENTS; RECESSIONS; PARAMETERS; LIKELIHOOD; FAILURE; MOMENTS;
D O I
10.1007/s10182-018-0325-8
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Weak identification is a well-known issue in the context of linear structural models. However, for probit models with endogenous explanatory variables, this problem has been little explored. In this paper, we study by simulating the behavior of the usual z-test and the LR test in the presence of weak identification. We find that the usual asymptotic z-test exhibits large level distortions (over-rejections under the null hypothesis). The magnitude of the level distortions depends heavily on the parameter value tested. In contrast, asymptotic LR tests do not over-reject and appear to be robust to weak identification.
引用
收藏
页码:611 / 631
页数:21
相关论文
共 50 条
  • [21] Feature Screening in High Dimensional Regression with Endogenous Covariates
    Hu, Qinqin
    Lin, Lu
    COMPUTATIONAL ECONOMICS, 2022, 60 (03) : 949 - 969
  • [22] Partial identification in nonseparable binary response models with endogenous regressors
    Gu, Jiaying
    Russell, Thomas M.
    JOURNAL OF ECONOMETRICS, 2023, 235 (02) : 528 - 562
  • [23] On probit versus logit dynamic mixed models for binary panel data
    Sutradhar, Brajendra C.
    Bari, Wasimul
    Das, Kalyan
    JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 2010, 80 (04) : 421 - 441
  • [24] Using Heteroscedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models
    Lewbel, Arthur
    JOURNAL OF BUSINESS & ECONOMIC STATISTICS, 2012, 30 (01) : 67 - 80
  • [25] Testing identifying assumptions in bivariate probit models
    Acerenza, Santiago
    Bartalotti, Otavio
    Kedagni, Desire
    JOURNAL OF APPLIED ECONOMETRICS, 2023, 38 (03) : 407 - 422
  • [26] Amultiple imputationmethod for incomplete correlated ordinal data using multivariate probit models
    Zhang, Xiao
    Li, Quanlin
    Cropsey, Karen
    Yang, Xiaowei
    Zhang, Kui
    Belin, Thomas
    COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2017, 46 (03) : 2360 - 2375
  • [27] GEL statistics under weak identification
    Guggenberger, Patrik
    Ramalho, Joaquim J. S.
    Smith, Richard J.
    JOURNAL OF ECONOMETRICS, 2012, 170 (02) : 331 - 349
  • [28] GMM with Nearly-Weak Identification
    Antoine, Bertille
    Renault, Eric
    ECONOMETRICS AND STATISTICS, 2024, 30 : 36 - 59
  • [29] CORRUPTION AND GROWTH UNDER WEAK IDENTIFICATION
    Shaw, Philip
    Katsaiti, Marina-Selini
    Jurgilas, Marius
    ECONOMIC INQUIRY, 2011, 49 (01) : 264 - 275
  • [30] A two recursive equation model to correct for endogeneity in latent class binary probit models
    Sarrias, Mauricio
    JOURNAL OF CHOICE MODELLING, 2021, 40