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Full-Text Articles in Social and Behavioral Sciences
A Conditional Linear Combination Test With Many Weak Instruments, Dennis Lim, Wenjie Wang, Yichong Zhang
A Conditional Linear Combination Test With Many Weak Instruments, Dennis Lim, Wenjie Wang, Yichong Zhang
Research Collection School Of Economics
We consider a linear combination of jackknife Anderson-Rubin (AR) and orthogonalized Lagrangian multiplier (LM) tests for inference in IV regressions with many weak instruments and heteroskedasticity. We choose the weight in the linear combination based on a decision-theoretic rule that is adaptive to the identification strength. Under both weak and strong identifications, the proposed linear combination test controls asymptotic size and is admissible. Under strong identification, we further show that our linear combination test is the uniformly most powerful test against local alternatives among all tests that are constructed based on the jackknife AR and LM tests only and invariant …
Lm Tests Of Spatial Dependence Based On Bootstrap Critical Values, Zhenlin Yang
Lm Tests Of Spatial Dependence Based On Bootstrap Critical Values, Zhenlin Yang
Research Collection School Of Economics
To test the existence of spatial dependence in an econometric model, a convenient test is the Lagrange Multiplier (LM) test. However, evidence shows that, in finite samples, the LM test referring to asymptotic critical values may suffer from the problems of size distortion and low power, which become worse with a denser spatial weight matrix. In this paper, residual-based bootstrap methods are introduced for asymptotically refined approximations to the finite sample critical values of the LM statistics. Conditions for their validity are clearly laid out and formal justifications are given in general, and in detail under several popular spatial LM …
Lm Tests Of Spatial Dependence Based On Bootstrap Critical Values, Zhenlin Yang
Lm Tests Of Spatial Dependence Based On Bootstrap Critical Values, Zhenlin Yang
Research Collection School Of Economics
To test the existence of spatial dependence in an econometric model, a convenient test is the Lagrange Multiplier (LM) test. However, evidence shows that, in finite samples, the LM test referring to asymptotic critical values may suffer from the problems of size distortion and low power, which become worse with a denser spatial weight matrix. In this paper, residual-based bootstrap methods are introduced for asymptotically refined approximations to the finite sample critical values of the LM statistics. Conditions for their validity are clearly laid out and formal justifications are given in general, and in details under several popular spatial LM …