School of Economics

Lim Guo Wei Dennis

LIM GUO WEI DENNIS

Ph.D Candidate in Economics

I am a job market candidate and I will be available for interviews.

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REFERENCES

Professor Yichong ZHANG
Email: yczhang@smu.edu.sg
Tel: +65 68280881

Professor Jun YU
Email: yujun@smu.edu.sg
Tel: +65 68280858

Professor Jia LI

Email: jiali@smu.edu.sg
Tel: +65 68280890

Professor Wenjie WANG
Email: wang.wj@ntu.edu.sg
Tel: +65 6316-8958

WORKING PAPERS

"A Valid Anderson-Rubin Test under Both Fixed and Diverging Number of Weak Instruments",  (Job Market Paper)

The conventional and jackknife Anderson-Rubin (AR) Tests are developed separately to conduct weak-identification-robust inference when the number of instrumental variables (IVs) are fixed or diverging to infinity with the sample size, respectively. These two tests compare distinct test statistics with distinct critical values. To implement them, researchers first need to take a stance on the asymptotic behaviour of the number of IVs, which is ambiguous when this number is just moderate. Instead, in this paper, we propose two analytical weak-identificationrobust  AR tests, both of which control asymptotic size whether the number of IVs are fixed or diverging. We further analyze the power properties of these uniformly valid AR tests under both cases.

RESEARCH PAPERS

"A Conditional Linear Combination Test with Many Weak Instruments"

We consider a linear combination of jackknife Anderson-Rubin (AR), jackknife Lagrangian multiplier (LM), and orthogonalized jackknife LM tests for inference in IV regressions with many weak instruments and heteroskedasticity. Following I.Andrews (2016), we choose the weights 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 test controls asymptotic size and is admissible among certain class of tests. Under strong identification, our linear combination test has optimal power against local alternatives among the class of invariant or unbiased tests which are constructed based on jackknife AR and LM tests. Simulations and an empirical application to Angrist and Krueger’s (1991) dataset confirm the good power properties of our test.

PUBLICATIONS

"A Conditional Linear Combination Test with Many Weak Instruments", with Zhang Yichong and Wang Wenjie, Journal of Econometrics, forthcoming