David T. Frazier, Eric Renault, Lina Zhang, Xueyan Zhao
arXiv 13 Nov 2020 · Econometrics
arXiv:2011.06753 · PDF · Extracted main text
We study the impact of weak identification in discrete choice models, and provide insights into the determinants of identification strength in these models. Using these insights, we propose a novel test that can consistently detect weak identification in commonly applied discrete choice models, such as probit, logit, and many of their extensions. Furthermore, we demonstrate that when the null hypothesis of weak identification is rejected, Wald-based inference can be carried out using standard formulas and critical values. A Monte Carlo study compares our proposed testing approach against commonly applied weak identification tests. The results simultaneously demonstrate the good performance of our approach and the fundamental failure of using conventional weak identification tests for linear models in the discrete choice model context. Furthermore, we compare our approach against those commonly applied in the literature in two empirical examples: married women labor force participation, and US food aid and civil conflicts.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | J. H. Stock and M. Yogo (2005) Testing for weak instruments in linear IV regression. Chapter 5 in Identification and Inference in Econometric Models: Essays in… | 1.000 | 11 | 4 | 100% |
| 2 | D. Staiger and J. H. Stock (1997) Instrumental variables regression with weak instruments | 1.000 | 8 | 4 | 100% |
| 3 | D. Rivers and Q. H. Vuong (1988) Limited information estimators and exogeneity tests for simultaneous probit models | 1.000 | 7 | 4 | 100% |
| 4 | B. Antoine and E. Renault (2020) Testing identification strength | 0.977 | 15 | 4 | 93% |
| 5 | N. Nunn and N. Qian (2014) US food aid and civil conflict | 0.974 | 26 | 3 | 92% |
| 6 | J. H. Stock and J. H. Wright (2000) GMM with weak identification | 0.965 | 10 | 4 | 90% |
| 7 | B. Antoine and E. Renault (2012) Efficient minimum distance estimation with multiple rates of convergence | 0.909 | 8 | 3 | 75% |
| 8 | J. L. Montiel Olea and C. Pflueger (2013) A robust test for weak instruments | 0.894 | 7 | 5 | 71% |
| 9 | J. M. Wooldridge (2010) Econometric analysis of cross section and panel data | 0.874 | 6 | 2 | 100% |
| 10 | W. K. Newey, J. L. Powell, and F. Vella (1999) Nonparametric estimation of triangular simultaneous equations models | 0.874 | 5 | 2 | 100% |
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