Minsu Chang, Francis J. DiTraglia
arXiv 13 Nov 2020 · Econometrics · publishedJournal of Applied Econometrics (2018) · 4 citations (OpenAlex)
arXiv:2011.07085 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes a criterion for simultaneous GMM model and moment selection: the generalized focused information criterion (GFIC). Rather than attempting to identify the "true" specification, the GFIC chooses from a set of potentially mis-specified moment conditions and parameter restrictions to minimize the mean-squared error (MSE) of a user-specified target parameter. The intent of the GFIC is to formalize a situation common in applied practice. An applied researcher begins with a set of fairly weak "baseline" assumptions, assumed to be correct, and must decide whether to impose any of a number of stronger, more controversial "suspect" assumptions that yield parameter restrictions, additional moment conditions, or both. Provided that the baseline assumptions identify the model, we show how to construct an asymptotically unbiased estimator of the asymptotic MSE to select over these suspect assumptions: the GFIC. We go on to provide results for post-selection inference and model averaging that can be applied both to the GFIC and various alternative selection criteria. To illustrate how our criterion can be used in practice, we specialize the GFIC to the problem of selecting over exogeneity assumptions and lag lengths in a dynamic panel model, and show that it performs well in simulations. We conclude by applying the GFIC to a dynamic panel data model for the price elasticity of cigarette demand.
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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 | DiTraglia, F. J (2016) Using invalid instruments on purpose: Focused moment selection and averaging for GMM self | 0.899 | 11 | 5 | 73% |
| 2 | Andrews, D. W. K., Lu, B (2001) Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models | 0.811 | 4 | 2 | 100% |
| 3 | Baltagi, B. H., Griffin, J. M., Xiong, W (2000) To pool or not to pool: Homogeneous versus heterogeneous estimators applied to cigarette demand | 0.693 | 6 | 1 | 100% |
| 4 | Anderson, T., Hsiao, C (1982) Formulation and estimation of dynamic models using panel data | 0.644 | 2 | 2 | 100% |
| 5 | Leeb, H., Pötscher, B. M (2008) Sparse estimators and the oracle property, or the return of Hodges' estimator | 0.644 | 2 | 2 | 100% |
| 6 | Yang, Y (2005) Can the strengths of AIC and BIC be shared? a conflict between model identification and regression estimation | 0.644 | 2 | 2 | 100% |
| 7 | Hansen, B. E (2016) Efficient shrinkage in parametric models | 0.511 | 2 | 2 | 50% |
| 8 | Arellano, M., Bond, S (1991) Some tests of specification for panel data: Monte carlo evidence and an application to employment equations | 0.405 | 1 | 1 | 100% |
| 9 | Caner, M (2009) Lasso-type GMM estimator | 0.405 | 1 | 1 | 100% |
| 10 | Claeskens, G., Croux, C., Kerckhoven, J. V (2006) Variable selection for logistic regression using a prediction-focused information criterion | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 25 scored citations.