arXiv 17 Jan 2022 · Econometrics · 2 citations (OpenAlex)
arXiv:2201.06605 · PDF · DOI · OpenAlex · Extracted main text
The Granular Instrumental Variables (GIV) methodology exploits panels with factor error structures to construct instruments to estimate structural time series models with endogeneity even after controlling for latent factors. We extend the GIV methodology in several dimensions. First, we extend the identification procedure to a large $N$ and large $T$ framework, which depends on the asymptotic Herfindahl index of the size distribution of $N$ cross-sectional units. Second, we treat both the factors and loadings as unknown and show that the sampling error in the estimated instrument and factors is negligible when considering the limiting distribution of the structural parameters. Third, we show that the sampling error in the high-dimensional precision matrix is negligible in our estimation algorithm. Fourth, we overidentify the structural parameters with additional constructed instruments, which leads to efficiency gains. Monte Carlo evidence is presented to support our asymptotic theory and application to the global crude oil market leads to new results.
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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 | Bai, Jushan, Ng, Serena (2010) Instrumental variable estimation in a data rich environment | 0.950 | 7 | 5 | 86% |
| 2 | Gabaix, Xavier (2011) The granular origins of aggregate fluctuations | 0.941 | 6 | 3 | 83% |
| 3 | Fan, Jianqing, Liao, Yuan, Mincheva, Martina (2013) Large covariance estimation by thresholding principal orthogonal complements | 0.874 | 6 | 3 | 67% |
| 4 | Mohaddes, Kamiar, Pesaran, M Hashem (2016) Country-specific oil supply shocks and the global economy: A counterfactual analysis | 0.874 | 5 | 2 | 100% |
| 5 | Bai, Jushan, Ng, Serena (2002) Determining the number of factors in approximate factor models | 0.737 | 5 | 4 | 40% |
| 6 | Ahn, Seung C, Horenstein, Alex R (2013) Eigenvalue ratio test for the number of factors | 0.737 | 5 | 2 | 60% |
| 7 | Bai, Jushan (2003) Inferential theory for factor models of large dimensions | 0.737 | 3 | 3 | 67% |
| 8 | Bai, Jushan, Liao, Yuan (2017) Inferences in panel data with interactive effects using large covariance matrices | 0.737 | 3 | 3 | 67% |
| 9 | Bai, Jushan (2009) Panel data models with interactive fixed effects | 0.737 | 3 | 2 | 100% |
| 10 | Gabaix, Xavier, Koijen, Ralph SJ (2021) Granular instrumental variables | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 77 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Granular Instrumental Variables in Large Panels: Identification and Inference Across Strong, Nearly Weak, and Weak GIV | 0.679 | 16 | 4 |
| 2 | Granular Instrumental Variables: Estimation and Inference | 0.644 | 2 | 2 |
| 3 | Structural Analysis of Vector Autoregressive Models | 0.585 | 3 | 1 |