Inferential Theory for Granular Instrumental Variables in High Dimensions

Saman Banafti () and Tae Hwy Lee
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Saman Banafti: University of California Riverside

No 202203, Working Papers from University of California at Riverside, Department of Economics

Abstract: 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.

Keywords: Interactive effects; Factor error structure; Simultaneity; Power-law tails; Asymptotic Herfindahl index; Global crude oil market; Precision matrix. (search for similar items in EconPapers)
JEL-codes: C26 C36 C38 (search for similar items in EconPapers)
Pages: 57 Pages
Date: 2022-01
New Economics Papers: this item is included in nep-ecm and nep-ore
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Working Paper: Inferential Theory for Granular Instrumental Variables in High Dimensions (2023)
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