Double instrumental variable estimation of interaction models with big data

Patrick Gagliardini () and Christian Gourieroux

Journal of Econometrics, 2017, vol. 201, issue 2, 176-197

Abstract: The factor analysis of a (n,m) matrix of observations Y is based on the joint spectral decomposition of the matrix squares YY′ and Y′Y for Principal Component Analysis (PCA). For very large matrix dimensions n and m, this approach has a high level of numerical complexity. The big data feature suggests new estimation methods with a smaller degree of numerical complexity. The double Instrumental Variable (IV) approach uses row and column instruments to estimate consistently the factors via an averaging method. We compare the double IV approach to PCA in terms of numerical complexity and statistical efficiency. The double IV approach can be used for the analysis of recommender systems and provides a new collaborative filtering approach.

Keywords: Interaction model; Factor analysis; Big data; Instrumental variable; Recommender system (search for similar items in EconPapers)
JEL-codes: C23 C26 C38 C55 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:201:y:2017:i:2:p:176-197

DOI: 10.1016/j.jeconom.2017.08.002

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