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Double instrumental variable estimation of interaction models with big data

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  • Gagliardini, Patrick
  • Gouriéroux, Christian

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.

Suggested Citation

  • Gagliardini, Patrick & Gouriéroux, Christian, 2017. "Double instrumental variable estimation of interaction models with big data," Journal of Econometrics, Elsevier, vol. 201(2), pages 176-197.
    • Handle: RePEc:eee:econom:v:201:y:2017:i:2:p:176-197
    DOI: 10.1016/j.jeconom.2017.08.002
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    Cited by:

    1. Alain-Philippe Fortin & Patrick Gagliardini & O. Scaillet, 2022. "Eigenvalue tests for the number of latent factors in short panels," Swiss Finance Institute Research Paper Series 22-81, Swiss Finance Institute.
    2. Jad Beyhum & Eric Gautier, 2020. "Factor and factor loading augmented estimators for panel regression," Working Papers hal-02957008, HAL.

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    More about this item

    Keywords

    Interaction model; Factor analysis; Big data; Instrumental variable; Recommender system;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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