Kwon, Dae-Heum (2007-12). Causality and aggregation in economics: the use of high dimensional panel data in micro-econometrics and macro-econometrics. Doctoral Dissertation. Thesis uri icon

abstract

  • This study proposes one plausible procedure to address two methodological issues, which are common in micro- and macro- econometric analyses, for the full realization of research potential brought by recently available high dimensional data. To address the issue of how to infer the causal structure from empirical regularities, graphical causal models are proposed to inductively infer causal structure from non-temporal and non-experimental data. However, the (probabilistic) stability condition for the graphical causal models can be violated for high dimensional data, given that close co-movements and thus near deterministic relations are oftentimes observed among variables in high dimensional data. Aggregation methods are proposed as one possible way to address this matter, allowing one to infer causal relationships among disaggregated variables based on aggregated variables. Aggregation methods also are helpful to address the issue of how to incorporate a large information set into an empirical model, given that econometric considerations, such as degrees-of-freedom and multicollinearity, require an economy of parameters in empirical models. However, actual aggregation requires legitimate classifications for interpretable and consistent aggregation. Based on the generalized condition for the consistent and interpretable aggregation derived from aggregation theory and statistical dimensional methods, we propose plausible methodological procedure to consistently address the two related issues of causal inference and actual aggregation procedures. Additional issues for empirical studies of micro-economics and macro-economics are also discussed. The proposed procedure provides an inductive guidance for the specification issues among the direct, inverse, and mixed demand systems and an inverse demand system, which is statistically supported, is identified for the consumer behavior of soft drink consumption. The proposed procedure also provides ways to incorporate large information set into an empirical model with allowing structural understanding of U.S. macro-economy, which was difficult to obtain based on the previously used factor augmented vector autoregressive (FAVAR) framework. The empirical results suggest the plausibility of the proposed method to incorporate large information sets into empirical studies by inductively addressing multicollinearity problem in high dimensional data.
  • This study proposes one plausible procedure to address two methodological issues,
    which are common in micro- and macro- econometric analyses, for the full realization of
    research potential brought by recently available high dimensional data. To address the issue of
    how to infer the causal structure from empirical regularities, graphical causal models are
    proposed to inductively infer causal structure from non-temporal and non-experimental data.
    However, the (probabilistic) stability condition for the graphical causal models can be violated
    for high dimensional data, given that close co-movements and thus near deterministic relations
    are oftentimes observed among variables in high dimensional data. Aggregation methods are
    proposed as one possible way to address this matter, allowing one to infer causal relationships
    among disaggregated variables based on aggregated variables. Aggregation methods also are
    helpful to address the issue of how to incorporate a large information set into an empirical model,
    given that econometric considerations, such as degrees-of-freedom and multicollinearity, require
    an economy of parameters in empirical models. However, actual aggregation requires legitimate
    classifications for interpretable and consistent aggregation.
    Based on the generalized condition for the consistent and interpretable aggregation
    derived from aggregation theory and statistical dimensional methods, we propose plausible
    methodological procedure to consistently address the two related issues of causal inference and
    actual aggregation procedures. Additional issues for empirical studies of micro-economics and
    macro-economics are also discussed. The proposed procedure provides an inductive guidance for
    the specification issues among the direct, inverse, and mixed demand systems and an inverse
    demand system, which is statistically supported, is identified for the consumer behavior of soft
    drink consumption. The proposed procedure also provides ways to incorporate large information
    set into an empirical model with allowing structural understanding of U.S. macro-economy, which was difficult to obtain based on the previously used factor augmented vector
    autoregressive (FAVAR) framework. The empirical results suggest the plausibility of the
    proposed method to incorporate large information sets into empirical studies by inductively
    addressing multicollinearity problem in high dimensional data.

publication date

  • December 2007