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Causality Estimation in Panel Data

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  • Hrishikesh Vinod

    (Fordham University, Department of Economics)

Abstract

Evaluation of causal paths from panel data (time series of cross sections or lon- gitudinal data) can use pooled data, ignoring the time and space dimensions. More generally, we want to draw readers' attention to an algorithm causeSum2Panel(.), freely available in the R package 'generalCorr.' It estimates causality directions and strengths, focusing on the time and space dimensions. We describe new tools using the space dimension data to formally test Granger causal directions. We illustrate the uniquely new insights gained from the two dimensions, using three datasets already available in the R package 'plm' for panel linear models, namely Grunfeld, Crime, and Cigar. Among new insights available nowhere else, we identify which regressions suffer from endogeneity issues, causal path directions, and strengths. We indicate fruitful areas for further research in studies of panel data.

Suggested Citation

  • Hrishikesh Vinod, 2023. "Causality Estimation in Panel Data," Fordham Economics Discussion Paper Series dp2023-09er:dp2023-09, Fordham University, Department of Economics.
    • Handle: RePEc:frd:wpaper:dp2023-09er:dp2023-09
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    File URL: https://archive.fordham.edu/ECONOMICS_RESEARCH/PAPERS/dp2023_09_vinod.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

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    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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