Causality Estimation in Panel Data

Hrishikesh Vinod

Fordham Economics Discussion Paper Series from 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.

Keywords: Porfolio; choice (search for similar items in EconPapers)
JEL-codes: C30 C51 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://archive.fordham.edu/ECONOMICS_RESEARCH/PAPERS/dp2023_09_vinod.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:frd:wpaper:dp2023-09er:dp2023-09

Access Statistics for this paper

More papers in Fordham Economics Discussion Paper Series from Fordham University, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Fordham Economics ().