Panel data econometrics in R: The plm package

By Yves Croissant, Giovanni Millo. 2008

1,221 citations recorded by the source corpus.

Panel data econometrics is obviously one of the main fields in the profession, but most of the models used are difficult to estimate with R. plm is a package for R which intends to make the estimation of linear panel models straightforward. plm provides functions to estimate a wide variety of models and to make (robust) inference.

Subfields: Economics and Econometrics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4dec7b76bc41e4adf3073a77ef382ab700248db0

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Citations in the corpus

splm: Spatial Panel Data Models in R (2012, methodology): “Nonetheless, data can be supplied according to the conventions implemented in the plm package for panel data econometrics (Croissant and Millo 2008).”
The FDI location decision: Distance and the effects of spatial dependence (2014, methodology): “There are currently no routines for spatial panel models in commerciallyavailable econometric software, so we extended the routines for spatial cross-sectional models provided in the spdep package (Bivand, 2010) and for panel data models in the plm package (Croissant and Millo, 2008).”
The impact of urban expansion on agricultural land use intensity in China (2013, background): “…stimated to account for the spatial heterogeneity and stable nobservable characteristics associated with individual counties Baltagi et al., 2003; Croissant and Millo, 2008; Hausman, 1978). ssuming that the random disturbance term has two separate comonents, the resulting model can be described…”
Does a Carbon Tax Reduce CO2 Emissions? Evidence from British Columbia (2019, positive): “All panel models are estimated using the plm and lfe packages in R (Croissant and Millo 2008; Gaure 2013).”
A study of factors that contribute to online review helpfulness (2015, neutral): “…(N = 1327) Mean SD Mean SD Rating 4.07 1.33 4.12 1.16 Word count 144.00 152.35 250.55 209.79 Helpfulness% 64.07 36.32 82.27 27.82 Data Set 1 was analyzed using Tobit Regression (Croissant & Millo, 2008; Tobin, 1958) because of the dependent variable, review helpfulness, never becomes negative.”
Canadian boreal forest greening and browning trends: an analysis of biogeographic patterns and the relative roles of disturbance versus climate drivers (2017, methodology): “Panel regression models were estimated for each500 mpanelusing theplmpackage in R (Croissant and Millo 2008) to model linear trends in peak summer NDVI.”
Technological Innovation Inputs, Outputs, and Performance (2016, methodology): “This test checks the absence of correlation between the individual effects and the independent variables; thus, when the null hypothesis is not rejected, a higher degree of efficiency in the estimation is indicated, which leads to the use of the random effects model (Croissant & Millo, 2008).”