Estimating Fixed Effects Individual Slope Models

Provides the function feis() to estimate fixed effects individual slope (FEIS) models. The FEIS model constitutes a more general version of the often-used fixed effects (FE) panel model, as implemented in the package 'plm' by Croissant and Millo (2008) . In FEIS models, data are not only person demeaned like in conventional FE models, but detrended by the predicted individual slope of each person or group. Estimation is performed by applying least squares lm() to the transformed data. For more details on FEIS models see Bruederl and Ludwig (2015, ISBN:1446252442); Frees (2001) ; Polachek and Kim (1994) ; Ruettenauer and Ludwig (2020) ; Wooldridge (2010, ISBN:0262294354). To test consistency of conventional FE and random effects estimators against heterogeneous slopes, the package also provides the functions feistest() for an artificial regression test and bsfeistest() for a bootstrapped version of the Hausman test.


The packages feisr provides a function to estimate fixed effects individual slope (FEIS) models in R. FEIS models constitute a more general version of the often used conventional fixed effects (FE) panel models. In contrast to conventional fixed effects models, data are not person ‘demeaned’, but ‘detrended’ by the predicted individual slope of each person, which relaxes the assumptions of parallel trends between treated and untreated groups. For more information see Bruederl and Ludwig (2015); Frees (2001); Polachek and Kim (1994); Wooldridge (2010).

Installation

You can install feisr from github with:

# install.packages("devtools")
devtools::install_github("ruettenauer/feisr")

Example

The following example investigates the ‘marriage wage premium’: we test whether marriage leads to an increase in the hourly wage for men. The packages provides the function feis to estimate fixed effects individual slope models, which control for the hypothesis that those men who marry earlier also have a steeper wage growth over time. Similar to the plm function, feis requires to indicate a unique person / group identifier. To include individual-specific slopes, feis uses two-part formulas (expr | slope_expr), where slope_expr gives the expression for modelling the individual slopes. In our example, we use work experience (exp) and squared work experience as the slope variables:

library(feisr)
data("mwp", package = "feisr")
feis.mod <- feis(lnw ~ marry + enrol + yeduc + as.factor(yeargr)
                   | exp + I(exp^2), data = mwp, id = "id")
summary(feis.mod)
#> 
#> 
#> Call:
#> feis(formula = lnw ~ marry + enrol + yeduc + as.factor(yeargr) | 
#>     exp + I(exp^2), data = mwp, id = "id")
#> 
#> 
#> Residuals :
#>       Min.    1st Qu.     Median    3rd Qu.       Max. 
#> -2.0790815 -0.1050450  0.0046876  0.1112708  1.9412090 
#> 
#> Coefficients :
#>                      Estimate Std. Error t-value  Pr(>|t|)    
#> marry               0.0134582  0.0273006  0.4930    0.6221    
#> enrol              -0.1181725  0.0234275 -5.0442 4.913e-07 ***
#> yeduc              -0.0020607  0.0137673 -0.1497    0.8810    
#> as.factor(yeargr)2 -0.0464504  0.0352096 -1.3193    0.1872    
#> as.factor(yeargr)3 -0.0189333  0.0510825 -0.3706    0.7109    
#> as.factor(yeargr)4 -0.1361305  0.0616378 -2.2086    0.0273 *  
#> as.factor(yeargr)5 -0.1868589  0.0769889 -2.4271    0.0153 *  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Normal standard errors
#> Slope parameters:  exp, I(exp^2) 
#> Total Sum of Squares:    190.33
#> Residual Sum of Squares: 185.64
#> R-Squared:      0.024626
#> Adj. R-Squared: 0.022419

The package also comes with an artificial regression test, which performs a Hausman-like test comparing FEIS against FE, FEIS against random effects (RE), and FE against RE models. The option feistest can be used with cluster-robust standard errors:

ht <- feistest(feis.mod, robust = TRUE, type = "all")
summary(ht)
#> 
#> 
#> Call:
#> feis(formula = lnw ~ marry + enrol + yeduc + as.factor(yeargr) | 
#>     exp + I(exp^2), data = mwp, id = "id")
#> 
#> Robust Augmented Regression Test 
#> 
#> FEIS vs. FE:
#> ------------
#> H0: FEIS and FE estimates consistent 
#> Alternative H1: FE inconsistent 
#> Model constraints: marry_hat enrol_hat yeduc_hat as_factor_yeargr_2_hat 
#> as_factor_yeargr_3_hat as_factor_yeargr_4_hat as_factor_yeargr_5_hat = 0 
#> 
#> Chi-squared test:
#> Chisq = 49.558, df = 7, P(> X2) = 1.7639e-08
#> 
#> 
#> FE vs. RE:
#> ------------
#> H0: FE and RE estimates consistent 
#> Alternative H1: RE inconsistent 
#> Model constraints: marry_mean enrol_mean yeduc_mean 
#> as_factor_yeargr_2_mean as_factor_yeargr_3_mean as_factor_yeargr_4_mean 
#> as_factor_yeargr_5_mean exp_mean exp_2_mean = 0 
#> 
#> Chi-squared test:
#> Chisq = 13.087, df = 9, P(> X2) = 0.15872
#> 
#> 
#> FEIS vs. RE:
#> ------------
#> H0: FEIS and RE estimates consistent 
#> Alternative H1: RE inconsistent 
#> Model constraints: marry_hat enrol_hat yeduc_hat as_factor_yeargr_2_hat 
#> as_factor_yeargr_3_hat as_factor_yeargr_4_hat as_factor_yeargr_5_hat = 0 
#> 
#> Chi-squared test:
#> Chisq = 55.231, df = 7, P(> X2) = 1.342e-09

References

Bruederl J, Ludwig V (2015). “Fixed-Effects Panel Regression.” In H Best, C Wolf (eds.), The Sage Handbook of Regression Analysis and Causal Inference, pp. 327-357. Sage, Los Angeles. ISBN 1446252442.

Frees EW (2001). “Omitted Variables in Longitudinal Data Models.” Canadian Journal of Statistics, 29(4), 573-595. http://dx.doi.org/10.2307/3316008.

Polachek SW, Kim MK (1994). “Panel Estimates of the Gender Earnings Gap.” Journal of Econometrics, 61(1), 23-42. http://dx.doi.org/10.1016/0304-4076(94)90075-2.

Wooldridge JM (2010). Econometric Analysis of Cross Section and Panel Data}. MIT Press, Cambridge, Mass. ISBN 0262294354.

News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("feisr")

1.1.1 by Tobias Ruettenauer, 17 days ago


https://github.com/ruettenauer/feisr


Report a bug at https://github.com/ruettenauer/feisr/issues


Browse source code at https://github.com/cran/feisr


Authors: Tobias Ruettenauer [aut, cre] , Volker Ludwig [aut]


Documentation:   PDF Manual  


Task views: Econometrics


GPL (>= 2) license


Imports aod, Formula, plm, Rdpack, stats, dplyr

Suggests texreg, testthat, knitr, rmarkdown, ggplot2


Suggested by insight.

Enhanced by texreg.


See at CRAN