Fixed Effects Individual Slopes using feisr
The main purpose of the package
feisr is the estimation of fixed effects individual slope
models and respective test statistics. The fixed effects individual
slope (FEIS) estimator is a more general version of the well-known fixed
effects estimator (FE), which allows to control for heterogeneous slopes
in addition to time-constant heterogeneity (e.g.
Bruederl and Ludwig 2015; Frees 2001; Lemieux 1998; Polachek and Kim
1994; Ruettenauer and Ludwig 2020; Wooldridge 2010). Formally,
the FEIS estimator can be expressed as
$$ \begin{align} \boldsymbol{\mathbf{y}}_{i} =& \boldsymbol{\mathbf{X}}_{i}\boldsymbol{\mathbf{\beta }}+ \boldsymbol{\mathbf{W}}_i \boldsymbol{\mathbf{\alpha}}_i + \boldsymbol{\mathbf{\epsilon}}_{i}, \end{align} $$ where yi is T × 1, Xi is T × K, and ϵi is T × 1. Wi is a T × J matrix of slope variables, and αi a J × 1 vector of individual-specific slope parameters, for J slope parameters including a constant term. If Wi consists of a constant term only, Wi = 1, thus αi reduces to αi1, and the above equation represents the well-known formula of a conventional FE model with individual fixed effects.
As with the conventional FE, FEIS can be estimated using
lm() by including N − 1 individual-specific dummies
and interaction terms of each slope variable with the N − 1 individual-specific dummies
((N − 1) * J
controls). This is however highly inefficient. As with the conventional
FE estimator, we can achieve the same result by running an
lm() on pre-transformed data. Therefore, specify the
‘residual maker’ matrix Mi = IT − Wi(Wi⊺Wi)−1Wi⊺,
and estimate $$
\begin{align}
y_{it} - \hat{y}_{it} =& (\boldsymbol{\mathbf{x}}_{it} -
\hat{\boldsymbol{\mathbf{x}}}_{it})\boldsymbol{\mathbf{\beta }}+
\epsilon_{it} - \hat{\epsilon}_{it}, \\
\boldsymbol{\mathbf{M}}_i \boldsymbol{\mathbf{y}}_i =&
\boldsymbol{\mathbf{M}}_i
\boldsymbol{\mathbf{X}}_i\boldsymbol{\mathbf{\beta }}+
\boldsymbol{\mathbf{M}}_i \boldsymbol{\mathbf{\epsilon}}_{i}, \\
\tilde{\boldsymbol{\mathbf{y}}}_{i} =&
\tilde{\boldsymbol{\mathbf{X}}}_{i}\boldsymbol{\mathbf{\beta }}+
\tilde{\boldsymbol{\mathbf{\epsilon}}}_{i},
\end{align}
$$ where $\tilde{\boldsymbol{\mathbf{y}}}_{i}$, $\tilde{\boldsymbol{\mathbf{X}}}_{i}$, and
$\tilde{\boldsymbol{\mathbf{\epsilon}}}_{i}$
are the residuals of regressing yi, each
column-vector of Xi, and
ϵi
on Wi.
Intuitively, we (1) estimate the individual-specific predicted values
for the dependent variable and each covariate based on an individual
intercept and the additional slope variables of Wi, (2)
‘detrend’ the original data by these individual-specific predicted
values, and (3) run an OLS model on the residual data. Similarly, we can
estimate a correlated random effects (CRE) model (Chamberlain 1982; Mundlak 1978; Wooldridge
2010) including the individual specific predictions $\hat{\boldsymbol{\mathbf{X}}}_{i}$ to obtain
the FEIS estimator: $$
\begin{align}
\boldsymbol{\mathbf{y}}_{i} =&
\boldsymbol{\mathbf{X}}_{i}\boldsymbol{\mathbf{\beta }}+
\hat{\boldsymbol{\mathbf{X}}}_{i}\boldsymbol{\mathbf{\rho }}+
\boldsymbol{\mathbf{\epsilon}}_{i}.
\end{align}
$$ We use this transformation of the FEIS estimator to derive a
Hausman-like Artificial Regression Test (ART) for hetergeneous slopes in
panel or otherwise nested models.
The main functions of the feisr package are:
feis(): Estimates fixed effects individual slope estimators by applying linearlmmodels to ‘detrended’ data.feistest(): Estimates a regression-based Hausman test for fixed effects individual slope models.bsfeistest(): Estimates a bootstrapped Hausman test for fixed effects individual slope models.slopes(): Extracts the individual slopes fromfeisobjects.
The functions included in the R package feisr are also
available in the xtfeis ado
for Stata. The package plm (Croissant and Millo 2008, 2019) provides
estimation of related models, as for instance the mean group (MG) or
common correlated effects mean groups (CCEMG) estimator via
pmg() function, and the estimator of models with variable
coefficients via pvcm(). The package fixest also
provides estimation of FE models with individual slopes in its function
`feols’, including higher dimensional FEIS models and instrumental
variable approaches.
Estimating feis models
We demonstrate the most important functions of the feisr
package by conducting an exemplary replication of the results presented
in Ludwig and Bruederl (2018). We
therefore use the mwp panel data, containing information on
wages and family status of 268 men. This is a random sample drawn from
the National Longitudinal Survey of Youth (Bureau
of Labor Statistics 2014), and more details on the selection of
observations and variable construction can be found in Ludwig and Bruederl (2018). To load the package
and data use:
library(feisr)
data("mwp", package = "feisr")
head(mwp)
#> id year lnw exp expq marry evermarry enrol yeduc age cohort
#> 1 1 1981 1.934358 1.076923 1.159763 0 1 1 11 18 1963
#> 2 1 1983 2.468140 3.019231 9.115755 0 1 1 12 20 1963
#> 3 1 1984 2.162480 4.038462 16.309174 0 1 1 12 21 1963
#> 4 1 1985 1.746280 5.076923 25.775146 0 1 0 12 22 1963
#> 5 1 1986 2.527840 6.096154 37.163090 0 1 1 13 23 1963
#> 6 1 1987 2.365361 7.500000 56.250000 0 1 1 13 24 1963
#> yeargr yeargr1 yeargr2 yeargr3 yeargr4 yeargr5
#> 1 2 0 1 0 0 0
#> 2 2 0 1 0 0 0
#> 3 2 0 1 0 0 0
#> 4 2 0 1 0 0 0
#> 5 3 0 0 1 0 0
#> 6 3 0 0 1 0 0The data set contains a unique person identifier (id)
and survey year indicator (year). Furthermore, we have
information about the log hourly wage rate (lnwage), work
experience (exp) and its square (expq), family
status (marry), enrollment in current education
(enrol), years of formal education education
(yeduc), age (age), birth cohort
(cohort), and a grouped year indicator
(yeargr).
To illustrate the usage of the feisr package, we
exemplary investigate the ‘marriage wage premium’: we analyze whether
marriage leads to an increase in the hourly wage for men. We use the
function feis to estimate fixed effects individual slope
models to control for the hypothesis that those men who are more likely
to marry or 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 the most simple setting, we just regress the wage
(lnw) on the dichotomous marriage indicator
(marry) and the control variable year. Instead
of controlling for a common linear time trend
(lnw ~ marry + year), we use year as slope
variable in feis() and thus allow each individual in our
dataset to have their own linear time trend
(lnw ~ marry | year):
wages.feis0 <- feis(lnw ~ marry | year, data = mwp, id = "id")
summary(wages.feis0)
#>
#>
#> Call:
#> feis(formula = lnw ~ marry | year, data = mwp, id = "id")
#>
#>
#> Residuals:
#> Min. 1st Qu. Median 3rd Qu. Max.
#> -2.4525833 -0.1252836 0.0086407 0.1443467 1.9814428
#>
#> Coefficients:
#> Estimate Std. Error t-value Pr(>|t|)
#> marry 0.067184 0.023025 2.9178 0.003555 **
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Normal standard errors
#> Slope parameters: year
#> Total Sum of Squares: 247.46
#> Residual Sum of Squares: 246.64
#> R-Squared: 0.0033108
#> Adj. R-Squared: 0.0029892The summary() function prints the model output with the
conventional information. The result of this simple model suggests that
marriage increases the wage above what we would have expected based on
each individual’s linear time trend. Note however that the assumption of
a linear time trend or a linear effect of other control variables might
be spurious. In our example, it is highly unlikely that wages are
linearly increasing with time / age. Thus, more complex transformations
of the slope variable(s) might be necessary in many applications (see e.g. Kneip and Bauer 2009; Wolfers 2006 for
further examples).
An advantage of FEIS is that it allows to use any observed variable
to be specified as individual-specific covariate. The formula in
feis() also allows ‘as is’ transformations of original
variables using I(). To replicate the analysis of Ludwig and Bruederl (2018), we use work
experience (exp) and squared work experience as the slope
variables:
wages.feis <- feis(lnw ~ marry + enrol + yeduc + as.factor(yeargr)
| exp + I(exp^2), data = mwp, id = "id")
summary(wages.feis)
#>
#>
#> 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: 0.022419In the example above, we can see that marriage has only a small
effect of 0.013, and is not statistically significant. Note that the
(first stage) slope regression, ‘detrending’ the data, always includes
an intercept to control for constant person-specific differences. The
regression on the detrended data, by default, does not contain an overal
intercept (intercept = FALSE). By default, the
feis() functions returns conventional standard errors for
homoscedastic errors (the scaled variance-covariance matrix is attached
to the output objects and can be extracted by vcov()).
Using the option robust = TRUE instead returns panel-robust
standard errors (e.g. Arellano 1993; Berger,
Graham, and Zeileis 2017; Wooldridge 2010):
wages.feis.rob <- feis(lnw ~ marry + enrol + yeduc + as.factor(yeargr)
| exp + I(exp^2), data = mwp, id = "id",
robust = TRUE)To compare the results with a conventional fixed effects and random
effects models, we use the well-known plm (Croissant and Millo 2008) package and estimate
a within and a random effects model. Note that
the feis() function would produce identical results to a
plm(..., model = "whitin", effect = "individual") model if
specified with an intercept in the slope variables only (without any
additional slope variables). However, for this case, we recommend using
the well-established plm package instead.
library(plm)
wages.fe <- plm(lnw ~ marry + enrol + yeduc + as.factor(yeargr)
+ exp + I(exp^2), data = mwp, index = c("id", "year"),
model = "within", effect = "individual")
wages.re <- plm(lnw ~ marry + enrol + yeduc + as.factor(yeargr)
+ exp + I(exp^2), data = mwp, index = c("id", "year"),
model = "random", effect = "individual")The difference between the three models produced above can be
visualized using the screenreg() and texreg()
functions of the texreg package
(Leifeld 2013). Since version
1.37.1, the texreg packages comes with an extract
method for feis models.
library(texreg)
#> Version: 1.39.4
#> Date: 2024-07-23
#> Author: Philip Leifeld (University of Manchester)
#>
#> Consider submitting praise using the praise or praise_interactive functions.
#> Please cite the JSS article in your publications -- see citation("texreg").
screenreg(list(wages.feis, wages.feis.rob, wages.fe, wages.re), digits = 3,
custom.model.names = c("FEIS", "FEIS robust", "FE", "RE"))
#>
#> ==========================================================================
#> FEIS FEIS robust FE RE
#> --------------------------------------------------------------------------
#> marry 0.013 0.013 0.078 *** 0.091 ***
#> (0.027) (0.029) (0.022) (0.020)
#> enrol -0.118 *** -0.118 *** -0.208 *** -0.202 ***
#> (0.023) (0.024) (0.023) (0.021)
#> yeduc -0.002 -0.002 0.056 *** 0.063 ***
#> (0.014) (0.018) (0.007) (0.005)
#> as.factor(yeargr)2 -0.046 -0.046 -0.141 *** -0.157 ***
#> (0.035) (0.038) (0.030) (0.029)
#> as.factor(yeargr)3 -0.019 -0.019 -0.165 *** -0.197 ***
#> (0.051) (0.052) (0.047) (0.044)
#> as.factor(yeargr)4 -0.136 * -0.136 * -0.276 *** -0.316 ***
#> (0.062) (0.062) (0.062) (0.057)
#> as.factor(yeargr)5 -0.187 * -0.187 * -0.298 *** -0.349 ***
#> (0.077) (0.074) (0.079) (0.073)
#> exp 0.073 *** 0.074 ***
#> (0.009) (0.008)
#> exp^2 -0.001 *** -0.001 ***
#> (0.000) (0.000)
#> (Intercept) 1.562 ***
#> (0.071)
#> --------------------------------------------------------------------------
#> R^2 0.025 0.025 0.414 0.440
#> Adj. R^2 0.022 0.022 0.357 0.439
#> Num. obs. 3100 3100 3100 3100
#> Num. groups: id 268 268
#> RMSE 0.285 0.285
#> s_idios 0.341
#> s_id 0.279
#> ==========================================================================
#> *** p < 0.001; ** p < 0.01; * p < 0.05The most striking difference here is the effect of marriage on wage. The FE returns a highly significant effect of marriage on the logarithmic wage of 0.078, which exceeds the prediction of the FEIS by 0.065. Consequently, according to the FE, we would conclude that there really is a ‘marital wage premium’ for men. The random effects model produces results, which are relatively similar to the results of the conventional FE model. However, the FEIS revises this conclusion by showing that a substantial part of the effect stems from the fact that men with a steeper wage growth tend to marry at higher rates (those men with a stronger effect of experience on wage also exhibit a stronger effect of experience on marriage). In consequence, we can conclude that the ‘marital wage premium’ mainly stems from selection on growth rather than from a causal effect of marriage on wage. The models also differ in terms of explained variance: R2 of 0.414 in the FE compared to 0.025 in the FEIS model. However, this seems quite plausible, given that we have ‘discarded’ all the variance in wages, which can be explained by individual work experience in the FEIS model.
Hausman-like specification tests
The example above illustrates how heterogeneous growth curves related
to the covariates can drastically influence the conclusions drawn in
conventional fixed effects models. For applied research, we thus propose
to test for the presence of heterogeneous slopes when using panel
models. Therefore, the function feistest() provides a
Hausman-like artificial regression test (as
discussed in Ruettenauer and Ludwig 2020), which estimates the
Mundlak specification (Mundlak 1978) of
the FEIS model and performs a Wald χ2 test on the
coefficients of the individual-specific predicted values using the aod package (Lesnoff and Lancelot 2012). The test can be
performed on the FEIS model estimated by feis(), and no
further model specifications are necessary. By default, the test is
jointly performed on all covariates given on the right side of the
formula. If required, the test can also be restricted to specified
covariates only using the option terms
(e.g. feistest(wages.feis, terms = c("marry", "enrol"))).
With the option type = "all", the function
feistest() provides a test of FEIS against FE, the
regression-based version of the well-known Hausman test comparing FE
against RE (Hausman 1978; Wooldridge
2010), and a third test comparing the FEIS directly against the
RE model. In the example below, we use cluster-robust standard errors
for the artificial regression test. A summary method is provided in the
package:
ht <- feistest(wages.feis, 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 Artificial 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-09Alternatively, we can also use a bootstrapped Hausman test to compare
the FEIS, the FE, and the RE model. The function
bsfeistest() performs bootstrapping by pairs cluster
resampling from the original dataset with replacement, meaning that we
run a feis, a plm within model,
and a plm random model for each
rep random sample. Note that resampling is perfomed on the
cluster ids. Thus, for unbalanced samples, only the number of clusters
is identical in each replication while the total number of observations
can vary. This method is analogue to the ‘bootstrap-se’ method described
in Cameron, Gelbach, and Miller (2008),
allowing us to receive an empirical estimate of the variance-covariance
matrix ${\boldsymbol{\mathbf{V}}}_{\hat{\boldsymbol{\mathbf{\beta}}}_1
- \hat{\boldsymbol{\mathbf{\beta}}}_0}$ and to perform the
original version of the Hausman test (Hausman
1978).
bsht <- bsfeistest(wages.feis, type = "all", rep = 100, seed = 91020104)
#> Simulations completed (by 10):
#> +++++++++ 100
summary(bsht)
#>
#>
#> Call:
#> feis(formula = lnw ~ marry + enrol + yeduc + as.factor(yeargr) |
#> exp + I(exp^2), data = mwp, id = "id")
#>
#> Bootstrapped Hausman Test
#> Repetitions: 100
#>
#> FEIS vs. FE:
#> ------------
#> H0: FEIS and FE estimates consistent
#> Alternative H1: FE inconsistent
#> Model constraints: beta_FEIS = beta_FE for: marry, enrol, yeduc,
#> as_factor_yeargr_2, as_factor_yeargr_3, as_factor_yeargr_4, as_factor_yeargr_5
#>
#>
#> Chi-squared test:
#> Chisq = 48.112, df = 7, P(> X2) = 3.3855e-08
#>
#>
#> FE vs. RE:
#> ------------
#> H0: FE and RE estimates consistent
#> Alternative H1: RE inconsistent
#> Model constraints: beta_FE = beta_RE for: marry, enrol, yeduc,
#> as_factor_yeargr_2, as_factor_yeargr_3, as_factor_yeargr_4, as_factor_yeargr_5,
#> exp, exp_2
#>
#> Chi-squared test:
#> Chisq = 13.458, df = 9, P(> X2) = 0.14297
#>
#>
#> FEIS vs. RE:
#> ------------
#> H0: FEIS and RE estimates consistent
#> Alternative H1: RE inconsistent
#> Model constraints: beta_FEIS = beta_RE for: marry, enrol, yeduc,
#> as_factor_yeargr_2, as_factor_yeargr_3, as_factor_yeargr_4, as_factor_yeargr_5
#>
#>
#> Chi-squared test:
#> Chisq = 56.537, df = 7, P(> X2) = 7.388e-10The first test comparing FEIS against FE offers a clear rejection of the null- hypothesis that FE is consistent. A highly significant χ2 of 49.558 (48.112 in the bootstrapped version) indicates that estimates of the conventional FE are inconsistent because of heterogeneous slopes. Thus, we should use FEIS instead of FE. Interestingly, the second test of FE against RE gives a non-significant χ2 of 13.087 with p=0.159 (or 13.458 with p=0.143 respectively), indicating that we could use an RE model instead of a conventional FE model. This result is in line with the fact that RE and FE produce relatively similar coefficients in our example.
Thus, when RE is the preferred model, we highly recommend to test the
RE against the FE and against the FEIS model. The third test
statistic of feistest() and bsfeistest()
offers a direct comparison of FEIS against RE: both versions –
artificial regression based and bootstrapped – show a highly significant
χ2 of 55.231 with
p=0 (or 56.537 with p=0 respectively), thereby indicating that we should
reject the null-hypothesis of consistent estimates in the RE model. In
sum, when ignoring the possibility of heterogeneous slopes, we would
lean towards using a conventional RE model, thereby relying on an effect
of marriage which equals 7 times the effect obtained in a model
accounting for individual-specific slopes.
Extracting individual slopes
Occasionally, estimates of individual slopes might be of interest in
themselves. For example, we could compute the average conditional slopes
over the sample of i as
estimates for E(αi2).
Similarly, we might compare estimated slopes across treatment groups.
Using the function slopes() on an object of class
“feis” returns the individual slope estimates for each
id:
alpha <- slopes(wages.feis)
head(alpha)
#> (Intercept) exp I(exp^2)
#> 1 1.888163 0.151121316 -0.004378886
#> 2 2.260406 0.242650679 -0.020343662
#> 3 2.624885 -0.005875327 0.001429338
#> 4 2.654415 0.009721989 0.002030869
#> 5 2.284673 0.347351383 -0.011189687
#> 6 2.199719 0.197108584 -0.007619929As shown above, we receive a matrix which contains the individual ids
in the rownames. For each individual, we get an intercept (the
individual fixed effects as in a conventional FE), and a coefficient for
each slope variable (in our case exp and squared
exp). We can use the rownames to merge the individual
slopes to the original data.
colnames(alpha) <- paste0("slp_", colnames(alpha))
alpha.df <- data.frame(alpha, id = rownames(alpha))
mwp <- merge(mwp, alpha.df, by = "id")Thus, we can also use the individual slope parameters for further transformation or analysis. As an example, we plot the predicted ln wage trends by marriage status using ggplot2 (Wickham 2016), and also add the average trends by marriage group (ever married vs. never married).
### Individual predicted trend of lnw (based on slopes)
mwp$pred <- mwp$slp_.Intercept. + mwp$exp * mwp$slp_exp + mwp$exp^2 * mwp$slp_I.exp.2.
### Average value by evermarry and exp bins
mwp$exp_gr <- round(mwp$exp, 0)
aggr <- aggregate(mwp$pred, by = list(exp = mwp$exp_gr, evermarry = mwp$evermarry), mean)
### Plot
library(ggplot2)
zp1 <- ggplot(data = mwp, aes(x = exp, y = pred)) +
geom_line(aes(group = id, col = as.factor(marry)),
linetype = "solid", lwd = 0.5, alpha = 0.4) +
geom_line(data = aggr, aes(y = x, linetype = as.factor(evermarry)),
alpha = 1, size = 1.2) +
labs(color = "Married", linetype = "Ever-married") +
guides(colour = guide_legend(override.aes = list(alpha = 1))) +
labs(y = "Predicted log wage growth", x = "Work experience") +
theme_bw()
#> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
#> ℹ Please use `linewidth` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
zp1
This example shows that the data exhibits strong heterogeneity in the
predicted wage growth over experience. It also shows, that even
conditional on the overall marriage effect – marry is controlled for in
the model from which the slopes are extracted – ever-married
individuals, on average, have a stronger wage growth over the years than
never-married men. Furthermore, the average trends already start to
diverge when most respondents are still unmarried. This leads to a
violation of the parallel trends assumption and biased point estimates
in conventional FE models.
For more information on FEIS models, the respective test statistics, and applied examples, see also Ruettenauer and Ludwig (2020).