This R package implements the dynamic panel data modeling framework described by Allison, Williams, and Moral-Benito (2017). This approach allows fitting models with fixed effects that do not assume strict exogeneity of predictors. That means you can simultaneously get the robustness to confounding offered by fixed effects models and account for reciprocal causation between the predictors and the outcome variable. The estimating approach from Allison et al. provides better finite sample performance in terms of both bias and efficiency than other popular methods (e.g., the Arellano-Bond estimator).
These models are fit using structural equation models, using maximum likelihood estimation and offering the missing data handling and flexibility afforded by SEM. This package will reshape your data, specify the model properly, and fit it with
If a result doesn’t seem right, it would be a good idea to cross-reference it with
xtdpdml for Stata. Go to https://www3.nd.edu/~rwilliam/dynamic/ to learn about
xtdpdml and the underlying method. You may also be interested in the article by Paul Allison, Richard Williams, and Enrique Moral-Benito in Socius, accessible here.
dpm will soon be on CRAN. In the meantime, you can get it from Github.
This package assumes your data are in long format, with each row representing a single observation of a single participant. Contrast this with wide format in which each row contains all observations of a single participant. For help on converting data from wide to long format, check out the tutorial that accompanies the
First we load the package and the
This next line of code converts the data to class
panel_data, which is a class specific to the
panelr that helps to simplify the treatment of the long-form panel data. You don’t have to do this, but it saves you from providing
wave arguments to the model fitting function each time you use it.
The formula syntax used in this package is meant to be as similar to a typical regression model as possible.
The most basic model can be specified like any other:
y ~ x, where
y is the dependent variable and
x is a time-varying predictor. If you would like to include time-invariant predictors, you will make the formula consist of two parts, separated with a bar (
|) like so:
y ~ x | z where z is a time invariant predictor, like ethnicity.
One of the innovations of the method, however, is the notion of pre-determined, or sequentially exogenous, predictors. To specify a model with a pre-determined variable, put the variable within a
y ~ pre(x1) + x2 | z. This tells the function that
x1 is pre-determined while
x2 is strictly exogenous by assumption. You could have multiple pre-determined predictors as well (e.g.,
y ~ pre(x1) + pre(x2) | z).
You may also fit models with lagged predictors. Simply apply the lag function to the lagged predictors in the formula:
y ~ pre(lag(x1)) + lag(x2) | z. To specify more than 1 lag, just provide it as an argument. For instance,
y ~ pre(lag(x1, 2)) + lag(x2) | z will use 2 lags of the
This will replicate the analysis of the wages data in the Socius article that describes these models.
Note that to get matching standard errors, set
information = "observed" to override
information = "expected".
MODEL INFO: Dependent variable: wks Total observations: 595 Complete observations: 595 Time periods: 2 - 7 MODEL FIT: 𝛘²(76) = 138.476 RMSEA = 0.037, 90% CI [0.027, 0.047] p(RMSEA < .05) = 0.986 SRMR = 0.025 | | Est. | S.E. | z val. | p | |:------------------|-------:|------:|-------:|------:| | union (t - 1) | -1.206 | 0.522 | -2.309 | 0.021 | | lwage (t - 1) | 0.588 | 0.488 | 1.204 | 0.229 | | ed | -0.107 | 0.056 | -1.893 | 0.058 | | wks (t - 1) | 0.188 | 0.020 | 9.586 | 0.000 | Model converged after 613 iterations
Any arguments supplied other than those that are documented within the
dpm function are passed on to
sem from the
The following arguments allow you to make changes to the default model specification:
y.lag: By default the lag 1 value of the DV is included as a predictor (this is why they are dynamic models). You may choose a different value or multiple values instead, including 0 (no lagged DV at all).
fixed.effects: By default, the model is specified as a fixed effects model. If you set this to FALSE, you get a random effects specification instead.
error.inv: This constrains error variances to be equal in each wave. It is FALSE by default.
const.inv: This constrains the constants to be equal in each wave. It is FALSE by default, but if TRUE it eliminates cross-sectional dependence.
y.free: This allows the regression coefficent of the lagged DV to vary across time. It is FALSE by default and you can either set it to TRUE or to the specific lag number(s).
x.free: This allows the regression coefficients for the predictors to vary across time. It is FALSE by default and you can either set it to TRUE to set all predictors’ coefficients free over time or else pass a vector of strings of the predictors whose coefficients should be set free over time.
alpha.free: If TRUE, relaxes the constraint that the fixed effects are equal across time. Default is FALSE to be consistent with how fixed effects models normally work.
You have most of the options available to you via
lavaan’s summary method.
You can choose to omit any of: the z statistics (
zstat = FALSE), the standard errors (
se = FALSE), or the p values (
pvalue = FALSE). You may also add confidence intervals (
ci = TRUE) at any specified level (
ci.level = .95). If you used bootstrapping for uncertainty intervals, you can also specify the method (
boot.ci.type = "perc").
The number of digits to print can be set via
digits or with the option
dpm-digits. You may also standardize coefficients via
lavaan’s method using
standardize = TRUE.
If you just want the
lavaan model specification and don’t want this package to fit the model for you, you can set
print.only = TRUE. To reduce the amount of output, I’m condensing
wages to 4 waves here.
## Main regressions wks_2 ~ en1 * union_1 + ex1 * lwage_1 + c1 * ed + p1 * wks_1 wks_3 ~ en1 * union_2 + ex1 * lwage_2 + c1 * ed + p1 * wks_2 wks_4 ~ en1 * union_3 + ex1 * lwage_3 + c1 * ed + p1 * wks_3 ## Alpha latent variable (random intercept) alpha =~ 1 * wks_2 + 1 * wks_3 + 1 * wks_4 ## Alpha free to covary with observed variables (fixed effects) alpha ~~ union_1 + union_2 + union_3 + lwage_1 + lwage_2 + lwage_3 + wks_1 ## Correlating DV errors with future values of predetermined predictors wks_2 ~~ union_3 ## Predetermined predictors covariances union_1 ~~ ed + lwage_1 + lwage_2 + lwage_3 + wks_1 union_2 ~~ ed + lwage_1 + lwage_2 + lwage_3 + union_1 + wks_1 union_3 ~~ ed + lwage_1 + lwage_2 + lwage_3 + union_1 + union_2 + wks_1 ## Exogenous (time varying and invariant) predictors covariances lwage_1 ~~ ed + wks_1 lwage_2 ~~ ed + lwage_1 + wks_1 lwage_3 ~~ ed + lwage_1 + lwage_2 + wks_1 ed ~~ wks_1 ## DV error variance free to vary across waves wks_2 ~~ wks_2 wks_3 ~~ wks_3 wks_4 ~~ wks_4 ## Let DV variance vary across waves wks_2 ~ 1 wks_3 ~ 1 wks_4 ~ 1
Alternately, you can extract the
lavaan model syntax and wide-formatted data from the fitted model object to do your own fitting and tweaking.
The model is a special type of
lavaan object. This means most methods implemented for
lavaan objects will work on these. You can also convert the fitted model into a typical
While you could convert the model to
lavaan model and apply any of
lavaan’s functions to it (and you should!), as a convenience you can use
lav_summary() to get
lavaan’s summary of the model.
Take advantage of
lavaan’s missing data handling by using the
missing = "fiml" argument as well as any other arguments accepted by
y ~ x + lag(x)).
y ~ scale(x)will cause an error.
Feature parity with
xtdpdml (Stata) is a goal. Here’s how we are doing in terms of matching relevant
1.0.0; option to specify as 0 — no lagged DV — added in
Many and perhaps more SEM fitting options are implemented by virtue of accepting any
y ~ scale(x)(fixed in
predictmethod and perhaps some ability to plot predictions
x.freeoption to allow the coefficients of all predictors to vary across periods. This will make the
summaryoutput a pain, so it will take some time to implement. (added in
Allison, P. D., Williams, R., & Moral-Benito, E. (2017). Maximum likelihood for cross-lagged panel models with fixed effects. Socius, 3, 1–17. https://doi.org/10.1177/2378023117710578
Leszczensky, L., & Wolbring, T. (2018, August 30). How to deal with reverse causality using panel data? Recommendations for researchers based on a simulation study. Working paper. https://doi.org/10.31235/osf.io/8xb4z
Moral-Benito, E., Allison, P., & Williams, R. (2019). Dynamic panel data modelling using maximum likelihood: An alternative to Arellano-Bond. Applied Economics, 51, 2221–2232. https://doi.org/10.1080/00036846.2018.1540854
Williams, R., Allison, P. D., & Moral-Benito, E. (2018). Linear dynamic panel-data estimation using maximum likelihood and structural equation modeling. The Stata Journal, 18, 293–326. https://doi.org/10.1177/1536867X1801800201