What are the best methods for checking regression model stability with panel data?

1 Basic checks

The first step to check the stability of panel data regression models is to perform some basic checks, such as descriptive statistics, graphical analysis, and correlation analysis. These checks can help to identify outliers, missing values, trends, patterns, and potential multicollinearity in the data. For example, you can use the summary() and plot() functions in R to get a summary of the data and visualize the distribution and relationship of the variables. You can also use the cor() function to calculate the correlation matrix and check for high correlations among the explanatory variables.

2 Model specification

The second step to check the stability of panel data regression models is to choose the appropriate model specification, such as fixed effects, random effects, or pooled OLS. The choice of model specification depends on the nature of the data and the research question. For example, if you are interested in the effect of time-invariant variables on the outcome variable, you may prefer a random effects model over a fixed effects model, as the latter would eliminate the variation between units. To compare different model specifications, you can use the plm() function in R and apply various tests, such as the Hausman test, the Breusch-Pagan test, and the F-test.

3 Diagnostic tests

The third step to check the stability of panel data regression models is to conduct diagnostic tests, such as tests for autocorrelation, heteroskedasticity, endogeneity, and structural breaks. These tests can help to detect and correct any violations of the assumptions of panel data regression models, such as independence, homoskedasticity, exogeneity, and stationarity. For example, you can use the pbgtest() and phtest() functions in R to test for group-wise and time-wise heteroskedasticity, respectively. You can also use the plmtest() function to test for serial correlation, endogeneity, and cross-sectional dependence.

4 Robustness checks

The fourth step to check the stability of panel data regression models is to perform robustness checks, such as sensitivity analysis, alternative specifications, and out-of-sample predictions. These checks can help to assess how sensitive the regression results are to changes in the data, the model, or the estimation method. For example, you can use the robust() function in R to obtain robust standard errors that are consistent in the presence of heteroskedasticity and autocorrelation. You can also use the update() and predict() functions to modify the model specification and generate out-of-sample predictions, respectively.

6 Model comparison

The sixth step to check the stability of panel data regression models is to compare the model results with other models, such as competing theories, alternative data sources, or different estimation methods. These comparisons can help to validate, challenge, or extend the findings of the panel data regression models, and provide insights into the robustness, generalizability, and applicability of the results. For example, you can use the AIC() and BIC() functions in R to compare the Akaike information criterion and the Bayesian information criterion, respectively, among different models. You can also use the coefplot() function to plot the coefficients and confidence intervals of different models.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?