Stata what is panel data

When the same cross-section of individuals is observed across multiple periods of time , the resulting dataset is called a panel dataset. For example, a dataset of annual GDP of 51 U. The key difference in running regressions with panel data with both cross-sectional and time-series variations from a usual OLS regression with only cross-sectional variation is that one needs to control for the common effect for all individuals in a particular time point, and also the idiosyncratic individual effect that is common across all years.

These are called the time fixed effects and the individual fixed effects respectively. The variation that is left after controlling for these fixed effects is the variation at the interaction between individual and time.

The most common specification for a panel regression is as follows:. In the above regression, b 2 denotes the individual fixed effects, while b 3 denotes the time fixed effects. These fixed effects are nothing but the coefficients of the dummy variables D i and D t. Once again, the problem of the dummy variable trap becomes relevant, as discussed in the section on regression with dummy variables. The individual dummies are defined as follows: D i takes the value 1 if the data-point corresponds to individual i , and otherwise takes the value 0.

Thus, D i will be 1 for T data-points and 0 for N-1 T data-points. Similarly for the time dummies, D t takes the value 1 if the data-point correponds to time-point t , and otherwise takes the value 0. Thus D t will be 1 for N data-points and 0 for T-1 N data-points. Depending on whether the individual effects D i are allowed to be correlated with the explanatory variable x it , the regression model is either called a fixed effects FE model or a random effects RE model.

While the uncorrelatedness of x it is desirable for both the FE and RE models, the RE model additionally imposes the independence of the individual effects D i with the explanatory variable x it. As a rule of thumb, it is always better to assume a fixed effects model because the estimates from an FE model is always consistent, while the RE model is consistent only if the underlying true model is RE.

The only disadvantage of wrongly assuming an FE model when the true model is RE, is that the FE estimator will be inefficient that is, the variance of the estimators will be larger. Depending on the nature of the dependent variable y it , e. In STATA , before one can run a panel regression, one needs to first declare that the dataset is a panel dataset.

This is done by the following command:. The random coefficients model relaxes this assumption and introduces individual-specific effects through the coefficient, such that. The two-way individual effects model allows the presence of both time-specific effects and individual-specific effects.

In the special case that there are only two groups and two individuals this model is equivalent to the difference-in-difference model. Like the one-way fixed effects model, this model could be estimated by including dummy variables.

However, in the two-way fixed effects model dummy variables must be included for both the time periods and the groups. Under most circumstances, the number of dummy variables included in the two-way fixed effects model makes standard ordinary least squares estimation too computationally difficult. Instead, the two-way fixed effects model is estimated using a within-group estimator which removes the variation both within groups and within the time periods.

Like the one-way random effects model, the two-way random effects model can be estimated using feasible generalized least squares FGLS or maximum likelihood estimation MLE. Dynamic Panel Data Model A key component of pure time series models is the modeling of dynamics using lagged dependent variables.

These lagged variables capture the autocorrelation between observations of the same dataset at different points in time. Because panel datasets include a time series component, it is also important to address the possibility of autocorrelation in panel data. The dynamic panel data model adds dynamics to the panel data individual effects framework.

Dynamic panel data models are most commonly estimated using a generalized method of moments GMM framework proposed by Arellano and Bond In panel data that covers small time frames, there is little need to worry about stationarity.

However, when panel data covers longer time frames, like is the case in many macroeconomic panel data series, the panel data must be tested for stationarity. Nonstationary panel data series are any panel series that do not meet the conditions of a weakly stationary time series. In part because of these considerations, a large field of research and literature surrounding panel data unit root tests has developed. Testing for unit roots in panel data requires more than just testing the individual cross sections for the presence of unit roots.

Panel data unit root tests must:. After today's blog, you should have an understanding of the fundamentals of panel data. We covered the basics of panel data including:. She is an economist skilled in data analysis and software development. She has earned a B. Concept — ClimateKimchi. You must be logged in to post a comment.

Subscribe Now. Introduction to the Fundamentals of Panel Data. There are a number of advantages of panel data: Panel data can model both the common and individual behaviors of groups.

Panel data contains more information, more variability, and more efficiency than pure time series data or cross-sectional data. Panel data can detect and measure statistical effects that pure time series or cross-sectional data can't.

Panel data can minimize estimation biases that may arise from aggregating groups into a single time series. What Is an Example of Panel Data? Field Example topics Example dataset Microeconomics GDP across multiple countries, Unemployment across different states, Income dynamic studies, international current account balances. Penn World Tables Epidemiology and Health Statistics Public health insurance data, disease survival rate data, child development and well-being data.

Medical Expenditure Panel Survey Finance Stock prices by firm, market volatilities by country or firm. Unbalanced panel datasets have missing values at some time observations for some of the groups. Marginal analysis. Checkout Continue shopping. Stata: Data Analysis and Statistical Software. Go Stata. Purchase Products Training Support Company. Study the time-invariant features within each panel, the relationships across panels, and how outcomes of interest change over time.

Fit linear models or nonlinear models for binary, count, ordinal, censored, or survival outcomes with fixed-effects, random-effects, or population-averaged estimators. Fit dynamic models or models with endogeneity. And much more. Linear fixed- and random-effects models Linear model with panel-level effects and i. Watch Fixed-effects and random-effects multinomial logit models. Watch Random-effects regression with endogenous sample selection.

Watch Extended regression models for panel data. Watch Multilevel models for survey data in Stata. Watch Panel-data cointegration tests. Watch Postestimation Selector.