Background
The within-groups estimator is a powerful tool used in econometrics to analyze panel data, which consists of multiple observations over time for several cross-sectional units, such as individuals, firms, or countries.
Historical Context
The development of panel data models, including the within-groups estimator, gained momentum in the late 20th century as availability of longitudinal data improved and computational advancements allowed for more sophisticated analysis. This method addresses specific challenges that arise when the data includes multiple observations across time for different units, particularly unobserved heterogeneity.
Definitions and Concepts
The within-groups estimator is an estimator of the vector of parameters in a model with panel data. It is computed using ordinary least squares (OLS) on the deviations from the time averages of the data for each cross-sectional unit. This essentially means that the estimator focuses on within-unit variations by “demeaning” the data — subtracting the average over time for each unit from the observed values.
Major Analytical Frameworks
Classical Economics
Does not typically involve within-groups estimators, as it predates advanced econometric techniques for panel data analysis.
Neoclassical Economics
Employs the within-groups estimator in panel data econometrics to control for unobserved individual heterogeneity.
Keynesian Economics
Uses similar econometric methods but focuses on different applications, primarily macroeconomic aggregates rather than panel microdata.
Marxian Economics
Less commonly employs panel data methods like within-groups estimators, often due to the nature of its economic analysis.
Institutional Economics
Balances qualitative and quantitative analyses and may use within-groups estimators in examining institutional effects on individual or firm behavior over time.
Behavioral Economics
Could utilize within-groups estimators to analyze the temporal aspects of individual behavioral data in experimental settings.
Post-Keynesian Economics
Similar to Keynesian economics, may use within-groups estimators in certain empirical analyses but focuses more on macro and monetary issues.
Austrian Economics
Relies more on theoretical constructs, hence less frequent usage of within-groups estimators in empirical investigations.
Development Economics
Frequently uses within-groups estimators to examine the impacts of policies or interventions across different cultures, regions, and times.
Monetarism
Although typically focusing on large-scale economic aggregates, it may use within-groups estimators when dealing with panel data consisting of monetary or financial metrics across countries or regions.
Comparative Analysis
The within-groups estimator is especially beneficial when dealing with fixed effects models in contrast to random effects models, where the assumption is that entity-specific effects are not correlated with other regressors in the model. Compared to between-groups estimators, within-groups estimators utilize within-unit variations, providing a difference-in-differences approach to mitigate bias from unobserved heterogeneity.
Case Studies
- Development Economics: Assessing the impact of a social intervention on household income across various villages over a period.
- Labor Economics: Examining the effect of training programs on employee productivity, adjusting for individual characteristics.
Suggested Books for Further Studies
- “Econometric Analysis of Panel Data” by Badi H. Baltagi.
- “Analysis of Panel Data” by Cheng Hsiao.
Related Terms with Definitions
Panel Data: Data that contains observations over multiple time periods for the same cross-sectional units.
Ordinary Least Squares (OLS): A method of estimating the parameters in a linear regression model to minimize the sum of the squared differences between observed and predicted values.
Least Squares Dummy Variable (LSDV) Model: A model that includes dummy variables representing each cross-sectional unit to control for unobserved heterogeneity.
Between-groups Estimator: An estimator that uses the variation between different cross-sectional units’ averages rather than within them.
