Background
The Generalized Method of Moments (GMM) is an advanced econometric technique used for parameter estimation in statistical models. It’s particularly useful when there are more moment conditions available than parameters to be estimated.
Historical Context
GMM was first introduced by Lars Peter Hansen in 1982. The method extended Fisher’s method of moments to complex situations where the traditional approaches were impractical or insufficient.
Definitions and Concepts
Generalized Method of Moments (GMM) Estimator: A method that estimates model parameters by minimizing the sum of squared differences between the theoretical moments (expected values of functions of the parameters) and the corresponding sample moments (sample analogs).
Major Analytical Frameworks
Classical Economics
Classical economics does not typically engage with GMM directly, but its principles often provide foundational theoretical conditions under which GMM could be applied.
Neoclassical Economics
Neoclassical models often form the theoretical underpinning where GMM can be utilized to estimate parameters, particularly in efficiency and productivity analysis.
Keynesian Economic
GMM could be applied to macroeconomic models derived from Keynesian principles, particularly those involving consumption, investment, and aggregate demand components with a multitude of moment conditions.
Marxian Economics
GMM isn’t commonly used in Marxian economics, but it could potentially apply in empirical studies attempting to quantify labor and capital contributions using surplus value equations.
Institutional Economics
Institutional economists might use GMM to handle the complexities and dynamic nature of economic institutions’ behavior, where conventional estimators would be inappropriate.
Behavioral Economics
GMM can be used in behavioral economics to estimate parameters in models where multiple, often intricate, moment conditions derive from behavioral assumptions.
Post-Keynesian Economics
In Post-Keynesian frameworks, GMM can provide an empirical estimation technique for dynamic, non-equilibrium models where traditional statistical tools fail.
Austrian Economics
While Austrian economics traditionally rejects large-scale econometric modeling, GMM can serve specialized studies involving price signals and capital structures through multiple moments.
Development Economics
GMM is valuable in development economics to deal with endogeneity issues in cross-country growth analyses and similar empirical investigations.
Monetarism
GMM could be used to estimate parameters in monetarist models addressing the relations among money supply, inflation, and economic output.
Comparative Analysis
GMM is often compared to other estimation techniques like Ordinary Least Squares (OLS) and Maximum Likelihood Estimators (MLE). It stands out for its flexibility in scenarios where assumptions for OLS or MLE are violated.
Case Studies
- Financial Asset Pricing: GMM is extensively used in empirical finance to estimate parameters of asset pricing models.
- Economic Growth Studies: Applied in macroeconomic growth studies to derive estimations considering numerous country-specific characteristics.
Suggested Books for Further Studies
- “Econometrics” by Fumio Hayashi
- “Time Series Analysis” by James D. Hamilton
- “Econometric Theory and Methods” by Russell Davidson and James G. MacKinnon
Related Terms with Definitions
- Method of Moments Estimator: An estimation technique that involves equating sample moments to population moments to solve for parameter estimates.
- Ordinary Least Squares (OLS): An estimator that minimizes the sum of squared residuals to produce parameter estimates.
- Maximum Likelihood Estimator (MLE): An estimation method that maximizes the likelihood function to estimate parameters based on observed data.
