Background
An instrumental variable (IV) is a critical concept in the field of econometrics, particularly used to solve issues related to endogeneity in regression models. Endogeneity occurs when an explanatory variable is correlated with the error term, leading to biased and inconsistent estimates. By utilizing an instrumental variable, one can obtain consistent estimators even in the presence of endogeneity issues.
Historical Context
The conceptual foundation of instrumental variable techniques dates back to the 1920s with studies on agricultural productivity. However, it wasn’t until the mid-20th century that the method gained prominence through works such as that of Wright and Haavelmo. Instrumental variables became widely used in various areas of economic research, including labor economics, health economics, and public policy.
Definitions and Concepts
An instrumental variable must satisfy two main conditions:
- Relevance: The IV must be correlated with the endogenous explanatory variable.
- Exogeneity: The IV must be uncorrelated with the error term in the explanatory equation.
The primary goal of using an instrumental variable is to produce unbiased and consistent parameter estimates in the presence of endogenous regressors.
Major Analytical Frameworks
Classical Economics
Classical economics, which focuses on the self-regulating nature of markets, does not strongly emphasize the role of econometrics and the use of instrumental variables.
Neoclassical Economics
Neoclassical economics incorporates mathematical modeling and statistical analysis, making the use of instrumental variables relevant for ensuring the accuracy and reliability of empirical findings.
Keynesian Economics
In Keynesian economic analysis, where macroeconomic relationships often suffer from endogeneity, instrumental variable techniques offer a way to arrive at more robust conclusions.
Marxian Economics
Marxian economists may use instrumental variable methods primarily in empirical research where endogeneity is a concern, though these techniques are less central to the largely theoretical framework of Marxian analysis.
Institutional Economics
Institutional economists often deal with complex social processes where endogeneity is a risk, making the application of instrumental variables necessary to refine their empirical research.
Behavioral Economics
Instrumental variables can be particularly useful in behavioral economics for isolating causal relationships from potentially biasing endogeneity effects often seen in observational data.
Post-Keynesian Economics
Similar to Keynesianism, post-Keyesian analysis can greatly benefit from instrumental variable techniques in addressing endogeneity issues in macroeconomic modeling and forecasting.
Austrian Economics
The Austrian school of thought, skeptical of heavy reliance on econometrics, is less inclined to use instrumental variables; however, applied research within this framework might necessitate it.
Development Economics
In development economics, instrumental variables are vital for addressing endogeneity problems in evaluating the impacts of policies or interventions in observational studies.
Monetarism
In monetarist studies, where the relationships between monetary policy variables and economic outcomes can suffer from endogeneity, instrumental variable methods ensure the consistency and reliability of empirical outliers.
Comparative Analysis
The use of instrumental variables provides a crucial tool across various economic paradigms for addressing issues of endogeneity, offering a pathway to more accurate and reliable economic models.
Case Studies
Orley Ashenfelter’s Work
Orley Ashenfelter applied instrumental variables to labor economics by finding instruments that weren’t directly observed but correlated with the model to remain consistent and reduce endogeneity.
Angrist and Krueger
Joshua Angrist and Alan Krueger famously used the quarter of birth as an instrumental variable to study the impact of education on earnings, illustrating how IVs help address endogeneity.
Suggested Books for Further Studies
- “Mostly Harmless Econometrics: An Empiricist’s Companion” by Joshua D. Angrist and Jörn-Steffen Pischke
- “Econometric Analysis” by William H. Greene
- “Introductory Econometrics: A Modern Approach” by Jeffrey M. Wooldridge
- “Instrumental Variables: Economic and Econometric Applications” by Carlo A. Binder
Related Terms with Definitions
- Endogeneity: A situation in a statistical model where explanatory variables are correlated with the error term.
- Ordinary Least Squares (OLS): A method for estimating the unknown parameters in a linear regression model.
- Exogeneity: A condition where an explanatory variable is uncorrelated with the error term in a regression model.
- Two-Stage Least Squares (2SLS): A method used to estimate the coefficients in models with endogenous explanatory variables by first regressing the endogenous variable on the instrument and then using the predicted values in the second stage.
