Structural econometrics uses economic theory to model and analyze outcomes, allowing for the estimation of structural parameters and comparisons of different theories. Non-structural approaches rely on statistics or economic theory alone, or a combination of both. Structural econometrics offers distinct advantages, but the choice of approach depends on the research project.
The definition of structural econometrics is generally best approached by first understanding both terms individually in the context of economics. The term econometrics generally refers to the blending of economic theory and statistical methods when analyzing data. Structurally it commonly refers to estimation, in the sense of the deliberate application of economic theory in modeling empirical studies. Therefore, structural econometrics is often defined as empirical studies that incorporate economic theory to model and analyze outcomes. Some economists find the method useful for drawing concise conclusions between economic, statistical, and institutional relationships and assumptions.
Structural modeling may vary when considering a research project. Researchers have great flexibility in determining how much economic theory to incorporate into the design and analysis. They also have great flexibility in determining how much to rely on statistical assumptions, and this ultimate utility of the method usually rests on the tradeoff. Disagreement about the choices available and what situations to select those choices is routine, while compromises made on research projects are often argued since there are no concrete rules. However, focusing on structural econometrics appears to offer some distinct advantages.
It implicitly results in the linking of statistical models and economic theory in non-structural approaches, with economic theory often not even present in college econometrics courses. It is explicitly the goal of the structural approach, getting researchers to make connections between economics, statistics and the real world. Thus, structural econometrics potentially offers some distinct advantages.
Estimating structural parameters is one such benefit, while allowing for the use of counter experiments, simulations, and comparison of statistics. It also allows for the comparison of different theories applied to a research project, while explicit hypotheses will also provide insights into the mechanisms affecting the results. The application of structural econometrics is often used in game theory projects to understand market supply and demand, as well as a variety of other research endeavors where explicit results are useful, rather than a hindrance.
Nonstructural approaches, however, do one of three things: relying on statistics will produce little or no input from economic theory, relying on economic theory with little or no input from statistics, or will incorporate little or no conflation of the two. Examples of non-structural studies include forecasting, which is based on statistics, and measurement studies such as GDP, which is based on economic theory. Policy valuation infuses both economic theory and statistics by estimating random effects, but it is not considered structural econometrics because it usually makes minimal assumptions. Conceptually, this means that non-structural and structural works are fundamentally different, but not always in application as the lines blur considerably.
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