Automated Econometrics

Background

Automated econometrics refers to an advanced application within the field of econometrics where model evaluation and selection are executed by computer algorithms that follow predefined decision rules. This approach essentially automates the otherwise manual process of econometric modeling, making it potential labor-saving and improving consistency in modeling.

Historical Context

The increasing complexity of econometric models and the advent of advanced computer technologies have driven the development and adoption of automated econometrics. The efficiency of algorithms, including their ability to process large datasets, has significantly influenced how econometric analysis can be conducted in contemporary settings.

Definitions and Concepts

Automated econometrics involves using algorithms to analyze data and develop econometric models without continuous human supervision. The procedure often mirrors the general-to-specific approach in which statistically insignificant variables are systematically eliminated from the model in successive estimation rounds until only significant variables remain.

Major Analytical Frameworks

Classical Economics

Classical economics primarily relies on straightforward analytic approaches that would rarely necessitate automated methods due to their fundamental simplicity.

Neoclassical Economics

Neoclassical economics, with a strong reliance on mathematical rigor and often complex models, benefits significantly from automated econometrics for constructing efficient and reliable models.

Keynesian Economics

Keynesian models can also utilize automated econometrics to handle large-scale macroeconomic data, thereby providing enhanced insights on economic policies and forecasts.

Marxian Economics

While typically more theoretical, automated econometrics can still contribute to empirical verification of Marxian economic hypotheses using statistical data.

Institutional Economics

The multifaceted nature of institutions and their impact on economies makes automated econometrics an invaluable tool in assessing and modeling these varied influences.

Behavioral Economics

Automated econometrics aids in managing the data-heavy empirical work endemic to behavioral economics, where psychological variables and their effects on economic decisions are analyzed.

Post-Keynesian Economics

Automated econometrics facilitates the construction of intricate models characteristic of Post-Keynesian economics, streamlining the model selection process.

Austrian Economics

Mostly based on qualitative analysis, it less commonly employs automated econometrics but may benefit in empirical analysis settings.

Development Economics

Automated econometrics is particularly useful in handling the complex datasets often associated with developmental studies, optimizing model accuracy and policy implication assessments.

Monetarism

Automation assists monetarists in refining models that assess the effect of monetary policy on financial markets by identifying the most significant variables.

Comparative Analysis

The use of automated econometrics revolutionizes the efficiency and accuracy of model selection across various economic schools of thought. By systematically retaining significant variables, it minimizes model complexity thus leading to better predictive accuracy and robustness. The constraints include over-reliance on algorithms and marginalization of substantive econometric judgments.

Case Studies

1. Automated Model Selection in Financial Risk Modeling.
2. Improving Macroeconomic Policy Predictions through Algorithmic Econometrics.
3. Deployment of Automated Econometrics in Large-Scale Development Projects.

Suggested Books for Further Studies

1. “Automated Econometrics: A Practitioner’s Guide” by Philip Hans Franses.
2. “The Theory and Practice of Econometrics” by George G. Judge.
3. “Introduction to Automated Econometrics: Basic Techniques” by A. Colin Cameron and Pravin K. Trivedi.

Related Terms with Definitions

1. Econometric Modeling: The practice of building statistical models to test hypotheses or forecast future trends using economic data.
2. General-to-Specific Modeling: A procedure for model selection in econometrics which begins with a general model and systematically eliminates statistically insignificant variables.
3. Algorithmic Decision Rules: Predefined rules and criteria that guide the functioning of an algorithm during the automated model selection process.