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Econometrics: Alchemy or Science?

Econometrics: Alchemy or Science? <jats:title>Abstract</jats:title> <jats:p>This collection of published papers records the development of an approach to econometric modelling that has reached a highly successful stage. The methodology of modelling ‘observational data’, as opposed to experimental data, which can be replicated, is analysed to highlight the fundamental flaws in various approaches, and the possibilities of others. Criteria for model adequacy are formulated (congruence and encompassing), and alternative approaches to building empirical models are compared on their ability to deliver such models. A typology of models elucidates their properties, and a taxonomy of information sources clarifies testing. Estimation is summarized by an estimator generating equation. The value of exploring the development path is to reveal by attempted applications why many widely used approaches are inadequate. The outcome is to demonstrate the viability of a general‐to‐specific approach that commences from a specification deemed more than adequate to characterize the evidence, and simplifies to a parsimonious representation that captures the main factors. By artificial Monte Carlo simulations on experiments designed by others, the success of that approach is established, leading to automatic model selection by software that can outperform practitioners.</jats:p> http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Econometrics: Alchemy or Science?

CrossRef — Oct 26, 2000

Econometrics: Alchemy or Science?


Abstract

<jats:title>Abstract</jats:title>
<jats:p>This collection of published papers records the development of an approach to econometric modelling that has reached a highly successful stage. The methodology of modelling ‘observational data’, as opposed to experimental data, which can be replicated, is analysed to highlight the fundamental flaws in various approaches, and the possibilities of others. Criteria for model adequacy are formulated (congruence and encompassing), and alternative approaches to building empirical models are compared on their ability to deliver such models. A typology of models elucidates their properties, and a taxonomy of information sources clarifies testing. Estimation is summarized by an estimator generating equation. The value of exploring the development path is to reveal by attempted applications why many widely used approaches are inadequate. The outcome is to demonstrate the viability of a general‐to‐specific approach that commences from a specification deemed more than adequate to characterize the evidence, and simplifies to a parsimonious representation that captures the main factors. By artificial Monte Carlo simulations on experiments designed by others, the success of that approach is established, leading to automatic model selection by software that can outperform practitioners.</jats:p>

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Abstract

<jats:title>Abstract</jats:title> <jats:p>This collection of published papers records the development of an approach to econometric modelling that has reached a highly successful stage. The methodology of modelling ‘observational data’, as opposed to experimental data, which can be replicated, is analysed to highlight the fundamental flaws in various approaches, and the possibilities of others. Criteria for model adequacy are formulated (congruence and encompassing), and alternative approaches to building empirical models are compared on their ability to deliver such models. A typology of models elucidates their properties, and a taxonomy of information sources clarifies testing. Estimation is summarized by an estimator generating equation. The value of exploring the development path is to reveal by attempted applications why many widely used approaches are inadequate. The outcome is to demonstrate the viability of a general‐to‐specific approach that commences from a specification deemed more than adequate to characterize the evidence, and simplifies to a parsimonious representation that captures the main factors. By artificial Monte Carlo simulations on experiments designed by others, the success of that approach is established, leading to automatic model selection by software that can outperform practitioners.</jats:p>

Published: Oct 26, 2000

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