Qin Duo
- Published in print:
- 1997
- Published Online:
- November 2003
- ISBN:
- 9780198292876
- eISBN:
- 9780191596803
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0198292872.003.0007
- Subject:
- Economics and Finance, History of Economic Thought, Econometrics
Looks at problems associated with econometric model construction for the period immediately after the formative phase, and tries to link up the previous chapters and to show what has been left ...
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Looks at problems associated with econometric model construction for the period immediately after the formative phase, and tries to link up the previous chapters and to show what has been left unsolved in the formation of econometrics. The structural modelling procedure explained only how to estimate and identify a priori given structural models, while many of the empirical studies involved searching for the appropriate structural models from the given data. This mismatch of the two sides gave rise to many problems and disputes, mostly in connection with the roles that modellers attributed to individual tools of testing, identification, and estimation in the integrated process of empirical model construction, as the procedure and the associated techniques spread and formed the core of orthodox econometrics. Revisits the issue of model construction with particular respect to the roles of testing, identification, and estimation, depicting how controversies arose as econometricians were swung back to more data‐based positions, away from the emphasis on a priori considerations; back to statistical results, away from reliance on economic theory; and back to dynamics, away from concerns over contemporaneous interdependency. The first section looks at modelling issues associated with hypothesis testing; the second examines problems about model formulation with respect to identification; the third turns to the estimation aspect of modelling; and the fourth leads the discourse to the focal issue of the probability approach underlying established econometrics by illustrating that most of the problems could be viewed as due to the incompleteness of the probability approach (as suggested in Chapter 1).Less
Looks at problems associated with econometric model construction for the period immediately after the formative phase, and tries to link up the previous chapters and to show what has been left unsolved in the formation of econometrics. The structural modelling procedure explained only how to estimate and identify a priori given structural models, while many of the empirical studies involved searching for the appropriate structural models from the given data. This mismatch of the two sides gave rise to many problems and disputes, mostly in connection with the roles that modellers attributed to individual tools of testing, identification, and estimation in the integrated process of empirical model construction, as the procedure and the associated techniques spread and formed the core of orthodox econometrics. Revisits the issue of model construction with particular respect to the roles of testing, identification, and estimation, depicting how controversies arose as econometricians were swung back to more data‐based positions, away from the emphasis on a priori considerations; back to statistical results, away from reliance on economic theory; and back to dynamics, away from concerns over contemporaneous interdependency. The first section looks at modelling issues associated with hypothesis testing; the second examines problems about model formulation with respect to identification; the third turns to the estimation aspect of modelling; and the fourth leads the discourse to the focal issue of the probability approach underlying established econometrics by illustrating that most of the problems could be viewed as due to the incompleteness of the probability approach (as suggested in Chapter 1).
Qin Duo
- Published in print:
- 1997
- Published Online:
- November 2003
- ISBN:
- 9780198292876
- eISBN:
- 9780191596803
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0198292872.003.0003
- Subject:
- Economics and Finance, History of Economic Thought, Econometrics
This chapter recounts the evolution of econometric models up to the 1940s, discussing the common criteria and principles used for model choice, and the generalization of model construction as ...
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This chapter recounts the evolution of econometric models up to the 1940s, discussing the common criteria and principles used for model choice, and the generalization of model construction as econometrics focused on the structural modelling procedure. The first section reviews the pre‐model period, and the second looks at the emergence of models and the structural method of model construction. The initial generalization (formalization) efforts of the model‐building strategy and criteria are dealt with in the third section. Concludes with the establishment of the structural modelling procedure (the maturity of simultaneous‐equations model formulation).Less
This chapter recounts the evolution of econometric models up to the 1940s, discussing the common criteria and principles used for model choice, and the generalization of model construction as econometrics focused on the structural modelling procedure. The first section reviews the pre‐model period, and the second looks at the emergence of models and the structural method of model construction. The initial generalization (formalization) efforts of the model‐building strategy and criteria are dealt with in the third section. Concludes with the establishment of the structural modelling procedure (the maturity of simultaneous‐equations model formulation).
Odo Diekmann, Hans Heesterbeek, and Tom Britton
- Published in print:
- 2012
- Published Online:
- October 2017
- ISBN:
- 9780691155395
- eISBN:
- 9781400845620
- Item type:
- book
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691155395.001.0001
- Subject:
- Biology, Disease Ecology / Epidemiology
Mathematical modeling is critical to our understanding of how infectious diseases spread at the individual and population levels. This book gives readers the necessary skills to correctly formulate ...
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Mathematical modeling is critical to our understanding of how infectious diseases spread at the individual and population levels. This book gives readers the necessary skills to correctly formulate and analyze mathematical models in infectious disease epidemiology, and is the first treatment of the subject to integrate deterministic and stochastic models and methods. The book fully explains how to translate biological assumptions into mathematics to construct useful and consistent models, and how to use the biological interpretation and mathematical reasoning to analyze these models. It shows how to relate models to data through statistical inference, and how to gain important insights into infectious disease dynamics by translating mathematical results back to biology. This comprehensive and accessible book also features numerous detailed exercises throughout; full elaborations to all exercises are provided. The book covers the latest research in mathematical modeling of infectious disease epidemiology; it integrates deterministic and stochastic approaches; and teaches skills in model construction, analysis, inference, and interpretation.Less
Mathematical modeling is critical to our understanding of how infectious diseases spread at the individual and population levels. This book gives readers the necessary skills to correctly formulate and analyze mathematical models in infectious disease epidemiology, and is the first treatment of the subject to integrate deterministic and stochastic models and methods. The book fully explains how to translate biological assumptions into mathematics to construct useful and consistent models, and how to use the biological interpretation and mathematical reasoning to analyze these models. It shows how to relate models to data through statistical inference, and how to gain important insights into infectious disease dynamics by translating mathematical results back to biology. This comprehensive and accessible book also features numerous detailed exercises throughout; full elaborations to all exercises are provided. The book covers the latest research in mathematical modeling of infectious disease epidemiology; it integrates deterministic and stochastic approaches; and teaches skills in model construction, analysis, inference, and interpretation.
Joseph Ambrose, Sebastian Schneegans, Gregor Schöner, and John P. Spencer
- Published in print:
- 2015
- Published Online:
- January 2016
- ISBN:
- 9780199300563
- eISBN:
- 9780190299026
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199300563.003.0015
- Subject:
- Neuroscience, Development
This chapter takes a step back from the concepts in the book and addresses how to arrive at a model from scratch. It explores the process of approaching a new task and developing a model to capture ...
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This chapter takes a step back from the concepts in the book and addresses how to arrive at a model from scratch. It explores the process of approaching a new task and developing a model to capture behavior and addresses implementation and testing of a dynamic field model over various stages. Given the complexity of the resulting models, this is no simple task. The chapter shows the reader the entire construction process through the lens of the spatial recall model. Following development through stops, starts and dead ends, it reveals the process behind process modeling. The chapter concludes with a critical discussion of how models are assessed and evaluated.Less
This chapter takes a step back from the concepts in the book and addresses how to arrive at a model from scratch. It explores the process of approaching a new task and developing a model to capture behavior and addresses implementation and testing of a dynamic field model over various stages. Given the complexity of the resulting models, this is no simple task. The chapter shows the reader the entire construction process through the lens of the spatial recall model. Following development through stops, starts and dead ends, it reveals the process behind process modeling. The chapter concludes with a critical discussion of how models are assessed and evaluated.
