Book contents
- Frontmatter
- Contents
- List of contributors
- Foreword by Arthur Brown
- Preface by Robert Leeson
- Part I Bill Phillips: Some Memories and Reflections
- Part II The Phillips Machine
- Part III Dynamic Stabilisation
- Part IV Econometrics
- 36 The Bill Phillips legacy of continuous time modelling and econometric model design
- 37 The published papers
- 38 The influence of A.W. Phillips on econometrics
- 39 An appreciation of A.W. Phillips
- 40 Some notes on the estimation of time-forms of reactions in interdependent dynamic systems
- 41 Cybernetics and the regulation of economic systems
- 42 The estimation of parameters in systems of stochastic differential equations
- 43 Estimation, regulation and prediction in interdependent dynamic systems
- 44 The Walras-Bowley Paper
- 45 Estimation of systems of difference equations with moving average disturbances
- 46 The estimation of continuous time models
- 47 Estimation in continuous time series models with autocorrelated disturbances
- 48 Efficient fitting of rational spectral density functions and transfer functions
- 49 The Lucas Critique: did Phillips make a comparable contribution?
- 50 Models for the control of economic fluctuations
- 51 Statistical estimation for the purpose of economic regulation
- 52 The last paper: a foreshadowing of the Lucas Critique?
- References
- Index of names
- Index of subjects
36 - The Bill Phillips legacy of continuous time modelling and econometric model design
Published online by Cambridge University Press: 04 May 2010
- Frontmatter
- Contents
- List of contributors
- Foreword by Arthur Brown
- Preface by Robert Leeson
- Part I Bill Phillips: Some Memories and Reflections
- Part II The Phillips Machine
- Part III Dynamic Stabilisation
- Part IV Econometrics
- 36 The Bill Phillips legacy of continuous time modelling and econometric model design
- 37 The published papers
- 38 The influence of A.W. Phillips on econometrics
- 39 An appreciation of A.W. Phillips
- 40 Some notes on the estimation of time-forms of reactions in interdependent dynamic systems
- 41 Cybernetics and the regulation of economic systems
- 42 The estimation of parameters in systems of stochastic differential equations
- 43 Estimation, regulation and prediction in interdependent dynamic systems
- 44 The Walras-Bowley Paper
- 45 Estimation of systems of difference equations with moving average disturbances
- 46 The estimation of continuous time models
- 47 Estimation in continuous time series models with autocorrelated disturbances
- 48 Efficient fitting of rational spectral density functions and transfer functions
- 49 The Lucas Critique: did Phillips make a comparable contribution?
- 50 Models for the control of economic fluctuations
- 51 Statistical estimation for the purpose of economic regulation
- 52 The last paper: a foreshadowing of the Lucas Critique?
- References
- Index of names
- Index of subjects
Summary
Bill Phillips' contributions to econometrics came at a time when the subject was almost exclusively concerned with applications that involved discrete time series data and models that were based on simultaneous equations. Not only was theoretical research in econometrics during the 1950s and 1960s, when Phillips did his work, dominated by the concerns of simultaneous equations systems, but empirical applications were also predominantly based on systems of this type. Since the available observations of economic variables were discrete time series data that were commonly measured at annual and quarterly intervals, it is logical that most econometric studies of the time were concerned with the development of statistical methods for simultaneous equations models that were fitted with discrete time series data.
One of Phillips' greatest contributions to econometrics is that he opened up a new field of research on continuous time econometric modelling and statistical inference. This field contrasted in important ways with the simultaneous equations paradigm that dominated the thinking of Phillips' contemporaries. In the first place, the probabilistic framework of continuous time stochastic models was necessarily more sophisticated than discrete time series in order to accommodate the function space realisations of random processes like Brownian motion. Secondly, the models themselves were formulated as recursive systems in terms of stochastic differential equations rather than as non-recursive systems like simultaneous equations. In consequence, the models were conceptually and causally quite different from simultaneous equations.
- Type
- Chapter
- Information
- A. W. H. Phillips: Collected Works in Contemporary Perspective , pp. 341 - 347Publisher: Cambridge University PressPrint publication year: 2000
- 3
- Cited by