Book contents
- Frontmatter
- Contents
- List of figures
- List of screenshots
- Preface
- 1 Introduction
- 2 The classical linear regression model
- 3 Further development and analysis of the classical linear regression model
- 4 Diagnostic testing
- 5 Formulating and estimating ARMA models
- 6 Multivariate models
- 7 Modelling long-run relationships
- 8 Modelling volatility and correlation
- 9 Switching models
- 10 Panel data
- 11 Limited dependent variable models
- 12 Simulation methods
- Appendix: sources of data in this book
- References
- Index
1 - Introduction
Published online by Cambridge University Press: 05 June 2012
- Frontmatter
- Contents
- List of figures
- List of screenshots
- Preface
- 1 Introduction
- 2 The classical linear regression model
- 3 Further development and analysis of the classical linear regression model
- 4 Diagnostic testing
- 5 Formulating and estimating ARMA models
- 6 Multivariate models
- 7 Modelling long-run relationships
- 8 Modelling volatility and correlation
- 9 Switching models
- 10 Panel data
- 11 Limited dependent variable models
- 12 Simulation methods
- Appendix: sources of data in this book
- References
- Index
Summary
Description
‘RATS’ stands for Regression Analysis of Time-Series. Although, as the title suggests, the program was initially developed for the estimation of timeseries econometric models, recent versions of the software have a wide range of features which would be of use in the analysis of cross-sectional or panel data.
RATS is an econometric modelling package that enables the researcher to transform, analyse and estimate models for actual data, and also to conduct simulations using artificial data created in almost any way he chooses. The advantage of RATS over more traditional programming languages is that you do not have to ‘re-invent the wheel’ since most of the tasks that are of interest will be available by issuing just a couple of commands. Thus, RATS provides a useful bridge between simple but inflexible packages which are entirely menu driven, and full programming languages (such as FORTRAN or C/C++), which would require you to code up even OLS regressions yourself. The advantage of instruction-based programs such as this is that they make it quick and easy to replicate a set of results or to repeat the same analysis using a large number of different series; both would be more troublesome and time-consuming with pure menu-driven packages.
Recent versions of RATS have made the software even more powerful and yet simpler for novices to get to grips with via the use of ‘Wizards’, which will be described in detail below.
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- Publisher: Cambridge University PressPrint publication year: 2008