Contents
Table of Contents
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Table of Contents
1. Introduction to PcGets
1.1 The Econometrics of PcGets
1.2 PcGets model selection
1.3 The special features of PcGets
1.4 Documentation conventions
1.5 Using PcGets documentation
1.6 An overview of PcGets menus
1.7 Citation
1.8 World Wide Web
1.9 Some data sets
2. Getting Started
2.1 Starting PcGets
2.2 Loading and viewing the tutorial data set
2.3 GiveWin graphics
2.4 Calculator
Part II: Tutorials on PcGets
3. Tutorial on Model Formulation and Estimation
3.1 Starting PcGets
3.2 Formulating a mode
3.3 Ordinary Least Squares (OLS) estimation
3.4 Model output
3.5 Instrumental Variables Estimation (IVE)
3.6 Progress
4. Tutorial on Post-Estimation Model Evaluation
4.1 Graphical evaluation
4.2 Dynamic analysis
4.3 Analysis of forecasts
4.4 Collinearity analysis
4.5 Specification tests
4.6 Recursive analysis
5. Tutorial on Automatic Model Selection
5.1 Formulating general models
5.2 Model settings for selection
5.3 Testimation - GETS
5.4 Testimation - GETSIVE
5.5 Sequential simplification of an I(0) GUM
5.6 Pre-programmed selection settings
5.7 Constrained selection: using fixed variables
5.8 Expert settings
5.9 Applying PcGets substantively
5.10 Advice on using PcGets in modelling
6. Tutorial on Cross-section Model Selection
6.1 Formulating a regression
6.2 Model selection
6.2.1 Selection output
6.3 Regression graphics
6.4 Alternative selection strategies
6.5 Fixing selected variables .
7. Tutorial on Batch Usage
7.1 Batch codes generated by PcGets .
7.2 Example
7.3 Editing batch files
7.4 Create your own liberal and conservative strategy
8. Tutorial on Modelling VARs
8.1 Introduction
8.2 General-to-specific reductions of VAR models
8.3 A Small Monetary VAR of the UK
8.4 Conclusion
Part III: The Econometrics of PcGets
9. The Theory of Reduction
9.1 Introduction
9.2 Deriving the LDGP
9.3 The econometric model
9.4 Econometric concepts as measures of no information loss
9.5 A taxonomy of evaluation information .
9.6 Dominance
10. The Econometrics of Model Selection
10.1 Introduction
10.2 The selection stages of PcGets
10.3 Analyzing the algorithm
10.4 Selection probabilities
10.5 Deletion probabilities
10.6 Monte Carlo evidence on PcGets
11. Refuting Potential Criticisms of Gets
11.1 Introduction
11.2 Data-based model selection
11.3 Measurement without theory
11.4 Data mining
11.5 Pre-test biases
11.6 Ignoring selection effects
11.7 Spurious significance from repeated testing
11.8 Arbitrary choices of significance levels
11.9 Lack of identification
11.10 Path dependence of selection
11.11 Implications
11.12 What are the alternatives?
Part IV: Statistics of PcGets
12. Model Estimation Statistics
12.1 Introduction
12.2 Model formulation
12.3 OLS estimation
12.4 Recursive OLS estimation
12.5 Forecasting
12.6 Instrumental variables estimation
13. Post-estimation Evaluation Statistics
13.1 Introduction
13.2 Graphic analysis
13.3 Recursive graphics
13.4 Dynamic analysis
13.5 Collinearity analysis
13.6 Forecasts
13.7 Diagnostic tests
13.8 Linear restrictions test
13.9 Exclusion restrictions
13.10 Tests for omitted variables
13.11 Encompassing tests
PART V PcGets Menus and Options
14. PcGets Menus
14.1 Overview
14.2 File menu (Alt+f)
14.3 Package Menu
14.4 Model menu (Alt+m)
14.5 Test menu (Alt+t)
14.6 Help menu (Alt+h)194
15. Model-selection Strategy Options
15.1 Introduction
15.2 Model settings dialog box
15.3 Model settings options
15.4 Options (Expert user's srategy)
16. PcGets Batch Language
16.1 Introduction
16.2 PcGets batch commands
16.3 Illustrative batch code
A1 The PcGets Algorithm
A1.1 The PcGets algorithm
References
Author Index
Subject Index
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