Contents
Table
of Contents
Book
Order Form
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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