Contents
Table
of Contents
Book
Order Form
Table
of Contents
Preface
I PcGive Prologue
1 Introduction to PcGive
1.1 The PcGive system
1.2 Single equation modeling
1.3 The
special features of PcGive
1.4 Documentation conventions
1.5
Using PcGive documentation
1.6 Citation
1.7 World Wide
Web
1.8 Some data sets
II PcGive Tutorials
2 Tutorial on Cross-section Regression
2.1 Starting the modelling procedure
2.2 Formulating a
regression
2.3 Cross-section regression estimation
2.3.1
Simple regression output
2.4 Regression graphics
2.5 Testing
restrictions and omitted variables
2.6 Multiple regression
2.7
Fonual tests
2.8 Storing residuals in the database
3 Tutorial on Description Statistics and Unit
Roots
3.1 Descriptive data analysis
3.2 Autoregressive distributed
lag
3.3 Unit-root tests
4 Tutorial on Dynamic Modelling
4.1 Model formulation
4.2 Model estimation
4.3 Model
output.
4.3.1 Equation estimates.
4.3.2 Analysis of 1-step
forecast statistics.
4.4 Graphical evaluation
4.5 Dynamic
analysis
4.6 Mis-specification tests
4.7 Specification
tests
4.7.1 Exclusion, linear and general restrictions.
4.7.2
Test for common factors.
4.8 Options
4.9 Further
Output
4.10 Forecasting
5 Tutorial on Estimation Methods
5.1 Recursive estimation
5.2 Instrumental variables
5.2.1
Structural estimates.
5.2.2 Reduced forms.
5.3 Autoregressive
least squares
5.3.1 Optimization
5.3.2 RALS model
evaluation
5.4 Non-linear least squares
6 Tutorial on Batch Usage
7 Tutorial on Model Reduction
7.1 The problems of simple-to-general modeling
7.2 Formulating
general models
7.3 Analyzing general models
7.4 Sequential
simplification
7.5 Encompassing tests
7.6 Model
revision
8 Non-linear Models
8.1 Introduction
8.2 Non-linear modeling
8.3 Maximizing a
function
8.4 Logit and probit estimation
8.5 Tobit
estimation
8.6 ARMA estimation
8.7 ARCH estimation
III The Econometrics of PcGive
9 An Overview
10 Learning Elementary Econometrics Using PcGive
10.1 Introduction
10.2 Variation over time
10.3 Variation
across a variable
10.4 Populations, samples and shapes of
distributions
10.5 Correlation and scalar regression
10.6
Interdependence
10. 7 Time dependence
10.8 Dummy
variables
10.9 Sample variability
10.10 Collinearity
10.11
Nonsense regressions
11 Intermediate
Econometrics
11.1 Introduction
11.2 Linear dynamic equations
11.3
Cointegration
11.4 A typology of simple dynamic models
11.5
Interpreting linear models
11.6 Multiple regression
11.7
Econometrics concepts
11.8 Instrumental variables
11.9
Inference and diagnostic testing
11.10 Model selection
12 Statistical Theory
13.1 Introduction
13.2 Normal distribution
13.3 The
bivariate normal density
13.4 Multivariate normal
13.5
Likelihood
13.6 Estimation
13.7 Multiple regression
13 Advanced Econometrics
14.1 Introduction
14.2 Dynamic systems
14.3 Data density
factorizations
14.4 Model evaluation
14.5 An information
taxonomy
14.6 Test types
14.7 Modelling strategies
14.8
Model estimation
14.9 Conclusion
14 Nine Important Practical Econometric Problems
15.1 Multicollinearity
15.2 Residual auto correlation
15.3
Dynamic specification
15.4 Non-nested hypotheses
15.5
Simultaneous equations bias
15.6 Identifying restrictions
15.7 Predictive failure
15.8 Non-stationarity
15.9 Data
mining
IV The Statistical Output of PcGive
15 Descriptive Statistics in PcGive
15.1 Mean, standard deviations and correlations.
15.2
Normality test and descriptive statistics.
15.3 Correlogram (ACF)
and Portmanteau statistic.
15.4 Unit-root test.
15.5 Principal
component analysis
16 Model Estimation Statistics
16.1 Recursive estimation: RLS/RIVE/RNLS/RML
16.2 OLS
estimation
16.3 IV estimation
16.4 RALS estimation
16.5
Non-linear modeling
18 Model Evaluation Statistics
17.1 Graphic analysis
17.2 Recursive graphics
(RLS/RIVE/RNLS/RML)
17.3 Dynamic analysis.
17.4 Diagnostic
tests
17.5 Linear restrictions test
17.6
Generalrestrictions
17.7 Test for omitted variables (OLS)
17.8
Progress: the sequential reduction sequence
17.9 Encompassing and
'non-nested' hypotheses tests
V Appendices
A1 Algebra and Batch for Single Equation Modelling
A1.1 General restrictions
AI.2 Non-linear models
AI.3
PcGive batch language
A2 PcGive Artificial Data Set (data.in7/data.bn7)
A3 Numerical Changes From Previous Versions
A3.1 From version 9 to 10
A3.2 From version 8 to 9
A3.3
From version 7 to 8
Author Index
Subject
Index
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