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 Formal 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 Model Reduction
5.1 The problems of simple-to-general modelling
5.2 Formulating general variables
5.3 Analyzing general models
5.4 Sequential simplification
5.5 Ecompassing tests
5.6 Model revision
6 Tutorial on automatic model selection using Autometrics
6.1 Introduction
6.2 Modelling CONS
6.3 DHSY revisited
7 Tutorial on Estimation Methods
7.1 Recursive estimation
7.2 Instrumental variables
7.3 Autoregressive least squares (RALS)
7.4 Non-linear least squares
8 Tutorial on Batch Usage
8.1 Introduction
8.2 Generating and running Batch code
8.3 Generating and running Ox code
9 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
10 An Overview
11 Learning Elementary Econometrics Using PcGive
11.1 Introduction
11.2 Variation over time
11.3 Variation across a variable
11.4 Populations, samples and shapes of distributions
11.5 Correlation and scalar regression
11.6 Interdependence
11. 7 Time dependence
11.8 Dummy variables
11.9 Sample variability
11.10 Collinearity
11.11 Nonsense regressions
12 Intermediate Econometrics
12.1 Introduction
12.2 Linear dynamic equations
12.3 Cointegration
12.4 A typology of simple dynamic models
12.5 Interpreting linear models
12.6 Multiple regression
12.7 Econometrics concepts
12.8 Instrumental variables
12.9 Inference and diagnostic testing
12.10 Model selection
13 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 estimation
14.5 Model evaluation
14.6 Test types
14.7 An information taxonomy
14.8 Automatic model selection
14.9 Conclusion
15 Eleven 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
15.10 More Variables than observations
15.11 Structural breaks and dummy saturation
IV The Statistical Output of PcGive
16 Descriptive Statistics in PcGive
16.1 Mean, standard deviations and correlations.
16.2 Normality test and descriptive statistics.
16.3 Autocorrelations (ACF) and Portmanteau statistic.
16.4 Unit-root test.
16.5 Principal component analysis
16.6 Correlogram, ACF
16.7 Partial autocorrelation function (PACF)
16.8 Periodogram
16.9 Spectral density
16.10 Histogram, estimated density and distribution
16.11 QQ plot
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
17 Model Estimation Statistics
17.1 Recursive graphics (RLS/RIVE/RNLS/RML)
17.2 OLS estimation
17.3 IV estimation.
17.4 RALS estimation
17.5 Non-linear modelling
18 Model Evaluation Statistics
18.1 Graphics analysis
18.2 Recursive graphics (RLS/RIVE/RNLS/RML)
18.3 Dynamic analysis
18.4 Diagnistics tests.
18.5 Linear restrictions test
18.6 General restrictions
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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