OxMetrics 2006 Books' Tables of Contents


Books

Doornik, J.A. (2006). An Introduction to OxMetrics 4, London: Timberlake Consultants Press. (ISBN: 0-9542603-9-2)

Part I: Getting Started with OxMetrics

1. Introduction

  1.1 What is new?
   1.2 For GiveWin 2 users
   1.3 Help
   1.4 Modular structure
   1.5 Installation and Upgrades
   1.6 Registration
   1.7 Data samples
   1.8 Data storage
   1.9 Results storage
   1.10 Filenames and their extensions
   1.11 OxMetrics languages
   1.12 Citation
   1.13 Contact informaiton and World Wide Web
   1.14 Documentation conventions

2. Getting Started

   2.1 Starting OxMetrics
   2.2 Registering OxMetrics
   2.3 Loading and viewing the tutorial data set
   2.4 OxMetrics graphics
   2.5 Calculator
   2.6 Algebra
   2.7 The workspace

3. OxMetrics Modules

   3.1 OxMetrics Modules
   3.2 Financial data
   3.3 Weekly and daily data
   3.4 PcGive

Part II: OxMetrics Tutorials

4. Tutorial on Graphics

   4.1 Descriptive graphics
   4.2 Actual series with optimal transformations
   4.3 Multiple series with optional transformations
   4.4 Scatter plots
   4.5 Distribution
   4.6 Time-series: AFC etc
   4.7 QQ plots
   4.8 Two series by third
   4.9 3-dimensional plots

5. Tutorial on Graph Editining

   5.1 Multiple graphs
   5.2 Graphics paper: areas and coordinates
   5.3 Graphics view
   5.4 New Data Plot Window
   5.5 Copy and paste
   5.6 About line colour and style
   5.7 Editing graphs: Graphics properties
   5.8 Graphics setup
   5.9 Adding and removing from a graph
   5.10 Drawing
   5.11 Adding text and variables
   5.12 Legends
   5.13 Scaling variables

6. Tutorial on Data Input and Output

   6.1 Open Data File and files types
   6.2 From paper to OxMetrics
   6.3 From OxMetrics to disk
   6.4 From disk to OxMetrics
   6.5 Adding variables using the clipboard
   6.6 Changing the sample period
   6.7 Appending data
   6.8 Working with daily and weekly data

7. Tutorial on Data Transformaion

   7.1 Calculator
   7.2 Advanced algebra

Part II: OxMetrics Reference

8. OxMetrics Statistics

   8.1 Actual series and scatter plots
   8.2 Mean, standard deviation and variance
   8.3 Correlogram, ACF
   8.4 Partial authocorrelation function(PACF)
   8.5 Cross-correlation function
   8.6 Periodogram
   8.7 Spectral density
   8.8 Histogram,estimated density and distribution
   8.9 Regression lines and smooths
   8.10 QQ plot
   8.11 Box plot
   8.12 Exponentially-weighted moving average (EWMA)
   8.13 Exponentially- weighted moving correlation

9. OxMetrics file formats

   9.1 OxMetrics data files
   9.2 Spreadsheet files
   9.3 Data by observation
   9.4 Data with load info
   9.5 Gauss data file
   9.6 Stata data file
   9.7 Results file
   9.8 Batch file
   9.9 Algebra file
   9.10 Ox file
   9.11 TSP file
   9.12 Matrix file
   9.13 OxMetrics graphics file
   9.14 PostScript file(.EPS)
   9.15 PostScript file(.PS)
   9.16 Enchanced meta file
   9.17 Windows meta file

10. Algebra Language

   10.1 Introduction
   10.2 Executing Algebra code
   10.3 Syntax of Algebra language
   10.4 Algebra Functions

11. Batch Languages

   11.1 Introduction
   11.2 Excecuting Batch commnads
   11.3 Batch files and default folders
   11.4 Batch commands summary
   11.5 Batch commands
   11.6 Example

12. OxMetrics graphics

   12.1 Graphic paper
   12.2 Creating graphs
   12.3 Printing graphs
   12.4 Graphics formats
   12.5 Saving and loading graphs
   12.6 Graphics objects
   12.7 Copy and paste
   12.8 Graps and sample selection
   12.9 Text formatting

13. OxMetrics data management

   13.1 Creating data
   13.2 Database font
   13.3 Databaes description
   13.4 Printing data
   13.5 Data formats
   13.6 Summary Statistics
   13.7 Saving data
   13.8 Navigation and editing
   13.9 Renaming variables
   13.10 Deleting variables
   13.11 Reordering variables
   13.12 Adding variables
   13.13 Extending or reducing the sample period
   13.14 Copy and paste
   13.15 Appending data
   13.16 Daily, weekly and timed data

References
Index


Books

Doornik, J.A. (2006). Ox: An Object-Oriented Matrix Language, London: Timberlake Consultants Press. (0-9533394-0-8).

1. Prologue

1.1 Why Ox
1.2 Availability
1.3 Ox version
1.4 Learining Ox
1.5 Ox platforms
1.6 Ox supported data formats
1.7 Extending Ox
1.8 World Wide Wed
1.9 Online documentation
1.10 Ox-users dicussion list
1.11 Installation
1.12 Completing the basic installation
1.13 Directory structure
1.14 OX3PATH
1.15 Ox for Unix

Part I: Introduction to Ox

2. Getting started with Ox

2.1 Introduction
2.2 A first Ox program
2.3 Running the first Ox program
2.4 Online help
2.5 Using file names in Ox
2.6 Ox file extensions
2.7 More on running Ox programs
2.8 Command line arguments
2.9 Extending Ox

3. Introduction to the Ox language

3.1 Variables, types and scope
3.2 Indexing matrices
3.3 Functions and function arguements
3.4 The for and while loops
3.5 The if statement
3.6 Operations and matrix programming
3.7 Arrays
3.8 Multiple files: using #include and #import
3.9 Object-oriented programming
3.10 Sytle and Hungarian notation
3.11 Optimising for speed
3.12 OxGauss

4. Numerical accuracy

5. How to...

6. Function Summary

Part II: Function and Language Reference

7. Function summary

8. Function reference

9. Predefined Constants

9.1 Missing values (NaN)
9.2 Infinity

9. Graphics function reference

9.1 Introduction
9.2 Symbol and line types
9.3 Function reference

11. Packages

11.1 Arma package
11.2 Maximisation package
11.3 Probability package
11.4 QuadPack

12. Class reference

12.1 Database and Sample class
12.2 Modelbase class
12.3 PcFiml class
12.4 PcFimlDgp class
12.5 PcNaiveDgp class
12.6 RanMC class
12.7 Simulation class

13. Language Reference

13.1 Introduction
13.2 Lexical conventions
13.3 Identifers
13.4 Objects
13.5 External declarations
13.6 Namespace
13.7 Statements
13.8 Expressions
13.9 Preprocessing
13.10 Difference with ANSI C and C++

Reference

Subject Index

Ox™ Appendix
Chapter  A1 Extending Ox . . . . . . 1
A1.1  Introduction . . . . . . 1
A1.2  Adding C/C++ code: a simple dynamic link library . . . . . . 2
A1.2.1  Dynamic link library and search paths . . . . . . 5
A1.3  Dynamic link libraries on Unix platforms . . . . . . 5
A1.4  Adding C/C++ code: returning values in arguments . . . . . . 6
A1.5  Calling Ox-coded functions from C . . . . . . 8
A1.6  Adding a user-friendly interface with Visual C++ . . . . . . 10
A1.7  Adding a user-friendly interface with Visual Basic . . . . . . 16
A1.7.1  Calling the Ox DLL from Visual Basic . . . . . . 16
A1.7.2  The RanApp example in Visual Basic . . . . . . 18
A1.8  Linking Fortran code . . . . . . 20
A1.9  Ox function summary . . . . . . 21
A1.10  Macros to access OxVALUEs . . . . . . 36
A1.11  Ox exported mathematics functions . . . . . . 38
A1.11.1  MATRIX and VECTOR types . . . . . . 38
A1.11.2  Exported matrix functions . . . . . . 40
A1.11.3  Matrix function reference . . . . . . 44
Chapter  A2 Modelbase and OxPack . . . . . . 66
OxPackDialog . . . . . . 69
OxPackGetData . . . . . . 70
Modelbase::ReceiveData . . . . . . 70
Modelbase::ReceiveDialog . . . . . . 71
Modelbase::ReceiveModel . . . . . . 71
Modelbase::SendDialog . . . . . . 72
Modelbase::SendFunctions . . . . . . 72
Modelbase::SendMenu . . . . . . 73
Modelbase::SendMethods . . . . . . 74
Modelbase::SendResults . . . . . . 74
Modelbase::SendSpecials . . . . . . 74
Modelbase::SendVarStatus . . . . . . 74
A2.1  Adding support for a Batch language . . . . . . 75
Modelbase::Batch . . . . . . 75
Modelbase::BatchMethod . . . . . . 76
Modelbase::BatchVarStatus . . . . . . 77
Modelbase::GetBatchModelSettings . . . . . . 77
Chapter  A3 Using OxGauss . . . . . . 78
A3.1  Introduction . . . . . . 78
A3.2  Running OxGauss programs from the command line . . . . . . 78
A3.3  Running OxGauss programs from GiveWin . . . . . . 79
A3.4  Calling OxGauss from Ox . . . . . . 79
A3.5  How does it work? . . . . . . 80
A3.6  Some large projects . . . . . . 80
A3.6.1  DPD98 for Gauss . . . . . . 81
Rename file . . . . . . 81
Fix for OxGauss syntax . . . . . . 81
Convert data files . . . . . . 81
Running the program . . . . . . 81
A3.6.2  BACC2001 . . . . . . 82
Installation . . . . . . 82
Running the program . . . . . . 82
A3.7  Known limitations . . . . . . 82
Chapter  A4 OxGauss Function Summary . . . . . . 84
Chapter  A5 OxGauss Language Reference . . . . . . 107
A5.1  Lexical conventions . . . . . . 107
A5.1.1  Tokens . . . . . . 107
A5.1.2  Comment . . . . . . 107
A5.1.3  Space . . . . . . 107
A5.2  Identifiers . . . . . . 107
A5.2.1  Keywords . . . . . . 108
A5.3  Constants . . . . . . 108
A5.3.1  Integer constants . . . . . . 108
A5.3.2  Character constants* . . . . . . 108
A5.3.3  Double constants . . . . . . 108
A5.3.4  Matrix constants . . . . . . 109
A5.3.5  String constants . . . . . . 109
A5.3.6  Constant expression . . . . . . 110
A5.4  Objects . . . . . . 110
A5.4.1  Types . . . . . . 110
  A5.4.1.1 Type conversion . . . . . . 111
A5.4.2  Lvalue . . . . . . 111
A5.5  OxGauss Program . . . . . . 111
A5.6  External declarations . . . . . . 111
A5.6.1  External statement . . . . . . 112
A5.6.2  Declare statement . . . . . . 112
A5.6.3  Function (procedure, fn, keyword) definitions . . . . . . 113
A5.6.4  external-statement-list . . . . . . 115
A5.7  Statements . . . . . . 115
A5.7.1  Assignment statements . . . . . . 115
A5.7.2  Selection statements . . . . . . 116
A5.7.3  Iteration statements . . . . . . 116
A5.7.4  Call statements . . . . . . 117
A5.7.5  Jump and pop statements . . . . . . 117
A5.7.6  Command statements . . . . . . 118
  A5.7.6.1 print and format command . . . . . . 118
  A5.7.6.2 output command . . . . . . 118
A5.8  Expressions . . . . . . 118
A5.8.1  Primary expressions . . . . . . 120
A5.8.2  Postfix expressions . . . . . . 121
  A5.8.2.1 Indexing vector and array types . . . . . . 121
  A5.8.2.2 Transpose . . . . . . 122
  A5.8.2.3 Factorial . . . . . . 123
A5.8.3  Power expressions . . . . . . 123
A5.8.4  Unary expressions . . . . . . 123
A5.8.5  Multiplicative expressions . . . . . . 123
A5.8.6  Additive expressions . . . . . . 125
A5.8.7  Modulo expressions . . . . . . 125
A5.8.8  Concatenation expressions . . . . . . 125
A5.8.9  Dot-relational expressions . . . . . . 126
  A5.8.9.1 Logical dot-NOT expressions . . . . . . 126
A5.8.10  Logical dot-AND expressions . . . . . . 126
A5.8.11  Logical dot-OR expressions . . . . . . 127
A5.8.12  Logical dot-XOR expressions . . . . . . 127
A5.8.13  Logical dot-EQV expressions . . . . . . 127
A5.8.14  Relational expressions . . . . . . 127
A5.8.15  Logical-NOT expressions . . . . . . 127
A5.8.16  Logical-AND expressions . . . . . . 127
A5.8.17  Logical-OR expressions . . . . . . 128
A5.8.18  Logical-XOR expressions . . . . . . 128
A5.8.19  Logical-EQV expressions . . . . . . 128
A5.8.20  Assignment expressions* . . . . . . 128
A5.8.21  Constant expressions . . . . . . 128
A5.9  Preprocessing . . . . . . 128
A5.9.1  File inclusion . . . . . . 129
A5.9.2  Conditional compilation . . . . . . 129
A5.9.3  Constant definition . . . . . . 130
Chapter  A6 Comparing Gauss and Ox syntax . . . . . . 131
A6.1  Introduction . . . . . . 131
A6.2  Comparison . . . . . . 131
A6.2.1  Comment . . . . . . 131
A6.2.2  Program entry . . . . . . 131
A6.2.3  Case and symbol names . . . . . . 131
A6.2.4  Types . . . . . . 132
A6.2.5  Matrix indexing . . . . . . 132
A6.2.6  Arrays . . . . . . 132
A6.2.7  Declaration and constants . . . . . . 133
A6.2.8  Expressions . . . . . . 133
A6.2.9  Operators . . . . . . 134
A6.2.10  Loop statements . . . . . . 134
A6.2.11  Conditional statements . . . . . . 135
A6.2.12  Printing . . . . . . 135
A6.2.13  Functions . . . . . . 135
A6.2.14  String manipulation . . . . . . 136
A6.2.15  Input and Output . . . . . . 136
A6.3  G2Ox . . . . . . 136
Chapter  A7 Random Number Generators . . . . . . 137
A7.1  Modified Park & Miller generator . . . . . . 137
A7.2  Marsaglia's generator . . . . . . 137
A7.3  L'Ecuyer's generator . . . . . . 138
Chapter References . . . . . . 139
Chapter Subject Index . . . . . . 140


Books

Doornik, J.A. and Ooms, M. (2006). Introduction to Ox, London: Timberlake Consultants Press. (ISBN 0-9552127-0-7).

Preface

1. Ox Environment

1.1 Installing Ox
1.2 Ox version
1.3 Help and documentation
1.4 Running an Ox program
1.5 Redirecting output
1.6 Using GiveWin and OxRun
1.7 Using the OxEdit editor
1.8 Graphics
1.9 Compilation and run-time errors
1.10 Have you programmed before?

2. Syntax

2.1 Introduction
2.2 Comment
2.3 Program layout
2.4 Statements
2.5 Identifiers
2.6 Style
2.7 Matrix constants
2.8 Creating a matrix
2.9 Using functions

3. Operators

3.1 Introduction
3.2 Index operators
3.3 Matrix Operators
3.4 Dot operators
3.5 Relational and equality operators
3.6 Logical operators
3.7 Assignment operators
3.8 Conditional operators
3.9 And more operaotrs
3.10 Operator precedence

4. Input and Output

4.1 Introduction
4.2 Using paths in Ox
4.3 Using OxMetrics or Excel
4.4 Matrix file (.mat)
4.5 Spreadsheet files
4.6 OxMetrics/PcGive data file (.IN7/.BN7)
4.7 What about variable names ?
4.8 Finding that file

5. Program Flow and Program Design

5.1 Intervention
5.2 for loops
5.3 while loops
5.4 break and continue
5.5 Conditional statements
5.6 Vectorization
5.7 Functions as arguments
5.8 Imporing code
5.9 Global variables
5.10 Program organisation
5.11 Style and Hungarian notation

6. Graphics

6.1 Introduction
6.2 Graphics output
6.3 Running programs with graphics
6.4 Example

7. String, Arrays and Print Formats
    
7.1 Introduction
7.2 String operators
7.3 The sprint function
7.4 Escape sequence
7.5 Print formats
7.6 Arrays
7.7 Missing values
7.8 Infinity

8. Object-Oriented Programming

8.1 Introduction
8.2 Using object oriented code
8.3 Writing object-oriented code
8.4 Inheritance

9. Summary

9.1 Style
9.2 Functions
9.3 Efficient programming
9.4 Computaional speed
9.5 Noteworthy

10. Using Ox Classes

10.1 Introduction
10.2 Regression example
10.3 Simulation example
10.4 MySimula class
10.5 Conclusion

11. Example: probit estimation

11.1 Introduction
11.2 The probit model
11.3 Step 1: estimation
11.4 Step 2: Analytical scores
11.5 Step 3: removing global variables: the database class
11.6 Step 4: independence from the model specification
11.7 Step 5: using the modelbase class
11.8 A Monte Carlo experiment
11.9 Conclusion

A1 debug session

A2 Installation Issues

A.2.1 Update the environment
A.2.2 Using the OxEdit editor

References

Subject Index


Books

Hendry, D.F. and Doornik, J.A. (2006). Empirical Econometric Modelling Using PcGive™ Volume I, London: Timberlake Consultants Press. (ISBN 0-9542603-4-1)

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


Books

Doornik, J.A. and Hendry, D.F. (2006). Modelling Dynamic Systems Using PcGive™ Volume II, London: Timberlake Consultants Press. (ISBN 0-9533394-5-9)

Part I: Prologue

1. Introduction to Volume II

1.1 The PcGive system
1.2 Multiple-equation dynamic modelling
1.3 The special features
1.4 Documentation conventions
1.5 Using Volume II
1.6 An overview of PcGive menus
1.7 Citation
1.8 World Wide Web
1.9 Some data sets

Part II: Tutorials on Multiple-Equation Modelling

2. Tutorial Data

2.1 Introduction
2.2 The tutorial data set

3. Tutorial on Unrestricted System Estimation and Evaluation

3.1 Introduction to dynamic systems
3.2 Formulating a system
3.3 Unrestricted variables
3.4 Special variables
3.5 Estimating an unrestricted system
3.6 Graphic analysis and multivariate testing
3.7 System reduction
3.8 Dynamic analysis
3.9 Recursive estimation
3.10 Batch editor
3.11 Forecasting
3.12 Equilibrium-correction representation

4. Tutorial on Cointegration Analysis

4.1 Introduction to cointegration analysis
4.2 Intercepts and linear deterministic trends I
4.3 Unrestricted and restricted variables
4.4 Estimating the vector autoregression
4.5 Cointegration analysis
4.6 Intercepts and linear deterministic trends II
4.7 Recursive eigenvalues
4.8 Cointegration graphics

5. Tutorial on Cointegrated VARs

5.1 Introduction
5.2 Imposing the rank of the cointegration space .
5.3 Intercepts and linear deterministic trends III
5.4 Cointegration restrictions
5.5 Determining unique cointegration relations
5.6 Moving-average impact matrix
5.7 Cointegration graphics
5.8 Addendum: A and H matrices

6. Tutorial on Reduction to I(0)

6.1 Introduction
6.2 A parsimonious VAR
6.3 A restricted system
6.4 Progress

7. Tutorial on Simultaneous Equations Models

7.1 Introduction to dynamic models .
7.2 The cointegrated VAR in I(0) space
7.3 Dynamic analysis and dynamic forecasting
7.4 Modelling the parsimonious VAR
7.5 Maximization control
7.6 How well did we do?

8. Tutorial on Advanced VAR Modelling

8.1 Introduction
8.2 Loading the Lüaut;tkepohl data
8.3 Estimating a VAR
8.4 Dynamic analysis
8.5 Forecasting
8.6 Dynamic simulation and impulse response analysis
8.6.1 Impulse response analysis
8.7 Sequential reduction and information criteria
8.8 Diagnostic checking
8.9 Parameter constancy
8.10 Non-linear parameter constraints

Part III: The Econometrics of Multiple Equation Modelling

9. An Introduction to the Dynamic Econometric Systems
9.1 Summary of Part III
9.2 Introduction
9.3 Economic theoretical formulation
9.4 The statistical system
9.5 System dynamics
9.6 System evaluation
9.7 The impact of I(1) on econometric modelling
9.8 The econometric model and its identification
9.9 Simultaneous equations modelling
9.10 General to specific modelling of systems

10. Some Matrix Algebra

11. Econometric Analysis of the System

11.1 System estimation
11.2 Maximum likelihood estimation
11.3 Recursive estimation
11.4 Unrestricted variables
11.5 Forecasting
11.6 Dynamic analysis
11.7 Test types
11.8 Specification tests
11.9 Mis-specification tests

12. Cointegration Analysis

12.1 Introduction
12.2 Equilibrium correction models
12.3 Estimating the cointegrating rank
12.4 Deterministic terms and restricted variables
12.5 The I(2) analysis .
12.6 Numerically stable estimation
12.7 Recursive estimation .
12.8 Testing restrictions on alpha and beta
12.9 Estimation under general restrictions
12.10 Identification

13. Econometric Analysis of the Simultaneous Equations Model

13.1 The econometric model
13.2 Identification
13.3 The estimator generating equation
13.4 Maximum likelihood estimation
13.4.1 Linear parameters
13.4.2 Non-linear parameters
13.5 Estimators in PcGive
13.6 Recursive estimation
13.7 Computing FIML
13.8 Restricted reduced form
13.9 Unrestricted variables
13.10 Derived statistics
13.11 Progress

14. Numerical Optimization and Numerical Accuracy

14.1 Introduction to numerical optimization
14.2 Maximizing likelihood functions
14.3 Practical optimization
14.4 Numerical accuracy

Part IV: The Statistical Output of Multiple Equation Models

15. Unrestricted System

15.1 Introduction
15.2 System formulation
15.3 System estimation
15.4 System output
15.5 Graphic analysis
15.6 Recursive graphics
15.7 Dynamic analysis
15.8 System testing
15.9 Progress

16. Cointegrated VAR

16.0.1 Cointegration restrictions
16.1 Cointegrated VAR output
16.2 Graphic analysis
16.3 Recursive graphics

17. Simultaneous Euations Model

17.1 Model estimation
17.2 Model output
17.3 Graphic analysis
17.4 Recursive graphics
17.5 Dynamic analysis, forecasting and simulation
17.6 Model testing

Part V Appendices

A1 Algebra and Batch for Multiple Equation Modelling

A1.1 General restrictions
A1.1.1 Restrictions for testing
A1.1.2 Restrictions for estimation
A1.2 PcGive batch language

A2 Numerical Changes From Previous Versions

References
Author Index
Subject Index


Books

Doornik, J.A. and Hendry, D.F. (2006) with Manuel Arellano, Stephen Bond, H. Peter Boswijk and Marius Ooms. Econometric Modelling Using PcGive™ Volume III London: Timberlake Consultants Press. (ISBN 0-9542603-6-8)

Part I: Prologue

1. Introduction to Volume III

1.1 The PcGive system
1.2 Citation
1.3 World Wide Web

Part II: Volatility Models (GARCH) (with H. Peter Boswijk and Marius Ooms)

2. Introduction to Volatility Models (GARCH)

2.1 Introduction

3. Tutorial on GARCH Modelling

3.1 Estimating a GARCH(1,1) model
3.2 Evaluating the GARCH(1,1) model
3.3 Recursive estimation of the GARCH(1,1) model
3.4 GARCH(1,1) with regressors in the variance equation
3.5 GARCH(1,1) with Student t-distributed errors
3.6 EGARCH(1,1) GED-distributed errors
3.7 GARCH in mean
3.8 Asymmetric threshold GARCH

4. GARCH Implementation Details

4.1 GARCH model settings
4.2 Some implementation details
4.3 GARCH batch commands

Part III: Limited Dependent Models (LogitJD)

5. Discrete Choice Models

5.1 Introduction
5.2 Binary discrete choice
5.3 The binary logit and probit model
5.4 Multinomial discrete choice
5.5 Evaluation
5.6 Histograms
5.7 Norm observations
5.8 Observed versus predicted
5.9 Outlier analysis

6. Tutorial on Discrete Choice Modelling

6.1 Introduction
6.2 Data organization
6.3 Binary logit estimation
6.4 Binary probit estimation
6.5 Grouped logit estimation
6.6 Multinomial logit estimation
6.7 Conditional logit estimation

Part IV: Panel Data Models (DPD) (with Manuel Arellano and Stephen Bond)

7. Panel Data Models

7.1 Introduction
7.2 Econometric methods for static panel data models
7.3 Econometric methods for dynamic panel data models

8. Tutorial on Static Panel Data Modelling

8.1 Introduction
8.2 Data organization
8.3 Static panel data estimation

9. Tutorial on Dynamic Panel Data Modelling

9.1 Introduction
9.2 Data organization
9.3 One-step GMM estimation
9.4 Two-step GMM estimation
9.5 IV estimation
9.6 Combined GMM estimation

10. Panel Data Implementation Details
10.1 Transformations
10.2 Static panel-data estimation
10.3 Dynamic panel data estimation
10.4 Dynamic panel data, combined estimation
10.5 Panel batch commands

Part V: Time Series Models (ARFIMA) (with Marius Ooms)

11. Introduction to Time Series Models (ARFIMA)

12. Tutorial on ARFIMA Modelling

13. ARFIMA Implementation Details

13.1 Introduction
13.2 The Arfima model
13.3 Estimation
13.4 Estimation output
13.5 Estimation options
13.6 Forecasting
13.7 ARFIMA batch commands

Part VI: X12arima for GiveWin

14. Overview of X12arima for Oxmetrics

14.1 Introduction
14.2 X-12-ARIMA
14.3 Credits
14.4 Disclaimer
14.5 Limitations
14.6 Documentation
14.7 Census X-11 Seasonal Adjustment
14.8 X-12-ARIMA Seasonal Adjustment
14.9 regARIMA
14.10 X12arima menu commands

15. Tutorial on Seasonal Adjustment with X12arima for Oxmetrics

15.1 Introduction
15.2 Batch usage

16. Tutorial on ARIMA Modelling with X12arima for Oxmetrics

16.1 Introduction
16.2 regARIMA Model Example

17. Batch Usage

17.1 Additional Batch Commands
17.2 Specification Syntax, Additions and Differences

References

Author Index

Subject Index


Books

Doornik, J.A. and Hendry, D.F. (2006). PcGive Volume IV: Interactive Monte Carlo Experimentation in Econometrics Using PcNaive, London: Timberlake Consultants Press. (ISBN 0-9542603-7-6)

Part I: PcNaive Prologue

1 Introduction to PcNaive
1.1 General information
1.2 The special features of PcNaive
1.3 An overview of PcNaive
1.4 Documentation conventions
1.5 Using PcNaive documentation
1.6 Citation
1.7 World Wide Web
1.8 Installation

2 The Data Generation Processes and Models of PcNaive
2.1 AR(1) DGP
2.2 Static DGP
2.3 PcNaive and General DGP

Part II: PcNaive Tutorials

3 Introduction to Monte Carlo Experimentation

3.1 PcNaive
3.2 Monte Carlo
3.3 The data generation process
3.4 Simulation methods
3.5 The output of PcNaive

4 Tutorial for an IN[mu,sigma^2] Process

4.1 Introduction
4.2 Starting PcNaive
4.3 Designing the IN[mu,sigma^2] experiment
4.4 Running the IN[mu,sigma^2] experiment
4.5 Output from the IN[mu,sigma^2] experiment
4.6 Extended IN[mu,sigma^2] experiment
4.7 Graphical output

5 Tutorial on the Static DGP

5.1 Introduction
5.2 Designing the Static experiment

6 Tutorial for the AR(1) DGP

6.1 Introduction
6.2 Designing the AR(1) experiment
6.3 Recursive Monte Carlo

7 Tutorial on the PcNaive DGP

7.1 Introduction .
7.2 Example 1: AR(1) process
7.3 Example 2: unit roots
7.4 Example 3: cointegration
7.5 Example 4: autoregressive error and asymptotic analysis
7.6 Example 5: simultaneity and inter-estimator comparison
7.7 Example 6: cointegration analysis with dummies
7.8 Example 7: structural breaks

8 Tutorial on the General DGP

8.1 Introduction
8.2 Implementing the DGP
8.3 Specifying the equilibrium correction model

9 Tutorial on the PcNaive Code

9.1 Introduction
9.2 Program and class structure
9.3 A generated program

Part III: Learning Econometrics Using PcNaive

10 Introduction

11 Elementary Econometrics

11.1 The concept of variation
11.2 Shapes of some statistical distributions
11.3 How sample size affects distributional shape
11.4 Comparing t and normal
11.5 Convergence to normality: A Central Limit theorem at work
11.6 Bivariate regression theory really works
11.7 The accuracy of estimated coefficient standard errors
11.8 Fixed versus stochastic regressors
11.9 Omitted variables: compounding bias and variance
11.10 The effects of non-normal equation errors
11.11 The effects of data measurement errors

12 Intermediate econometrics

12.1 The impact of time: bias in autoregressive model estimation
12.2 Autocorrelated errors in regression equations
12.3 Inter-estimator comparisons: OLS and IV in a simultaneous system
12.4 The theory of Monte Carlo
12.5 Recursion in Monte Carlo applications
12.6 Test power: the impact of increasing sample size
12.7 The impact of dynamics on Chow test rejection frequencies
12.8 Nonsense regressions: the impact of evolution over time
12.9 Testing for unit roots
12.10 Testing for cointegration
12.11 Invalid weak exogeneity in a cointegration equation

13 Advanced econometrics

13.1 The role of asymptotic distribution theory in Monte Carlo
13.2 Distributions of inconsistent estimators
13.3 The impacts of structural breaks on econometric modelling
13.4 Testing the Lucas critique
13.5 Encompassing and non-nested hypothesis tests
13.6 Non-existence of moments
13.7 Cointegration analysis

Part IV: Monte Carlo Theory

14 Monte Carlo Methods

14.1 Stochastic solutions to deterministic problems
14.2 Distribution sampling
14.3 Sophisticated Monte Carlo
14.4 Invariance
14.5 Asymptotic analysis
14.6 Recursive Monte Carlo
14.7 Experimental design
14.8 Post-simulation analysis
14.9 Random number generation

15 Response surfaces

15.1 Introduction
15.2 The general approach
15.3 Experimental design, simulation, and post-simulation analysis
15.4 Heteroscedasticity
15.5 Testing the statistical adequacy of response surfaces
15.6 Numerical accuracy of response surfaces
15.7 Response surface formulations
15.8 Simpler forms of response surfaces
15.9 Conclusion

16 Asymptotic Analysis

16.1 Introduction
16.2 The DGP for asymptotic analysis
16.3 Companion form
16.4 Asymptotic moments
16.5 Asymptotic statistics

References
Author Index
Subject Index


Books

Koopman S.J., Harvey, A.C., Doornik, J.A. and Shephard, N. (2006). STAMP: Structural Time Series Analyser, Modeller and Predictor, London: Timberlake Consultants Press. (ISBN ISBN: 0-9542603-3-3).

I Prologue

1. Introduction

1.1 Overview of the STAMP book
1.2 General information
1.3 New in STAMP 7
1.4 The special features of STAMP
1.5 Basics of the program
1.6 Using STAMP documentation
1.7 Citation
1.8 World Wide Web
1.9 Tutorial data sets
1.10 Data sets used in exercises
1.11 STAMP and PcGive/PcFiml

2. Getting Started

2.1 Starting STAMP
2.2 Loading and viewing the tutorial data set
2.3 Oxmetrics graphics
2.4 Data transformations

II Tutorials on Structural Time Series Modelling

3. Introduction to univariate Modelling

3.1 Model formulation
3.2 Evaluating and testing the model
3.3 Exercises

4. Tutorial on components

4.1 Selection of components
4.2 Trend
4.3 Seasonal
4.4 Cycle
4.5 Autoregression
4.6 Exercises

5. Tutorial on interventions and explanatory variables

5.1 Interventions
5.2 Explanatory variables
5.3 Forecasts
5.4 Error correction and unobserved components
5.5 Statistical features of the models
5.6 Exercises

6. Applications in Macroeconomics and Finance

6.1 Trend-cycle decompositions
6.2 Expected inflation
6.3 Stochastic volatility
6.4 Seasonal adjustment and detrending
6.5 Missing Values
6.6 Exercises

7. Tutorial on Model Building and Testing

7.1 Specification of univariate models
7.2 Estimate a model
7.3 Model evaluation and testing
7.4 Forecasting and Backasting

III Statistical Treatment

8. Statistical Treatment of Model

8.1 Model definitions
8.2 State space form
8.3 Kalman filter

8.4 Parameter estimation

11. Statistical Model Output

11.1 Output from STAMP
11.2 Parameters
11.3 Final state
11.4 Goodness of fit
11.5 Components
11.6 Residuals
11.7 Auxiliary residuals
11.8 Predictive testing
11.9 Forecast

A1 STAMP Batch Language

References
Author Index
Subject Index


Books

Laurent S. and Peters, J.-P. (2006). G@RCH 4.2: Estimating and Forecasting ARCH Models, London: Timberlake Consultants Press. (ISBN 0-9542603-2-5).
1. Introduction

1.1 G@RCH
1.2 General Information
1.3 Installing and Running G@RCH 4.2

2. Getting Started

2.1 Starting G@RCH
2.2 Loading and Viewing the Tutorial Data Set
2.3 OxMetrics Graphics

3. Features and Package

3.1 Visual Inspection
3.2 Prelimenary Graphics
3.3 Prelimenary Tests
3.4 Conditional Mean Specification
3.5 Conditional Variance Specification: the ARCH Model
3.6 Estimation
3.7 Graphics
3.8 Misspecification Tests
3.9 Parameters Constraints
3.10 Forecasts
3.11 Further Options

4. Further GARCH Models

4.1 GARCH Model
4.2 EGARCH model
4.3 GJR Model
4.4 APARCH Model
4.5 IGARCH Model
4.6 RiskMetrics
4.7 Fractionally Integrated Models
4.8 Forecasting the Conditional Variance of GARCH-type models
4.9 Constrained Maximum Likelihood and Simulated Annealing
4.10 Accuracy of G@RCH

5. Estimating ARCH-type models using the Batch and Ox Versions

5.1 Using the "Batch Version"
5.2 Importing the G@RCH Class in Ox
5.3 Advanced Ox Usage
5.4 G@RCH and OxGauss

6. Value-at-Risk (VaR) estimation using G@RCH

6.1 VaR Models
6.2 Application

7. Realized Volatility and Intraday Seasonality

7.1 Introduction to diffusion models
7.2 Integrated Volatility
7.3 Realized Volatility
7.4 Microstructure Noise
7.5 Intraday Seasonality

8. Structure of the Program

8.1 Classes and Functions
8.2 Garch Member Functions
8.3 G@RCH Members Functions


References
Index


Books


Books

Doornik, J.A. and Hendry, D.F. (2001). GiveWin: An Interface to Empirical Modelling, London: Timberlake Consultants Press. (ISBN 0-9533394-3-2)

Chapter List of Figures . . . . . . xv
Chapter List of Tables . . . . . . xvi
Chapter Preface . . . . . . xvii
PART I GiveWin Prologue . . . . . . 1
Chapter  1 Introduction . . . . . . 3
1.1  GiveWin 2: what is new? . . . . . . 3
1.2  Documentation conventions . . . . . . 4
1.3  Help . . . . . . 4
1.4  Modular structure . . . . . . 4
1.5  Installation and upgrades . . . . . . 5
1.6  Registration . . . . . . 5
1.7  Interactive operation . . . . . . 6
1.8  Menu structure . . . . . . 6
1.9  Data samples . . . . . . 7
1.10  Data storage . . . . . . 7
1.11  Results storage . . . . . . 7
1.12  Filenames and their extensions . . . . . . 8
1.13  GiveWin languages . . . . . . 8
1.13.1  Algebra . . . . . . 8
1.13.2  GiveWin batch language . . . . . . 9
1.14  Registry . . . . . . 9
1.15  Citation . . . . . . 9
1.16  Contact information and World Wide Web . . . . . . 9
Chapter  2 Getting Started . . . . . . 10
2.1  Starting GiveWin . . . . . . 11
2.2  Registering GiveWin . . . . . . 11
2.3  Loading and viewing the tutorial data set . . . . . . 12
2.4  GiveWin graphics . . . . . . 15
2.4.1  A first graph . . . . . . 15
2.4.2  Multiple graphs . . . . . . 17
2.4.3  Graph saving and printing . . . . . . 19
2.4.4  Using the clipboard for graph pasting . . . . . . 19
2.5  Calculator . . . . . . 20
2.6  Algebra . . . . . . 22
2.7  The workspace . . . . . . 24
2.8  The main toolbar . . . . . . 26
Chapter  3 GiveWin Modules . . . . . . 27
3.1  Introduction . . . . . . 27
3.2  PcGive . . . . . . 28
3.3  TSP  29
3.3.1  TSP in interactive mode . . . . . . 29
3.3.2  TSP in batch mode . . . . . . 32
3.3.3  Explorer options . . . . . . 32
PART II GiveWin Tutorials . . . . . . 33
Chapter  4 Tutorial on Graph Editing . . . . . . 35
4.1  Multiple graphs . . . . . . 35
4.2  Graphics paper: areas and coordinates . . . . . . 35
4.3  Graphics view . . . . . . 37
4.4  Copy and paste . . . . . . 37
4.5  About line colour and style . . . . . . 38
4.6  Editing graphs: Graphics properties . . . . . . 38
4.7  Graphics setup . . . . . . 41
4.8  Drawing . . . . . . 43
4.9  Adding text and variables . . . . . . 44
4.10  Legends . . . . . . 46
4.11  Scaling variables . . . . . . 46
Chapter  5 Tutorial on Graphics . . . . . . 48
5.1  Descriptive graphics . . . . . . 48
5.2  Actual series . . . . . . 50
5.3  Transformed series . . . . . . 51
5.4  Scatter plots . . . . . . 52
5.5  Distribution . . . . . . 54
5.5.1  Histogram and density . . . . . . 54
5.5.2  Distribution . . . . . . 55
5.5.3  Frequency chart . . . . . . 55
5.5.4  Box plot . . . . . . 56
5.6  Time-series: ACF etc. . . . . . . 56
5.6.1  Autocorrelation function . . . . . . 56
5.6.2  Partial autocorrelation function (PACF) . . . . . . 57
5.6.3  Cross-correlation function . . . . . . 57
5.6.4  Spectrum and periodogram . . . . . . 57
5.7  QQ plots . . . . . . 58
5.8  Two series by a third . . . . . . 60
5.9  3-dimensional plots . . . . . . 63
5.9.1  Surface from scatter . . . . . . 63
5.9.2  Surface from table . . . . . . 65
Chapter  6 Tutorial on Data Input and Output . . . . . . 67
6.1  Open Data File and file types . . . . . . 68
6.2  From paper to GiveWin . . . . . . 69
6.2.1  Directly into the database . . . . . . 69
6.2.2  Using the clipboard; using GiveWin as editor . . . . . . 72
6.3  From GiveWin to disk . . . . . . 73
6.4  From disk to GiveWin . . . . . . 74
6.4.1  Loading GiveWin files . . . . . . 74
6.4.2  Loading spreadsheet files . . . . . . 75
6.4.3  Loading a human-readable (ASCII) file . . . . . . 76
6.5  Adding variables using the clipboard . . . . . . 77
6.6  Changing the sample period . . . . . . 77
6.6.1  Extending the sample period . . . . . . 77
6.6.2  Reset starting date . . . . . . 78
6.7  Appending data . . . . . . 78
Chapter  7 Tutorial on Data Transformation . . . . . . 79
7.1  Calculator . . . . . . 79
7.2  Advanced algebra . . . . . . 81
7.2.1  Introduction . . . . . . 81
7.2.2  Database for advanced algebra . . . . . . 81
7.2.3  Statistical distributions . . . . . . 81
7.2.4  Random number generators . . . . . . 85
7.2.5  Generating data . . . . . . 86
7.2.6  Smoothing data . . . . . . 89
PART III GiveWin Reference . . . . . . 91
Chapter  8 GiveWin Statistics . . . . . . 93
8.1  Actual values and scatter plots . . . . . . 93
8.2  Mean, standard deviation and variance . . . . . . 93
8.3  Correlogram, ACF . . . . . . 93
8.4  Partial autocorrelation function (PACF) . . . . . . 94
8.5  Cross-correlation function . . . . . . 94
8.6  Periodogram . . . . . . 95
8.7  Spectral density . . . . . . 95
8.8  Histogram, estimated density and distribution . . . . . . 96
8.9  Regression lines and smooths . . . . . . 96
8.9.1  Kernel smooth . . . . . . 97
8.9.2  Spline smooth . . . . . . 98
8.10  QQ plot . . . . . . 100
8.11  Box plot . . . . . . 100
Chapter  9 GiveWin file formats . . . . . . 101
9.1  GiveWin data files (.IN7/.BN7) . . . . . . 101
9.1.1  The .IN7 file format . . . . . . 101
9.1.2  The .BN7 file format . . . . . . 103
9.1.3  The information and ASCII data files (.IN7/.DAT) . . . . . . 103
9.2  Spreadsheet files (.XLS,.WKS,.WK1) . . . . . . 104
9.3  Data by variable and data by observation (.DAT) . . . . . . 106
9.4  Data with load info (.DAT) . . . . . . 106
9.5  Gauss data file (.DHT/.DAT) . . . . . . 107
9.6  Stata data file (.dta) . . . . . . 107
9.7  Results file (.OUT) . . . . . . 107
9.8  Batch file (.FL) . . . . . . 107
9.9  Algebra file (.ALG) . . . . . . 108
9.10  Ox file (.ox) . . . . . . 108
9.11  TSP file (.tsp) . . . . . . 108
9.12  Matrix file (.MAT) . . . . . . 108
9.13  GiveWin graphics file (.GWG) . . . . . . 108
9.14  PostScript file (.EPS) . . . . . . 110
9.15  PostScript file (.PS) . . . . . . 113
9.16  Enhanced meta file (.EMF) . . . . . . 113
9.17  Windows meta file (.WMF) . . . . . . 113
Chapter  10 Algebra Language . . . . . . 114
10.1  Introduction . . . . . . 114
10.2  Executing Algebra code . . . . . . 114
10.2.1  Calculator (Alt+c) . . . . . . 114
10.2.2  Algebra Editor (Alt+a) . . . . . . 115
10.2.3  Algebra from Results windows (Ctrl+a) . . . . . . 115
10.2.4  Algebra from a Batch file . . . . . . 115
10.3  Syntax of Algebra language . . . . . . 115
10.3.1  Variables and variable names . . . . . . 115
10.3.2  Comment . . . . . . 115
10.3.3  Constants . . . . . . 116
10.3.4  Algebra operators . . . . . . 116
  10.3.4.1 Arithmetic operators . . . . . . 116
  10.3.4.2 Relational and logical operators . . . . . . 116
  10.3.4.3 Algebra operator precedence . . . . . . 116
10.3.5  Assignment statements . . . . . . 116
10.3.6  Conditional assignment statements . . . . . . 117
10.3.7  Indexing . . . . . . 118
10.3.8  Keywords . . . . . . 119
10.4  Algebra Functions . . . . . . 119
10.4.1  Differencing and lag functions . . . . . . 119
10.4.2  ACF and periodogram functions . . . . . . 120
10.4.3  Sorting functions . . . . . . 120
10.4.4  Smoothing functions . . . . . . 122
  10.4.4.1 Hodrick--Prescott filter . . . . . . 122
  10.4.4.2 Kernel and spline smoothing . . . . . . 122
10.4.5  Algebra function list . . . . . . 123
Chapter  11 Batch Language . . . . . . 125
11.1  Introduction . . . . . . 125
11.2  Executing Batch commands . . . . . . 125
11.2.1  Batch Editor (Alt+b) . . . . . . 125
11.2.2  Batch from Results windows (Ctrl+b) . . . . . . 125
11.2.3  Batch from the File/Open command . . . . . . 126
11.2.4  Batch from the Windows Explorer . . . . . . 126
11.2.5  Batch from a Batch file . . . . . . 126
11.3  Batch files and default folders . . . . . . 126
11.4  Batch command summary . . . . . . 126
11.4.1  Comment . . . . . . 126
11.4.2  Command types . . . . . . 126
11.4.3  Default arguments . . . . . . 127
11.5  Batch commands . . . . . . 127
11.5.1  algebra . . . . . . 128
11.5.2  appenddata . . . . . . 128
11.5.3  appresults . . . . . . 128
11.5.4  break . . . . . . 128
11.5.5  chdir . . . . . . 128
11.5.6  closedata . . . . . . 129
11.5.7  command . . . . . . 129
11.5.8  database . . . . . . 129
11.5.9  draw . . . . . . 129
11.5.10  drawf . . . . . . 129
11.5.11  drawx . . . . . . 130
11.5.12  drawz . . . . . . 130
11.5.13  exit; . . . . . . 131
11.5.14  loadalgebra . . . . . . 131
11.5.15  loadbatch . . . . . . 131
11.5.16  loadcommand . . . . . . 131
11.5.17  loaddata . . . . . . 131
11.5.18  loadgraph . . . . . . 131
11.5.19  module . . . . . . 131
11.5.20  print . . . . . . 132
11.5.21  println . . . . . . 132
11.5.22  savedata . . . . . . 132
11.5.23  savedrawwindow . . . . . . 132
11.5.24  saveresults . . . . . . 132
11.5.25  setdraw . . . . . . 132
11.5.26  setdrawwindow . . . . . . 134
11.5.27  show . . . . . . 134
11.5.28  usedata . . . . . . 134
11.6  Examples . . . . . . 134
Chapter  12 GiveWin graphics . . . . . . 137
12.1  Graphics paper . . . . . . 137
12.2  Creating graphs . . . . . . 138
12.3  Printing graphs . . . . . . 138
12.4  Graphics formats . . . . . . 139
12.5  Saving and loading graphs . . . . . . 139
12.6  Graphics objects . . . . . . 139
12.6.1  Area . . . . . . 139
12.6.2  Line attributes . . . . . . 140
12.6.3  Regression, Scale . . . . . . 141
12.6.4  Axes . . . . . . 142
12.6.5  Legends . . . . . . 143
12.6.6  Histogram . . . . . . 143
12.6.7  Paper colour . . . . . . 143
12.6.8  Text . . . . . . 144
12.6.9  Lines . . . . . . 144
12.6.10  Adding, moving and deleting objects . . . . . . 144
12.6.11  Grid . . . . . . 144
12.6.12  Pointing . . . . . . 144
12.6.13  Graphics setup . . . . . . 144
12.7  Copy and paste . . . . . . 145
12.8  Graphics view size . . . . . . 145
12.9  Change graph scale . . . . . . 145
12.10  Graphics display mode . . . . . . 145
12.11  Graphs and sample selection . . . . . . 146
12.12  Text formatting . . . . . . 146
Chapter  13 GiveWin data management . . . . . . 149
13.1  Creating data . . . . . . 149
13.2  Database font . . . . . . 149
13.3  Database description . . . . . . 149
13.4  Printing data . . . . . . 149
13.5  Data formats . . . . . . 150
13.6  Print database info . . . . . . 150
13.7  Saving data . . . . . . 150
13.8  Navigation and editing . . . . . . 150
13.9  Renaming variables . . . . . . 151
13.10  Deleting variables . . . . . . 151
13.11  Reordering variables . . . . . . 151
13.12  Adding variables . . . . . . 151
13.13  Extending the sample period . . . . . . 151
13.13.1  Changing the sample period . . . . . . 151
13.14  Copy and paste . . . . . . 151
13.15  Appending data . . . . . . 152
Chapter References . . . . . . 153
Chapter Subject Index . . . . . . 155


Books

Hendry, D.F. and Krolzig, H.-M. (2001). Automatic Econometric Model Selection using PcGets 1.0, London: Timberlake Consultants Press. (ISBN 0-9533394-9-1 )

Chapter List of Figures . . . . . . xvii
Chapter List of Tables . . . . . . xviii
Chapter Preface . . . . . . xix
PART I PcGets Prologue . . . . . . 1
Chapter  1 Introduction to PcGets . . . . . . 3
1.1  The Econometrics of PcGets . . . . . . 3
1.2  PcGets model selection . . . . . . 6
1.3  The special features of PcGets . . . . . . 7
Efficient model formulation . . . . . . 9
Automatic model selection . . . . . . 10
Thorough evaluation . . . . . . 11
1.4  Documentation conventions . . . . . . 11
1.5  Using PcGets documentation . . . . . . 12
1.6  An overview of PcGets menus . . . . . . 13
1.7  Citation . . . . . . 13
1.8  World Wide Web . . . . . . 13
1.9  Some data sets . . . . . . 13
Chapter  2 Getting Started . . . . . . 14
2.1  Starting PcGets . . . . . . 14
2.2  Loading and viewing the tutorial data set . . . . . . 15
2.3  GiveWin graphics . . . . . . 18
2.3.1  A first graph . . . . . . 18
2.3.2  Graph saving and printing . . . . . . 19
2.4  Calculator . . . . . . 21
PART II Tutorials on PcGets . . . . . . 23
Chapter  3 Tutorial on Model Formulation and Estimation . . . . . . 25
3.1  Starting PcGets . . . . . . 25
3.1.1  Lagged and deterministic variables . . . . . . 27
3.2  Formulating a model . . . . . . 28
3.3  Ordinary Least Squares (OLS) estimation . . . . . . 29
3.4  Model output . . . . . . 30
3.4.1  Equation estimates . . . . . . 30
3.4.2  Mis-specification tests . . . . . . 31
3.5  Instrumental Variables Estimation (IVE) . . . . . . 32
3.5.1  Structural estimates . . . . . . 33
3.6  Progress . . . . . . 34
Chapter  4 Tutorial on Post-Estimation Model Evaluation . . . . . . 36
4.1  Graphical evaluation . . . . . . 36
4.1.1  Graphical analysis dialog . . . . . . 36
4.1.2  Actual and fitted values . . . . . . 37
4.1.3  Residual analysis . . . . . . 38
4.2  Dynamic analysis . . . . . . 39
4.3  Analysis of forecasts . . . . . . 40
4.4  Collinearity analysis . . . . . . 42
4.4.1  An I(0) transformation . . . . . . 43
4.5  Specification tests . . . . . . 45
4.5.1  Exclusion restrictions . . . . . . 45
4.5.2  Linear restrictions . . . . . . 45
4.5.3  Omitted variables . . . . . . 46
4.6  Recursive analysis . . . . . . 47
4.6.1  Recursive OLS estimation . . . . . . 47
4.6.2  Recursive instrumental variables . . . . . . 48
4.6.3  Recursive graphics . . . . . . 48
Chapter  5 Tutorial on Automatic Model Selection . . . . . . 51
5.1  Formulating general models . . . . . . 51
5.2  Model settings for selection . . . . . . 52
5.3  Testimation - GETS . . . . . . 53
5.4  Testimation - GETSIVE . . . . . . 56
5.5  Sequential simplification of an I(0) GUM . . . . . . 57
5.5.1  Pre-search procedures . . . . . . 58
5.5.2  Multi-path searches . . . . . . 60
5.5.3  Omitted variables . . . . . . 61
5.5.4  Outlier removal . . . . . . 62
5.5.5  Iteration symbols . . . . . . 63
5.6  Pre-programmed selection settings . . . . . . 64
5.6.1  Liberal strategy . . . . . . 64
5.6.2  Conservative strategy . . . . . . 64
5.6.3  Multi-path selection . . . . . . 64
5.7  Constrained selection: using fixed variables . . . . . . 65
5.8  Expert settings . . . . . . 67
5.8.1  Significance levels for selection statistics . . . . . . 67
5.8.2  Block search t-probabilities . . . . . . 68
5.8.3  Information criteria . . . . . . 68
5.8.4  Sample split analysis . . . . . . 69
5.8.5  Outlier-correction criterion . . . . . . 69
5.8.6  Mis-specification test settings . . . . . . 69
5.8.7  Re-setting selection strategies . . . . . . 70
5.9  Applying PcGets substantively . . . . . . 70
5.9.1  UK money demand . . . . . . 70
5.9.2  UK consumers' expenditure . . . . . . 71
5.10  Advice on using PcGets in modelling . . . . . . 73
Chapter  6 Tutorial on Cross-section Model Selection . . . . . . 75
6.1  Formulating a regression . . . . . . 75
6.2  Model selection . . . . . . 76
6.2.1  Selection output . . . . . . 78
6.3  Regression graphics . . . . . . 81
6.4  Alternative selection strategies . . . . . . 82
6.5  Fixing selected variables . . . . . . 83
Chapter  7 Tutorial on Batch Usage . . . . . . 84
7.1  Batch codes generated by PcGets . . . . . . 84
7.2  Example . . . . . . 85
7.3  Editing batch files . . . . . . 88
7.4  Create your own liberal and conservative strategy . . . . . . 90
Chapter  8 Tutorial on Modelling VARs . . . . . . 94
8.1  Introduction . . . . . . 94
8.2  General-to-specific reductions of VAR models . . . . . . 94
8.3  A Small Monetary VAR of the UK . . . . . . 96
8.4  Conclusion . . . . . . 98
PART III The Econometrics of PcGets . . . . . . 99
Chapter  9 The Theory of Reduction . . . . . . 101
9.1  Introduction . . . . . . 101
9.1.1  Empirical models . . . . . . 102
9.2  Deriving the LDGP . . . . . . 102
9.2.1  DGP . . . . . . 103
9.2.2  Data transformations and aggregation . . . . . . 103
9.2.3  Parameters of interest . . . . . . 103
9.2.4  Data partition . . . . . . 103
9.2.5  Marginalization . . . . . . 104
9.2.6  Sequential factorization . . . . . . 104
9.2.7  Mapping to I(0) . . . . . . 104
9.2.8  Conditional factorization . . . . . . 104
9.2.9  Constancy . . . . . . 105
9.2.10  Lag truncation . . . . . . 105
9.2.11  Functional form . . . . . . 105
9.3  The econometric model . . . . . . 106
9.4  Econometric concepts as measures of no information loss . . . . . . 106
9.5  A taxonomy of evaluation information . . . . . . 107
9.6  Dominance . . . . . . 108
9.6.1  Information criteria . . . . . . 108
9.6.2  Encompassing . . . . . . 109
Chapter  10 The Econometrics of Model Selection . . . . . . 110
10.1  Introduction . . . . . . 110
10.2  The selection stages of PcGets . . . . . . 111
10.2.1  Formulating the GUM . . . . . . 111
  10.2.1.1Integrated variables . . . . . . 112
10.2.2  Mis-specification tests . . . . . . 113
  10.2.2.1Significant mis-specification tests . . . . . . 113
  10.2.2.2Integrated variables . . . . . . 113
10.2.3  Pre-search reductions . . . . . . 113
10.2.4  Multiple search paths . . . . . . 114
10.2.5  Encompassing . . . . . . 114
10.2.6  Information criteria . . . . . . 114
10.2.7  Sub-sample reliability . . . . . . 115
10.2.8  Type I and type II errors . . . . . . 115
10.3  Analyzing the algorithm . . . . . . 115
10.3.1  Costs of inference and costs of search . . . . . . 116
10.4  Selection probabilities . . . . . . 116
10.5  Deletion probabilities . . . . . . 118
10.6  Monte Carlo evidence on PcGets . . . . . . 120
Chapter  11 Refuting Potential Criticisms of Gets . . . . . . 122
11.1  Introduction . . . . . . 122
11.2  Data-based model selection . . . . . . 123
11.3  Measurement without theory . . . . . . 123
11.4  Data mining . . . . . . 124
11.5  Pre-test biases . . . . . . 125
11.6  Ignoring selection effects . . . . . . 125
11.7  Spurious significance from repeated testing . . . . . . 127
11.8  Arbitrary choices of significance levels . . . . . . 128
11.9  Lack of identification . . . . . . 129
11.10  Path dependence of selection . . . . . . 129
11.11  Implications . . . . . . 130
11.12  What are the alternatives? . . . . . . 131
11.12.1  The problems of simple-to-general modelling . . . . . . 132
11.12.2  Retaining the initial general model . . . . . . 132
11.12.3  Selecting models by minimizing an information criterion . . . . . . 133
11.12.4  Testing theory models . . . . . . 133
11.12.5  Other model-simplification approaches . . . . . . 134
11.12.6  Gets . . . . . . 134
11.12.7  Non-nested hypothesis tests and encompassing . . . . . . 134
11.12.8  Bayesian model comparisons . . . . . . 135
PART IV Statistics of PcGets . . . . . . 137
Chapter  12 Model Estimation Statistics . . . . . . 139
12.1  Introduction . . . . . . 139
12.2  Model formulation . . . . . . 139
12.3  OLS estimation . . . . . . 140
12.3.1  The algebra of ordinary least squares . . . . . . 141
12.3.2  *Correlations . . . . . . 141
12.3.3  Estimated regression equation . . . . . . 141
12.3.4  Variances . . . . . . 142
  12.3.4.1Standard errors of the regression coefficients . . . . . . 142
12.3.5  Some intermediate algebra . . . . . . 142
12.3.6  Functions of normal variables: chi^2, t and F distributions . . . . . . 143
12.3.7  Distributional results . . . . . . 144
12.3.8  t-values and t-probability . . . . . . 146
12.3.9  Subsets of parameters . . . . . . 146
12.3.10  *Parameter reliability statistics . . . . . . 148
12.3.11  Log-likelihood (LogLik) . . . . . . 148
12.3.12  R^2: squared multiple correlation coefficient . . . . . . 148
12.3.13  Equation standard error . . . . . . 149
12.3.14  Residual sum of squares (RSS) . . . . . . 149
12.3.15  Information criteria . . . . . . 149
12.4  Recursive OLS estimation . . . . . . 149
12.5  Forecasting . . . . . . 150
12.5.1  Analysis of 1-step forecasts . . . . . . 151
12.5.2  Dynamic forecasting . . . . . . 152
12.6  Instrumental variables estimation . . . . . . 153
12.6.1  The algebra of IVE . . . . . . 154
12.6.2  Testing hypotheses on beta . . . . . . 155
12.6.3  Specification chi^2 . . . . . . 155
12.6.4  Recursive IV estimation . . . . . . 156
Chapter  13 Post-estimation Evaluation Statistics . . . . . . 157
13.1  Introduction . . . . . . 157
13.2  Graphic analysis . . . . . . 158
13.3  Recursive graphics . . . . . . 158
13.4  Dynamic analysis . . . . . . 160
13.4.1  Static long-run solution . . . . . . 161
13.4.2  Roots of the autoregressive lag polynomial . . . . . . 161
13.5  Collinearity analysis . . . . . . 162
13.6  Forecasts . . . . . . 162
13.6.1  Constancy tests . . . . . . 162
13.7  Diagnostic tests . . . . . . 162
13.7.1  Portmanteau statistic . . . . . . 163
13.7.2  Test for autocorrelated residuals . . . . . . 164
13.7.3  Test for autocorrelated squared residuals . . . . . . 164
13.7.4  Test for normality . . . . . . 165
13.7.5  Test for heteroscedasticity using squares . . . . . . 165
13.7.6  Test for heteroscedasticity using squares and cross-products . . . . . . 166
13.7.7  Small-sample properties of the mis-specification tests . . . . . . 166
13.8  Linear restrictions test . . . . . . 168
13.9  Exclusion restrictions . . . . . . 168
13.10  Tests for omitted variables . . . . . . 168
13.11  Encompassing tests . . . . . . 169
PART V PcGets Menus and Options . . . . . . 171
Chapter  14 PcGets Menus . . . . . . 173
14.1  Overview . . . . . . 173
14.1.1  File Menu . . . . . . 173
14.1.2  Package Menu . . . . . . 173
14.1.3  Model Menu . . . . . . 173
14.1.4  Test Menu . . . . . . 174
14.1.5  Help Menu . . . . . . 174
14.2  File menu (Alt+f) . . . . . . 174
14.3  Package Menu . . . . . . 175
14.4  Model menu (Alt+m) . . . . . . 175
14.4.1  Formulate (Alt+y) . . . . . . 175
14.4.2  Model settings (Alt+s) . . . . . . 178
14.4.3  Estimate (Alt+l) . . . . . . 178
  14.4.3.1 Estimate Model dialog box . . . . . . 179
  14.4.3.2 Ordinary least squares (OLS) . . . . . . 179
  14.4.3.3 Testimation (GETS) . . . . . . 179
  14.4.3.4 Instrumental variables estimation (IVE) . . . . . . 180
  14.4.3.5 Instrumental variables testimation (GETSIVE) . . . . . . 180
14.4.4  Estimation output . . . . . . 180
  14.4.4.1 Estimated regression equation . . . . . . 180
  14.4.4.2 Summary regression statistics . . . . . . 181
14.4.5  Progress . . . . . . 184
  14.4.5.1 Progress dialog box . . . . . . 184
  14.4.5.2 Progress . . . . . . 184
14.4.6  Options (Alt+o) . . . . . . 184
14.5  Test menu (Alt+t) . . . . . . 185
14.5.1  Graphic analysis . . . . . . 185
  14.5.1.1 Graphic analysis dialog box . . . . . . 185
  14.5.1.2 Data density and histogram . . . . . . 185
  14.5.1.3 Correlogram (ACF, PACF) . . . . . . 186
  14.5.1.4 Spectrum . . . . . . 186
14.5.2  Recursive analysis . . . . . . 187
  14.5.2.1 Recursive analysis dialog box . . . . . . 187
  14.5.2.2 Recursive estimation . . . . . . 188
  14.5.2.3 Recursive analysis output . . . . . . 188
14.5.3  Dynamic analysis . . . . . . 188
14.5.4  Collinearity analysis . . . . . . 189
14.5.5  Forecast . . . . . . 189
14.5.6  Omitted variables . . . . . . 191
14.5.7  Linear restrictions . . . . . . 191
  14.5.7.1 Linear restrictions dialog box . . . . . . 191
14.5.8  Exclusion restrictions . . . . . . 192
14.5.9  LaTeX output . . . . . . 193
14.5.10  PcGets settings . . . . . . 193
14.5.11  PcGets batch output . . . . . . 193
14.5.12  Store in database . . . . . . 193
14.6  Help menu (Alt+h) . . . . . . 194
14.6.1  Help topics . . . . . . 194
14.6.2  Contents . . . . . . 194
14.6.3  About . . . . . . 194
Chapter  15 Model-selection Strategy Options . . . . . . 195
15.1  Introduction . . . . . . 195
15.2  Model settings dialog box . . . . . . 195
15.2.1  PcGets testimation algorithm . . . . . . 195
15.2.2  Research strategy . . . . . . 195
15.2.3  Reporting . . . . . . 196
15.3  Model settings options . . . . . . 196
15.3.1  Outlier correction . . . . . . 196
15.3.2  F pre-search testing (lag order pre-selection) . . . . . . 196
15.3.3  F pre-search testing (top-down) . . . . . . 196
15.3.4  F pre-search testing (bottom-up) . . . . . . 196
15.3.5  Sample-split analysis . . . . . . 196
15.3.6  Sample-size adjusted significance level . . . . . . 197
15.3.7  Modelling strategies . . . . . . 197
15.3.8  Reporting . . . . . . 198
15.4  Options (Expert user's strategy) . . . . . . 198
15.4.1  Significance levels . . . . . . 198
15.4.2  F pre-search tests) . . . . . . 199
15.4.3  Block search . . . . . . 199
15.4.4  Selection criteria for final model . . . . . . 200
15.4.5  Sample-split analysis . . . . . . 200
15.4.6  Outlier detection . . . . . . 201
15.4.7  Diagnostic tests . . . . . . 201
15.4.8  Test options . . . . . . 201
15.4.9  Reset default . . . . . . 202
Chapter  16 PcGets Batch Language . . . . . . 203
16.1  Introduction . . . . . . 203
16.2  PcGets batch commands . . . . . . 203
16.2.1  Batch control of the Model Formulation dialog . . . . . . 205
16.2.2  Batch control of the Model Estimation dialog . . . . . . 206
16.2.3  Batch control of the Model Settings dialog . . . . . . 206
16.2.4  Batch control of the Options dialog . . . . . . 207
16.2.5  Batch control of the test dialog . . . . . . 209
16.3  Illustrative batch code . . . . . . 211
Chapter  A1 The PcGets Algorithm . . . . . . 212
A1.1  The PcGets algorithm . . . . . . 212
Chapter References . . . . . . 215
Chapter Author Index . . . . . . 225
Chapter Subject Index . . . . . . 227
Cramer J.S. (2001). An Introduction to the Logit Model for Economists, London: Timberlake Consultants Press. (ISBN 0-9533394-6-7).

1. Introduction;
2. The binary model;
3. Maximum likelihood estimation of the binary logit model;
4. Some statistical tests and measures of fit;
5. Outliers, misclassification of outcomes, and omitted variables;
6. Analyses of separate samples;
7. The standard multinomial logit model;
8. Discrete choice of random utility models;


Home | Software | Training | Bookshop | Students | Support | Purchasing | About Us