OxMetrics Books' Tables of Contents


Books

Doornik, J.A. (2009). An Introduction to OxMetrics 6, London: Timberlake Consultants Press. (ISBN: 978-0-9552127-3-4)

Part I: Getting Started with OxMetrics

1. Introduction

1.1 Supported Platforms
1.2 What is new?
1.3 What was new in OxMetrics 5
1.3.1 What was new in OxMetrics 4
1.4 Availability
1.5 Citation
1.6 Help
1.7 Installation on Windows
1.8 Installation on OS X
1.9 Installation on Linux
1.10 Modular structure
1.11 Registration
1.12 Updates
1.13 Algebra
1.14 Batch
1.15 Data storage
1.16 Filenames and their extensions
1.17 Output storage
1.18 Sample periods
1.19 Status bar
1.20 Tool bars
1.21 Documentation conventions

2. Installation
2.1 Windows Vista, Windows XP, Windows 2000
2.2 Windows Vista 64-bit, Windows XP 64
2.3 OS X 10.5, 10.4
2.4 Linux 32-bit
2.5 Linux 64-bit

3. Getting Started: Windows

3.1 Starting OxMetrics
3.2 Registering OxMetrics
3.3 Loading and viewing the tutorial data set
3.4 OxMetrics graphics
3.4.1 A first graph
3.4.2 Multiple graphs
3.4.3 Graph saving and printing
3.4.4 Using the clipboard for graph pasting
3.5 Calculator
3.6 Algerba
3.7 The workspace

4. Getting Started: OS X

4.1 Starting OxMetrics
4.2 Registering OxMetrics
4.3 Loading and viewing the tutorial data set
4.4 OxMetrics graphics
4.4.1 A first graph
4.4.2 Multiple graphs
4.4.3 Graph saving and printing
4.4.4 Using the clipboard for graph pasting
4.5 Calculator
4.6 Algerba
4.7 The workspace

5. Getting Started: Linux

5.1 Starting OxMetrics
5.2 Registering OxMetrics
5.3 Loading and viewing the tutorial data set
5.4 OxMetrics graphics
5.4.1 A first graph
5.4.2 Multiple graphs
5.4.3 Graph saving and printing
5.4.4 Using the clipboard for graph pasting
5.5 Calculator
5.6 Algerba
5.7 The workspace

6. OxMetrics Modules

6.1 OxMetrics Modules
6.2 Financial data
6.3 Weekly and daily data
6.4 PcGive
6.4.1 Formulate a model
6.5 TSP
6.5.1 TSP in interactive mode
6.5.2 TSP Help
6.5.3 TSP in batch mode
6.5.4 Explorer options

Part II: OxMetrics Tutorials

7. Tutorial on Graphics

7.1 Descriptive graphics
7.2 Actual series with optimal transformations
7.3 Multiple series with optional transformations
7.4 Scatter plots
7.5 Distribution
7.5.1 Density estimation: Histogram and density
7.5.2 Distribution
7.5.3 Frequencies
7.5.4 Box Plor
7.6 Time-series: AFC etc
7.6.1 Autocorrelation function or correlogram
7.6.2 Partial autocorrelation function
7.6.3 Cross-correlation function
7.6.4 Spectrum and periodogram
7.7 QQ plots
7.8 Two series by third
7.9 3-dimensional plots
7.9.1 Surface from scatter
7.9.2 Surface from table
7.10 Conclusions

8. Tutorial on Graph Editining

8.1 Multiple graphs
8.2 Graphics paper: areas and coordinates
8.3 Graphics view
8.4 New Data Plot Window
8.5 Copy and paste
8.6 About line colour and style
8.7 Editing graphs: Graphics properties
8.8 Graphics setup
8.9 Adding and removing from a graph
8.10 Drawing
8.11 Adding text and variables
8.12 Legends
8.13 Scaling variables

9. Tutorial on Data Input and Output

9.1 Open Data File and files types
9.2 From paper to OxMetrics
9.2.1 Directly into the database
9.2.2 Using the clipboard; using OxMetrics as editor
9.3 From OxMetrics to disk
9.4 From disk to OxMetrics
9.4.1 Loading OxMetrics files
9.4.2 Loading spreadsheet files
9.4.3 Loadinf a human-readable (text) file
9.5 Adding variables using the clipboard
9.6 Changing the sample period
9.6.1 Extending the sample period
9.6.2 Reset starting date
9.7 Appending data
9.8 Working with daily and weekly data
9.8.1 Using Change Sample to create a daily database
9.8.2 Using Algebra to create a daily database

10. Tutorial on Data Transformaion

10.1 Calculator
10.2 Advanced algebra
10.2.1 Introducion
10.2.2 Database for advanced algebra
10.2.3 Statistical distribution
10.2.4 random number generators
10.2.5 Generating data
10.2.6 Smoothing data

Part II: OxMetrics Reference

11. OxMetrics Statistics

11.1 Actual series and scatter plots
11.2 Mean, standard deviation and variance
11.3 Autocorrelation function (ACF) or covariogram
11.4 Partial authocorrelation function(PACF)
11.5 Correlogram
11.6 Cross-correlation function
11.7 Periodogram
11.8 Spectral density
11.9 Histogram,estimated density and distribution
11.10 Regression lines and smooths
11.10.1 Kernel smooth
11.10.2 Spline smooth
11.11 QQ plot
1112 Box plot
11.13 Exponentially-weighted moving average (EWMA)
11.14 Exponentially- weighted moving correlation

12 OxMetrics file formats

12.1 OxMetrics data files (.in7/ .bn7)
12.1.1 The .in7 file format
12.1.2 The .bn7 file format
12.1.3 The information and ASCII data files (.in7/.dat)
12.2 Spreadsheet files (.xls, .wks, .cvs)
12.3 Data by observation (.dat)
12.4 Data with load info (.dat)
12.5 Gauss data file (.dht/ .dat)
12.6 Stata data file (.dta)
12.7 Results file (.out)
12.8 Batch file (.fl)
12.9 Algebra file (.alg)
12.10 Ox file (.ox)
12.11 TSP file (.tsp)
12.12 Matrix file (.mat)
12.13 OxMetrics graphics file (.gwg)
12.14 PostScript file(.eps)
12.15 PostScript file(.ps)
12.16 Enchanced meta file (.emf)
12.17 Windows meta file (.wmf)

13. Algebra Language

13.1 Introduction
13.2 Executing Algebra code
13.2.1 Calculator (Alt+c)
13.2.2 Algebra Editor (Alt+a)
13.2.3 Algebra from Results windows(Crtl+a)
13.2.4 Algebra from a batch file
13.3 Syntax of Algebra language
13.3.1 variable and variable names
13.3.2 Comment
13.3.3 Constants
13.3.4 Algebra operators
13.3.4.1 Arithmetic operators
13.3.4.2 Relational andlogical operators
13.3.4.3 Algebra opearator precedence
13.3.5 Assignment statements
13.3.6 Conditionmal assignemtn statements
13.3.7 Indexing
13.3.8 Keywords
13.4 Algebra Functions
13.4.1 Differencing and lag functions
13.4.2 ACF and periodogram functions
13.4.3 Sorting functions
13.4.4 Smoothing functions
13.4.4.1 Hodrick-Prescott filter
13.4.4.2 Kernel and spline smoothing
13.4.4.3 Exponentially-weighed moving average and corelation
13.4.4.4 Date and time functions
13.5 Algebra function sumarry

14. Batch Languages

14.1 Introduction
14.2 Excecuting Batch commnads
14.2.1 Batch Editor (Alt+b)
14.2.2 Batch from Results windows (Ctrl+b)
14.2.3 Batch from the File/Open command
14.2.4 Batch from the Windows Explorer
14.2.5 Batch from a Batch file
14.3 Batch files and default folders
14.4 Batch commands summary
14.4.1 Comment
14.4.2 Command types
14.4.3 Default arguments
14.5 Batch commands
14.5.1 algebra {...}
14.5.2 appenddata ("filename", "group"="")
14.5.3 appresults ("filename")
14.5.4 break
14.5.5 chdir ("path")
14.5.6 closedata ("databasename")
14.5.7 command ("command_line")
14.5.8 database ("name", year1, period1, year2, period2, freq)
14.5.9 draw (area, "y", "mode"="")
14.5.10 drawf (area, "y", "function", d1=0, d2=0)
14.5.11 drawx (area, "y","x" "mode"="")
14.5.12 drawz (area, "y","x" "mode"="")
14.5.13 exit
14.5.14 loadalgebra ("filename")
14.5.15 loadbatch ("filename")
14.5.16 loadcommand ("filename")
14.5.17 loaddata ("filename")
14.5.18 loadgraph("filename")
14.5.19 module ("name")
14.5.20 package ("packagename", "modeltype"="")
14.5.21 print ("text)
14.5.22 println ("text")
14.5.23 savedata ("filename")
14.5.24 savedrawwindow ("filename", "windows"="")
14.5.25 saveresults ("name")
14.5.26 setdraw ("option", i1=0, i2=0, i3=0, i4=0, i5=0)
14.5.27 setdrawwindow ("name")
14.5.28 show
14.5.29 usedata ("databasename", i1=0)
14.6 Examples

15. OxMetrics graphics

15.1 Graphic paper
15.2 Creating graphs
15.2.1 Actual series with optional transformations
15.2.2 Multiple series with optional transformations
15.2.3 Scatter plots
15.2.4 Distribution
15.2.5 Time-series: ACF etc
15.2.6 QQ plots
15.2.7 Two series by a third
15.2.8 3-dimensional plots
15.3 Printing graphs
15.4 Graphics formats
15.5 Saving and loading graphs
15.6 Editing Graphs
15.6.1 Graph layout
15.6.2 Area layour
15.6.3 Variable against time, scatter or 3D
15.6.3.1 Error bars
15.6.3.2 Regression, Scale
15.6.4 Axes
15.6.4.1 Settings for non-default labelling
15.6.4.2 Location and transformation
15.6.4.3 Style
15.6.4.4 Label Style
15.6.5 Legend Style
15.6.6 Histogram
15.6.7 Copy properties to other areas
15.6.8 Text
15.6.9 Lines and symbols
15.6.10 Adding, moving and deleting objects
15.6.11 Pointing
15.6.12 Graphics setup
15.7 Copy and paste
15.8 Graphs and sample selection
15.9 Text formatting

16. OxMetrics data management

16.1 Creating data
16.2 Database font
16.3 Databae description
16.4 Printing data
16.5 Data formats
16.6 Summary Statistics
16.7 Saving data
16.8 Navigation and editing
16.9 Renaming variables
16.10 Deleting variables
16.11 Reordering variables
16.12 Adding variables
16.13 Extending or reducing the sample period
16.13.1 Changing the sample period
16.14 Copy and paste
16.15 Appending data
16.16 Daily, weekly and timed data

References
Index


Books

Doornik, J.A. (2009). An Object-Oriented Matrix Language Ox™ 6, London: Timberlake Consultants Press. (978-0-9552127-5-8).

1. Prologue

1.1 What is 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 OX6PATH
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 Optimizing for speed
3.12 OxGauss

4. Numerical accuracy

5. How to...

6. Some matrix algebra

Part II: Function and Language Reference

7. Function summary

8. Function reference

9. Predefined Constants

9.1 Missing values (NaN)
9.2 Infinity

10. Graphics function reference

10.1 Introduction
10.2 Symbol and line types
10.3 Function reference

11. Packages

11.1 Arma package
11.2 Maximization 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. (2007). Introduction to Ox, London: Timberlake Consultants Press. (ISBN 978-0-9552127-4-1).

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. (2009). Empirical Econometric Modelling Using PcGive 13™ Volume I, London: Timberlake Consultants Press. (ISBN 978-0-9552127-8-9)


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
7.2.1 Structural estimates
7.2.2 Reduced forms
7.3 Autoregressive least squares (RALS)
7.3.1 Optimization
7.3.2 RALS model evaluaation
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 12 to 13
A3.2 From version 9 to 10
A3.3 From version 8 to 9
A3.4 From version 7 to 8

Author Index
Subject Index


Books

Doornik, J.A. and Hendry, D.F. (2009). Modelling Dynamic Systems Using PcGive 13™ Volume II, London: Timberlake Consultants Press. (ISBN 978-0-9552127-9-6)

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 Citation
1.7 World Wide Web
1.8 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 System reduction usisng Autometrics
3.9 Dynamic analysis
3.10 Recursive estimation
3.11 Batch editor
3.12 Forecasting
3.13 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 Lükepohl 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. (2009) with Manuel Arellano, Stephen Bond, H. Peter Boswijk and Marius Ooms. Econometric Modelling Using PcGive 13™ Volume III London: Timberlake Consultants Press. (ISBN 978-0-9552127-7-2)

Part I: Prologue

1. Introduction to Volume III

1.1 The PcGive system
1.2 Citation
1.3 World Wide Web

Part II: Limited Dependent Models (LogitJD)

2. Discrete choice models

2.1 Introduction
2.2 Binary discrete choice
2.3 The binary logit and probit model
2.4 Multinominal discrete choice
2.5 Evaluation
2.6 Histograms
2.7 Norm observations
2.8 Observed versus predicted
2.9 Outlier analysis

3. 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 III: Panel Data Models (DPD) (with Manuel Arellano and Stephen Bond)

4. Panel Data Models

4
.1 Introduction
4.2 Econometric methods for static panel data models
4.3 Econometric methods for dynamic panel data models

5. Tutorial on Static Panel Data Modelling

5
.1 Introduction
5.2 Data organization
5.3 Static panel data estimation

6. Tutorial on Dynamic Panel Data Modelling

6.1 Introduction
6.2 Data organization
6.3 One-step GMM estimation
6.4 Two-step GMM estimation
6.5 IV estimation
6.6 Combined GMM estimation

7. Panel Data Implementation Details

7.1 Transformations
7.2 Static panel-data estimation
7.3 Dynamic panel data estimation
7.4 Dynamic panel data, combined estimation
7.5 Panel batch commands

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

8. Introduction to Volatility Models (GARCH)

8.1 Introduction

9. Tutorial on GARCH Modelling

9.1 Estimating a GARCH(1,1) model
9.2 Evaluating the GARCH(1,1) model
9.3 Recursive estimation of the GARCH(1,1) model
9.4 GARCH(1,1) with regressors in the variance equation
9.5 GARCH(1,1) with Student t-distributed errors
9.6 EGARCH(1,1) GED-distributed errors
9.7 GARCH in mean
9.8 Asymmetric threshold GARCH
9.9 GARCH batch usage

10. GARCH Implementation Details

10.1 GARCH model settings
10.2 Some implementation details
10.3 GARCH 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: Regime Switching Models (Switching)

14 Regime Switching Models

14.1 Introduction
14.2 Markov-switching models

15 Tutorial on Regime Switching Modelling

15.1 Estimating a 2-regime MS dynamic regression model, 1985(1)-2009(1)
15.2 Estimating an MS-DR(2) model, 1948(2)-1984(4)
15.3 MS-DR(3) model with switching variance, 1948(2)-2009(1)
15.4 Estimating an MS-AR model: replicating Hamilton's estimates

16 Regime Switching Implementation Detalils

16.1 Switching mdel settings
16.2 Regime Switching bath commands

Part VII: X12arima for OxMetrics

17. Overview of X12arima for Oxmetrics

17.1 Introduction
17.2 X-12-ARIMA
17.3 Credits
17.4 Disclaimer
17.5 Limitations
17.6 Documentation
17.7 Census X-11 Seasonal Adjustment
17.8 X-12-ARIMA Seasonal Adjustment
17.9 regARIMA
17.10 X12arima menu commands

18. Tutorial on Seasonal Adjustment with X12arima for Oxmetrics

18.1 Introduction
18.2 Batch usage

19. Tutorial on ARIMA Modelling with X12arima for Oxmetrics

19.1 Introduction
19.2 regARIMA Model Example

20. Batch Usage

20.1 Additional Batch Commands
20.2 Specification Syntax, Additions and Differences

References

Author Index

Subject Index


Books

Doornik, J.A. and Hendry, D.F. (2009). PcGive Volume IV: Interactive Monte Carlo Experimentation in Econometrics Using PcNaive 5, London: Timberlake Consultants Press. (ISBN 978-09552127-6-5)

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 Experimental Design .
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

SsfPack 3.0: Siem Jan Koopman, Neil Shephard, and Jurgen A. Doornik (2008) .  Statistical Algorithms for Models in State Space Form: SsfPack 3.0, London: Timberlake Consultants Press.
 (ISBN: 978-0-9557076-3-6).

Table of Contents

I Prologue

1. Introduction

1.1 General information
1.2 Overview of the SsfPack book
1.3 New Features
1.4 Support Platforms
1.5 Citation
1.6 World Wide Web
1.7 Acknowledgments

2 The state space form in SsfPack 3

2.1 The state space representation in SsfPack
2.2 Initial conditions
2.3 Time-varying state space form
2.4 Formulating the state space
2.5 Missing values

3 Models in state space form 11

3.1 Autoregressive moving average models
3.2 Autoregressive integrated moving average models
3.3 Seasonal ARIMA models
3.4 Structural time series models
3.5 Regression models
3.6 Adding regression effects to time series models
3.7 Nonparametric cubic spline models

II SsfPack Basic documentation

4 Prediction, smoothing and simulation

4.1 Simulating data from state space models
4.2 The Kalman Filter
4.3 Moment smoothing
4.4 Simulation smoothing
4.5 The conditional density: its mean and simulation

5 Ready-to-use functions

5.1 Likelihood and score evaluation
5.2 Prediction and smoothing
5.3 Applications

6 Illustrations

6.1 Seasonal components
6.2 Combining models
6.3 Regression effects in time-invariant models
6.4 Bayesian parameter estimation

II SsfPack Extended documentation

7 State Space form in SsfPack Extended

7.1 Variance matrices and restrictions
7.2 Initial Condition
7.3 Supporting functions

8 Prediction, filtering, smoothing and simulation

8.1 Simulating date from state space models
8.2 The univariate algorithms
8.3 The multivariate algorithms
8.4 Simulating smoothing

9 More ready-to-use functions

9.1 Exact likelihood evaluation
9.2 Augmentation method for likelihood evaluation
9.3 Regression
9.4 Prediction, filtering and smoothing
9.5 Forecasting
9.6 Weight functions
9.7 Bootstrap for general state space models

More illustrations

10.1 Estimation in multivariate local level model
10.2 Approximations to nonlinear non-Gaussian models

A SARIMA models in state space

A.1 ARIMA model with d = 2
A.2 SARIMA model with d = 1 and D = 1
A.3 SARIMA model with d = 2 and D = 1

References

Author Index

Subject Index


Books

Koopman S.J., Harvey, A.C., Doornik, J.A. and Shephard, N. (2009).
STAMP 8.2: Structural Time Series Analyser, Modeller and Predictor, London: Timberlake Consultants Press. (ISBN: 978-0-9557076-2-9).

I Prologue

1. Introduction

1.1 Overview of the STAMP book
1.2 General information
1.3 Features indroduced in STAMP 7
1.4 New in STAMP 8
1.5 Developments in STAMP 8.20
1.6 The special features of STAMP
1.7 Basics of the program
1.8 Using STAMP documentation
1.9 Citation
1.10 World Wide Web
1.11 Tutorial data sets
1.12 Data sets used in exercises
1.12 STAMP and PcGive

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 Forecasting
5.4 Statistical features of the models
5.5 Exercises

6. Tutorials on Multivariate Models

6.1. SUTSE models
6.2 Cycles
6.3 Autoregression
6,4 Common factors and cointegration
6.5 Explanatory variables and interventions
6.6 Assesing the effect of the seat belt law using a control group
6.7 Exercises

7. Applications in Macroeconomics and Finance

7.1 Univariate Trend-cycle decompositions: GDP
7.2 Multivariate trends and cycles: GDP and Investment
7.3 Inflation
7.4 Stochastic volatility
7.5 Seasonal adjustment and detrending
7.6 Missing Values
7.7 Exercises

8. Tutorial on Model Building and Testing

8.1 Specification of univariate models
8.2 Estimate a model
8.3 Model evaluation and testing
8.4 Forecasting

III Statistical Treatment

9. Statistical Treatment of Model

9.1 Model definitions
9.2 State space form
9.3 Kalman filter

9.4 Disturbance smoother
9.5 Forecasting
9.6 Parameter estimation

10. Statistical Model Output

10.1 Output from STAMP
10.2 Parameters
10.3 Final state
10.4 Goodness of fit
10.5 Components
10.6 Residuals
10.7 Auxiliary residuals
10.8 Predictive testing
10.9 Forecast

A1 STAMP Batch Language

References
Author Index
Subject Index


Books

Laurent S. (2009). G@RCH 6: Estimating and Forecasting ARCH Models, London: Timberlake Consultants Press. (ISBN 978-0-9557076-0-5).

1 Introduction 1
   1.1 G@RCH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
   1.1.1   Definition . . . . . . . . . . . . . . . . . . . . . . . . . 2
   1.1.2   Program Versions . . . . . . . . . . . . . . . . . . . . . 3
   1.1.3   What's new in G@RCH 6.0 ? . . . . . . . . . . . . . . . 3
   1.1.4   What's new in G@RCH 5.1 ? . . . . . . . . . . . . . . . 4
   1.1.5   What's new in G@RCH 5.0 ? . . . . . . . . . . . . . . . 6
   1.2    General Information . . . . . . . . . . . . . . . . . . . . . . . . 7
   1.2.1   Queries about G@RCH . . . . . . . . . . . . . . . . . . 7
   1.2.2   Availability and Citation . . . . . . . . . . . . . . . . . 7
  1.2.3   World Wide Web . . . . . . . . . . . . . . . . . . . . . 8
  1.3   Installing and Running G@RCH 6.0 . . . . . . . . . . . . . . . 8
2 Getting Started 9
  2.1    Starting G@RCH . . . . . . . . . . . . . . . . . . . . . . . . . . 9
  2.2    Loading and Viewing the Tutorial Data Set . . . . . . . . . . . . 9
  2.3    OxMetrics Graphics . . . . . . . . . . . . . . . . . . . . . . . . 12
  2.3.1   A First Graph . . . . . . . . . . . . . . . . . . . . . . . 13
  2.3.2   Graph Saving and Printing . . . . . . . . . . . . . . . . 13
  2.3.3   Including Graphs in LATEX Documents . . . . . . . . . . 13
3    Introduction to the Univariate ARCH Model 16
  3.1    Visual Inspection . . . . . . . . . . . . . . . . . . . . . . . . . . 16
  3.2    Preliminary Graphics . . . . . . . . . . . . . . . . . . . . . . . . 17
  3.3    Preliminary Tests . . . . . . . . . . . . . . . . . . . . . . . . . . 20
  3.4    Conditional Mean Specification . . . . . . . . . . . . . . . . . . 26
  3.5    Conditional Variance Specification: the ARCH Model . . . . . . 28
  3.5.1   Explanatory Variables . . . . . . . . . . . . . . . . . . . 30
  3.5.2   Positivity Constraints . . . . . . . . . . . . . . . . . . . 30
  3.5.3   Variance Targeting . . . . . . . . . . . . . . . . . . . . 30
  3.6    Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
  3.6.1   G@RCH menus . . . . . . . . . . . . . . . . . . . . . . 31
  3.6.2   Distributions . . . . . . . . . . . . . . . . . . . . . . . 35
  3.7    Graphics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
  3.8    Misspecification Tests . . . . . . . . . . . . . . . . . . . . . . . 47
  3.9    Parameter Constraints . . . . . . . . . . . . . . . . . . . . . . . 55
  3.10    Forecasts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
  3.10.1   Forecasting the Conditional Mean . . . . . . . . . . . . 57
  3.10.2   Forecasting the Conditional Variance . . . . . . . . . . . 58
  3.11    Further Options . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
  3.11.1   Exclusion Restrictions Dialog Box . . . . . . . . . . . . 60
  3.11.2   Linear Restrictions Dialog Box . . . . . . . . . . . . . . 60
  3.11.3   Store in Database Dialog . . . . . . . . . . . . . . . . . 61
  3.12    The random walk hypothesis (RWH) . . . . . . . . . . . . . . . . 62
  3.12.1   The Variance-ratio test . . . . . . . . . . . . . . . . . . 63
  3.12.2   Runs test . . . . . . . . . . . . . . . . . . . . . . . . . . 69
  3.12.3   Rescaled Range Tests . . . . . . . . . . . . . . . . . . . 71
4 Further Univariate GARCH Models 75
  4.1   GARCH Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
  4.2   EGARCH Model . . . . . . . . . . . . . . . . . . . . . . . . . . 80
  4.3   GJR Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
  4.4   APARCH Model . . . . . . . . . . . . . . . . . . . . . . . . . . 83
  4.5   IGARCH Model . . . . . . . . . . . . . . . . . . . . . . . . . . 86
  4.6   RiskMetricsTM . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
  4.7   Fractionally Integrated Models . . . . . . . . . . . . . . . . . . . 88
  4.8   Forecasting the Conditional Variance of GARCH-type models . . 94
  4.9   Constrained Maximum Likelihood and Simulated Annealing . . . 96
  4.10   Accuracy of G@RCH . . . . . . . . . . . . . . . . . . . . . . . 99
  4.11   Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
5 Estimating Univariate Models using the Batch and Ox Versions 104
  5.1    Using the Batch Version . . . . . . . . . . . . . . . . . . . . . . 104
  5.2    Importing the Garch Class in Ox . . . . . . . . . . . . . . . . . . 108
  5.2.1    GarchEstim.ox example . . . . . . . . . . . . . . . . . 108
  5.2.2    Running an Ox Program . . . . . . . . . . . . . . . . . 111
  5.2.2.1   Command Prompt . . . . . . . . . . . . . . . 112
  5.2.2.2   OxEdit . . . . . . . . . . . . . . . . . . . . . 112
  5.2.2.3   OxMetrics . . . . . . . . . . . . . . . . . . . 113
  5.3   Advanced Ox Usage . . . . . . . . . . . . . . . . . . . . . . . . 118
  5.3.1   Forecast.ox example . . . . . . . . . . . . . . . . . . 118
  5.3.2   Imposing Nonlinear Constraints . . . . . . . . . . . . . 122
  5.4   G@RCH and OxGauss . . . . . . . . . . . . . . . . . . . . . . . 127
  5.4.1   Calling Gauss Programs from Ox . . . . . . . . . . . . . 127
  5.4.2   Understanding OxGauss . . . . . . . . . . . . . . . . . 130
  5.4.3   Graphics Support in OxGauss . . . . . . . . . . . . . . 131
6 Value-at-Risk (VaR) estimation using G@RCH 132
  6.1   VaR Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
  6.1.1   RiskMetricsTM . . . . . . . . . . . . . . . . . . . . . . 133
  6.1.2   Normal APARCH . . . . . . . . . . . . . . . . . . . . . 134
  6.1.3   Student APARCH . . . . . . . . . . . . . . . . . . . . . 134
  6.1.4   Skewed-Student APARCH . . . . . . . . . . . . . . . . 135
  6.2   Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
  6.2.1   Model for VaR assessment . . . . . . . . . . . . . . . . 136
  6.2.2   In-sample VaR . . . . . . . . . . . . . . . . . . . . . . 140
  6.2.3   Out-of-sample VaR . . . . . . . . . . . . . . . . . . . . 146
7 Realized Volatility and Intraday Periodicity 149
  7.1 Introduction to diffusion models . . . . . . . . . . . . . . . . . . 150
  7.1.1   Standard Brownian motion / Wiener process . . . . . . . 150
  7.1.2   Generalized Wiener Process . . . . . . . . . . . . . . . 152
  7.2   Integrated Volatility . . . . . . . . . . . . . . . . . . . . . . . . . 153
  7.2.1   Theoretical background . . . . . . . . . . . . . . . . . . 153
  7.2.2    Illustration of the concept of integrated volatility . . . . 153
  7.3    Realized Volatility . . . . . . . . . . . . . . . . . . . . . . . . . 157
  7.4   Jumps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
  7.5   Intraday Periodicity . . . . . . . . . . . . . . . . . . . . . . . . . 162
  7.5.1   Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
  7.5.2   Evidence of intraday periodicity . . . . . . . . . . . . . 162
  7.5.3   Classical and robust estimation of intraday periodicity . . 165
  7.5.3.1   Non parametric estimation of periodicity . . . 166
  7.5.3.2   Parametric estimation of periodicity . . . . . . 168
  7.5.4   First illustration on simulated data . . . . . . . . . . . . 171
  7.5.5    Second illustration on EUR/USD data . . . . . . . . . . 176
  7.6   Robust to jumps volatility measures . . . . . . . . . . . . . . . . 178
  7.6.1   Bi-Power Variation . . . . . . . . . . . . . . . . . . . . 179
  7.6.2   Realized Outlyingness Weighted Variance . . . . . . . . 181
  7.7   Daily jump tests . . . . . . . . . . . . . . . . . . . . . . . . . . 184
  7.8   Intraday jump tests . . . . . . . . . . . . . . . . . . . . . . . . . 187
  7.9   Multivariate case . . . . . . . . . . . . . . . . . . . . . . . . . . 189
  7.9.1   Realized Quadratic Covariation . . . . . . . . . . . . . . 190
  7.9.2   Realized BiPower Covariation . . . . . . . . . . . . . . 190
  7.9.3   ROWQCov . . . . . . . . . . . . . . . . . . . . . . . . 191
  7.9.4   Correction factor for ROWVar and ROWQCov . . . . . 193
8 Getting started with RE@LIZED 194
  8.1   Univariate non parametric volatility . . . . . . . . . . . . . . . . 194
  8.2   Intraday tests for jumps . . . . . . . . . . . . . . . . . . . . . . 206
  8.3   Multivariate non parametric volatility . . . . . . . . . . . . . . . 211
  8.4   The Realized class . . . . . . . . . . . . . . . . . . . . . . . . . 214
9 Multivariate GARCH Models 219
  9.1   Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
  9.2   Estimating MGARCH Models with G@RCH . . . . . . . . . . . 221
  9.2.1   Misspecification Tests . . . . . . . . . . . . . . . . . . . 227
  9.3   Graphics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228
  9.4   Forecasts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231
  9.4.1   Exclusion Restrictions Dialog Box . . . . . . . . . . . . 234
  9.4.2   Linear Restrictions Dialog Box . . . . . . . . . . . . . . 234
  9.4.3   Store in Database Dialog . . . . . . . . . . . . . . . . . 235
  9.5   Overview of models . . . . . . . . . . . . . . . . . . . . . . . . 236
  9.5.1   Conditional mean specification . . . . . . . . . . . . . . 237
  9.5.2   Generalizations of the univariate standard GARCH model 238
  9.5.2.1   RiskMetrics and BEKK models . . . . . . . . 238
  9.5.3   Linear combinations of univariate GARCH models . . . 243
  9.5.4   Conditional correlation models . . . . . . . . . . . . . . 257
  9.6    Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266
  9.6.1    Maximum Likelihood . . . . . . . . . . . . . . . . . . 266
  9.6.2    Two-step estimation . . . . . . . . . . . . . . . . . . . 269
  9.6.3    Variance Targeting . . . . . . . . . . . . . . . . . . . . 271
  9.7    Diagnostic Checking . . . . . . . . . . . . . . . . . . . . . . . . 271
  9.7.1   Portmanteau Statistics . . . . . . . . . . . . . . . . . . . 272
  9.7.2   CCC Tests . . . . . . . . . . . . . . . . . . . . . . . . . 272
  9.8   Batch code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273
  9.9   Importing the MGarch Class in Ox . . . . . . . . . . . . . . . . . 278
  9.9.1   MGarchEstim.ox example . . . . . . . . . . . . . . . . 278
  9.10 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284
10 Structure of the Program 286
  10.1   Classes and Functions . . . . . . . . . . . . . . . . . . . . . . . 286
  10.2   Garch Member Functions List . . . . . . . . . . . . . . . . . . . 286
  10.3   Garch Members Functions . . . . . . . . . . . . . . . . . . . . . 293
  10.4   MGarch Member Functions List . . . . . . . . . . . . . . . . . . 340
  10.5   MGarch Members Functions . . . . . . . . . . . . . . . . . . . . 344
  10.6   RealizedMember Functions List . . . . . . . . . . . . . . . . . 369


Bibliography
Subject Index


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


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