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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
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
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
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
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
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
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
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
Laurent S. and Peters, J.-P. (2006).
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
Doornik, J.A. and Hendry, D.F. (2001). GiveWin™: An Interface to Empirical Modelling, London: Timberlake Consultants Press. (ISBN 0-9533394-3-2)
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 )
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;