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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 2. Installation 3. Getting Started: Windows 3.1 Starting OxMetrics 4. Getting Started: OS X 4.1 Starting OxMetrics 5. Getting Started: Linux 5.1 Starting OxMetrics 6. OxMetrics Modules 6.1 OxMetrics Modules Part II: OxMetrics Tutorials 7. Tutorial on Graphics 7.1 Descriptive graphics 8. Tutorial on Graph Editining 8.1 Multiple graphs 9. Tutorial on Data Input and Output 9.1 Open Data File and files types 10. Tutorial on Data Transformaion 10.1 Calculator Part II: OxMetrics Reference 11. OxMetrics Statistics 11.1 Actual series and scatter plots 12 OxMetrics file formats 12.1 OxMetrics data files (.in7/ .bn7) 13. Algebra Language 13.1 Introduction 14. Batch Languages 14.1 Introduction 15. OxMetrics graphics 15.1 Graphic paper 16. OxMetrics data management 16.1 Creating data References |
Doornik, J.A. (2009). An Object-Oriented Matrix Language Ox™ 6, London: Timberlake Consultants Press. (978-0-9552127-5-8).
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
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
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
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
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
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
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
Laurent S. (2009).
| 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
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Doornik, J.A. and Hendry, D.F. (2001). GiveWin™: An Interface to Empirical Modelling, London: Timberlake Consultants Press. (ISBN 0-9533394-3-2)