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The following books describe the various components in the current OxMetrics
system:

書籍紹介

革新的なソフトウェア「PcGive」を用い、時系列分析を初歩からステップ・バイ・ステップで学ぶことができる画期的なテキスト。

- An Introduction to
**OxMetrics 6****(NEW)**

by Doornik, J.A. (2009) - An Object-Oriented Matrix Language:
**Ox 6****(NEW)**

by Doornik, J.A. (2009) - Introduction to
**Ox**: An Object-Oriented Matrix Language

by Doornik, J.A. and Ooms, M. (2007) **PcGive**(in**Japanese**)

by David F. Hendry, Jurgen A. Doornik and Ichikawa Hiroya (2006)- Empirical Econometric Modelling Using
**PcGive 13 Volume I****(NEW)**

by Doornik, Jurgen A. and Hendry, David F. (2009) - Modelling Dynamic Systems Using
**PcGive 13 Volume II****(NEW)**

by Hendry, D.F., and Doornik, J.A. (2009) - Econometric Modelling Using
**PcGive 13 Volume III****(NEW)**

by Doornik, J.A. and Hendry, D.F. (2009) **PcGive 13 Volume IV**: Interactive Monte Carlo Experimentation in Econometrics using**PcNaive 5****(NEW)**

by Doornik, J.A. and Hendry, D.F. (2009)- Statistical Algorithms for Models in State Space Form
**SsfPack 3.0**

by Koopman S.J., Shephard, N. and Doornik, J.A. (2008) **STAMP 8.2**: Structural Time Series Analyser, Modeller and Predictor**(NEW)**

by Koopman S.J., Harvey, A.C., Doornik, J.A. and Shephard, N. (2009)**G@RCH 6**, Estimating and Forecasting ARCH Models Using G@RCH 6**(NEW)**

by Laurent S., (2009)**GiveWin**: An Interface to Empirical Modelling

by Doornik, J.A. and Hendry, D.F. (2001)

See the OxMetrics 2007 bookshop for a description of the OxMetrics 5 books (2007).

See the OxMetrics 2006 bookshop for a description of the OxMetrics 4 books (2006).

See the OxMetrics antique bookshop for a description of the previous OxMetrics 3 books (2000-2005).

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

Table of contents | Order |

**OxMetrics™**
is an interactive graphics-oriented program, which acts as
“front-end” to a series of integrated software modules:
PcGive, Ox, STAMP, G@RCH and X12Arima. OxMetrics provides a complete
separation of the front-end (for data manipulation and visualisation)
and the econometric and statistical modules, while maintaining a
reliable communication channel, and giving a closely integrated
appearance from the user perspective.

OxMetrics displays reports and graphics, which can be manipulated on screen, offers a calculator and algebraic language for transforming data, and enables the user to open multiple databases. A batch language allows for automation of many of these tasks. OxMetrics reduces the learning curve for econometric and statistical packages by providing a common front-end which is easy to use. Users with the necessary programming skills can write programs in suitable languages (including Ox) which can communicate with OxMetrics.

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

Table of contents | Order |

**Ox™** version 6
is an object-oriented matrix programming language. It is an important
tool for statistical and econometric programming with a syntax similar to
C++ and a comprehensive range of commands for matrix and statistical
operations. Ox runs faster than other similar programs and can read and
write spreadsheet and OxMetrics(PcGive) file formats. Ox uses the OxMetrics interface
to produce publication quality graphics and reports.

New estimation techniques and Monte Carlo experiments can be implemented easily and efficiently in Ox. Application procedures for dynamic econometric modelling (such as vector autoregressions and cointegration analysis) are included. Ox Packages for fractionally integrated, state space, dynamic panel data and stochastic volatility models are available for use with Ox.

**Doornik, J.A. and Ooms, M. (2007)**.
*Introduction to Ox: An Object-Oriented Matrix Language*,
London: Timberlake Consultants Press. (ISBN: 978-0-9552127-4-1).

Table of contents | Order |

**Introduction to Ox™** first introduces the Ox environment.
Syntax, operators, program flow and program design are discussed and illustrated using
econometric examples. Next we describe how to input and output data, how to generate graphics
and how to present results using string operators and print formats. Finally we discuss the use and
generation of Ox classes for object oriented programming, including a database class for time series
data and a simulation class for statistical models.

By working through the hands-on examples, readers will acquire an adequate level of Ox knowledge within a couple of days.

“*This takes the reader chapter by chapter through the intricacies of the Ox Language
(including object-oriented programming features), so that the users can appreciate the basics
of using Ox and its features efficiently*”

** D.F.ヘンドリー/著 J.A.ドーニック/著 市川博也/訳・解説**
Hendry, D.F., Doornik, J.A., and Ichikawa Hiroya (2006)

.

Buy at Yahoo Japan or | Amazon Japan |

革新的なソフトウェア「PcGive」を用い、時系列分析を初歩からステップ・バイ・ステップで学ぶことができる画期的なテキスト。

目次

序説 時系列分析入門(マクロ経済変数と新しい時系列の経済分析入門
「過去の人生」が「現在の自分」を説明する―自己回帰モデルと単位根の役割 ほか)
第1部 PcGiveへのプロローグ(PcGiveを始める
PcGiveを上手に使いこなすための基本的な操作)
第2部 PcGiveを用いた演習(クロスセクション回帰方程式の演習
記述統計量と単位根の演習 ほか)
第3部 PcGiveの計量経済学(概説
PcGiveを用いた初級計量経済学の学習)

** Doornik, J.A. and Hendry, D.F. (2009)**.
*Empirical
Econometric Modelling Using PcGive 13: Volume I*,
London:

Timberlake Consultants Press. (ISBN 978-0-9552127-8-9)

Table of contents | Order |

** PcGive™** version 13
is an essential tool for modern econometric modelling. It provides
the latest econometric techniques, from single equation methods, to
cointegration analysis and simultaneous equations modelling. PcGive is
easy to use and flexible, making it suitable for both teaching and research.
PcGive provides the estimation and testing procedures which have made it
indispensable in applied econometric analysis, and interacts with OxMetrics
to give excellent graphics and data facilities.

The PcGive books introduce the econometric methods of the program, emphasising empirical issues. Each volume contains an extensive econometrics section, starting at an elementary level, yet progressing to advanced econometrics. There are also tutorial chapters explaining the detailed use of PcGive, as well as precise descriptions of the statistics which it computes. Each volume can be used as a stand-alone text book as much as a reference for PcGive.

**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)

Table of contents | Order |

**PcGive™** version 13
is an essential tool for modern econometric modelling. It provides
the latest econometric techniques, from single equation methods to
cointegration analysis and simultaneous equations modelling.
Volume II covers advanced econometric techniques, including cointegration
analysis, simultaneous equations modelling and dynamic forecasting.
PcGive is easy to use and flexible, making it suitable both for teaching
and research. PcGive interacts with OxMetrics, and provides excellent
graphics, together with the estimation and testing procedures which have
made PcGive indispensable in applied econometric analysis.

The PcGive books introduce the econometric methods of the program, emphasising empirical issues. Each volume contains an extensive econometrics section, starting at a very elementary level, moving on to more advanced econometrics. In addition, there are many tutorial chapters, as well as a detailed descriptions of the statistics which are computed. Each volume can be used as a reference for PcGive, just as much as a stand-alone text book.

**Doornik, J.A. and Hendry, D.F. (2009)**
*Econometric Modelling Using PcGive 13: Volume III*
London: Timberlake Consultants Press. (ISBN 978-0-9552127-7-2)

Table of contents | Order |

**PcGive™** version 13
provides modern econometric techniques within an environment
which makes them easy to use, thus allowing the user to focus on the
modelling process. These features make PcGive an essential tool for
modern econometric modelling.

Volume I and II range from single equation dynamic econometric modelling to cointegration analysis and simultaneous equations methods.

Volume III , written with the cooperation of Manuel Arellano, Stephen Bond, H. Peter Boswijk and Marius Ooms, covers all remaining topics, including:

- Volatility models (GARCH, EGARCH, etc.),

with H. Peter Boswijk and Marius Ooms - Fractionally integrated models (ARFIMA),

with Marius Ooms - Static and dynamic panel data models (DPD),

with Manuel Arellano and Stephen Bond. - Regime Switching Models (RS)
**(NEW, 2009)** - X12Arima for OxMetrics (seasonal adjustment and ARIMA modelling)
- Limited dependent variable models (logitJD)

** Doornik, J.A. and Hendry, D.F. (2009)**.
*PcGive 13, Volume IV: Interactive Monte Carlo Experimentation in Econometrics Using PcNaive 5*,
London: Timberlake Consultants Press. (ISBN 978-09552127-6-5)

Table of contents | Order |

** PcNaive™**(Version 4), which
is part of PcGive 13,
is a program for designing Monte Carlo experiments of static and
dynamic econometric models. There is a set of simple interactive dialogs
to design experiments for a static regression with up to two regressors
or a simple autoregressive process. These are particularly useful to new
users and for teaching introductory Econometrics. More advanced dialogs
give access to the full range of features of PcNaive, up to simultaneous
equations models and cointegration tests.

PcNaive is an Ox package which generates Ox programs from the settings in the dialogs; Monte Carlo output appears in the OxMetrics desktop, and includes:

- theoretical analysis of the DGP,
- live graphical output as the experiment progresses,
- numerical output of final results.

PcNaive 5 is part of PcGive 13.

The PcNaive book contains three major components. The first is a set of extensive tutorials introducing Monte Carlo analysis, and showing how the program can be used. This is followed by a separate part that discusses how PcNaive can be used in teaching Econometrics (from an elementary level, through intermediate to advanced). Finally, there is a comprehensive introduction to the theory of Monte Carlo analysis, including asymptotic analysis and response surfaces. Many econometric examples are used throughout, and the book covers important material which is often missing from standard text books.

**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 | Order |

** SsfPack™**
(Extended) **version 3.0** is a suite of C routines for carrying out computations involving the
statistical analysis of univariate and multivariate models in state space form with easy-to-use functions for Ox.

SsfPack requires Ox 4 or above to run.

SsfPack allows for a full range of different
**state space forms**: from a simple time-invariant model to a complicated multivariate
time-varying model. Functions are provided to put standard models such as **SARIMA**,
**unobserved components**, **time-varying regressions** and **cubic spline models** into **state space form**.
Basic functions are available for filtering, moment smoothing and simulation
smoothing. Ready-to-use functions are provided for standard tasks such as
likelihood evaluation, forecasting and signal extraction.

SsfPack can be
easily used for implementing, fitting and analysing Gaussian models relevant to
many areas of econometrics and statistics. Furthermore it provides all relevant
**tools for the treatment of non-Gaussian and nonlinear state space models**.
In particular, tools are available to implement **simulation based estimation
methods** such as importance sampling and Markov chain Monte Carlo (MCMC) methods.

**Koopman S.J., Harvey, A.C., Doornik, J.A. and Shephard, N. (2009)**.

STAMP: Structural Time Series Analyser, Modeller and Predictor

(ISBN: 978-0-9557076-2-9).

Table of contents | Order |

** STAMP™**
stands for Structural Time series Analyser,

Modeller and Predictor.
It is a menu-driven system designed

to model, describe and predict time
series. It is based on

structural time series models. These models are
set up in

terms of components such as trends, seasonals and

cycles, which
have a direct interpretation. Estimation is

carried out
using state space
methods and Kalman filtering.

However, STAMP is set up in an easy-to-use
form which enables the user to concentrate on model selection and
interpretation.

**STAMP 8.2** for OxMetrics 6 handles time series with **missing values**. Explanatory
variables with **time varying coefficients** and **interventions** can be included.
Version 8 includes **extensions** and **improvements** for **Multivariate Models**: select **components by equation**, select regressors and interventions by equation,
separate dependence structures for each component, wide choice of variance matrices,
**higher order multivariate components**, missing observations allowed, forecasting,
exact likelihood computation, **automatic outlier and break detection**, fixing parameters is made easy.
Among the special features of STAMP are interactive model selection,
a wide range of diagnostics, easy creation of model based forecasts, spectral filters, observation weight functions,
and batch facilities.

The STAMP book introduces structural time series models and the way in which they can be used to model a wide range of series. Tutorials provide a step-by-step guide to learning the program and the structural time series methodology.

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

Table of contents | Order |

** G@RCH ™**
is a user-friendly system to estimate a wide range of GARCH-type models.

The G@RCH book provides extensive tutorials illustrating the use of the program.
It also reviews some of the most recent contributions
in this field.

- NEW in 6: RE@LIZED non-parametric estimators of quadratic variation, integrated volatility and jumps using intraday data.
- Models:
- Conditional Mean: ARMA, ARFIMA, ARCH-in-Mean, Explanatory Variables;
- Conditional Variance: GARCH, EGARCH, GJR, APARCH, IGARCH, RiskMetrics, FIGARCH, FIEGARCH, FIAPARCH, HYGARCH; Explanatory Variables;
- Multivariate Conditional Variance: MG@RCH: BEKK, DCC, CCC, OGARCH, GO-GARCH, Principal Components, RiskMetrics, Variance Targeting
- Conditional distributions: Normal, Student, GED or skewed-Student;

- Estimators:
- (Quasi-)Maximum Likelihood, Constrained Maximum Likelihood, Simulated Annealing;

- Tests:
- (Mis)Specifications Tests: Information Criteria, Jarque-Bera, Box-Pierce statistics, LM ARCH test, Sign Bias Test, Pearson goodness-of-fit, The Nyblom stability test, Residual-Based Diagnostic for for Conditional Heteroscedasticity, etc;

- Financial Analysis:
- Value-at-Risk, Expected shortfall, Backtesting (Kupiec LRT, Dynamic Quantile test);
- Forecasting, Realized volatility.

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

Table of contents |

**GiveWin™**
is an interactive graphics-oriented program, which acts as
“front-end” to a series of software modules:
TSP, Stamp and PcGets. It provides a complete
separation of the front-end (for data manipulation and visualisation)
and the econometric and statistical modules, while maintaining a
reliable communication channel, and giving a closely integrated
appearance from the user perspective.

The program displays reports and graphics, which can be manipulated on screen, offers a calculator and algebraic language for transforming data, and enables the user to open multiple databases. A batch language allows for automation of many of these tasks.

Since the release of TSP/OxMetrics 5.1 in 2009 GiveWin can no longer be purchased.