DOCSLIB.ORG

Announcing the Release of Oxmetricstm5

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Announcing the Release of Oxmetricstm5 OxMetrics www.oxmetrics.net www.timberlake.co.uk www.timberlake-consultancy.com OxMetrics news AUGUST 2007 ISSUE 7 NEW MODULES NEW RELEASES FAQS USERS VIEWS COURSES AND SEMINARS Announcing the release of OxMetricsTM5 OxMetrics™ is a modular software system state space forms: from a simple time-invariant In OxMetrics™ 5, all the modules of OxMetrics™ providing an integrated solution for the model to a complicated multivariate time- Enterprise Edition have been upgraded. econometric analysis of time series, forecasting, varying model. Functions are provided to put The details of new features are described below: financial econometric modelling, or statistical standard models such as ARIMA, Unobserved analysis of cross-section and panel data. The components, regressions and cubic spline New feature: Autometrics™ OxMetrics modules are: models into state space form. Basic functions by Jurgen. A. Doornik • Ox™ Professional is a powerful matrix are available for filtering, moment smoothing language, which is complemented by a and simulation smoothing. Ready-to-use 1 Autometrics comprehensive statistical library. Among the functions are provided for standard tasks such special features of Ox are its speed, well- as likelihood evaluation, forecasting and signal 1.1 Introduction designed syntax and editor, and graphical extraction. SsfPack can be easily used for With their publication in 1999, 1 Hoover and Perez facilities. Ox can read and write many data implementing, fitting and analysing Gaussian revisited the earlier experiments of Lovell, and formats, including spreadsheets; Ox can run models relevant to many areas of showed that an automated general-to-specific most econometric GAUSS programs. Ox™ econometrics and statistics. Furthermore it model selection (Gets) algorithm can work Packages extend the functionality of Ox in provides all relevant tools for the treatment of successfully. This contrasts with the results of various ways. Once installed, they become an non-Gaussian and nonlinear state space Lovell, who only considered simplistic methods integrated part of Ox. Some packages just add models. In particular, tools are available to such as forward selection, showing that these a few useful functions, whereas others offer an implement simulation based estimation can fail badly. extensive econometric or statistical technique methods such as importance sampling and which can also be used interactively using Ox Markov chain Monte Carlo (MCMC) methods The success of automated Gets created Professional. Most of these packages are FREE (not yet released). renewed interest in computer-based algorithms. of charge (see www.doornik.com for full list of • TSP/GiveWin™ by TSP International (founded In particular, Hendry and Krolzig extended available packages) in 1982 by Bronwyn H. Hall) is an econometric the algorithm of Hoover–Perez and developed • PcGive™ aims to give an operational and software package, with convenient input of PcGets, a user-friendly computer program structured approach to econometric modelling commands and data, all the standard aimed at the empirical modeller. PcGets was using the most sophisticated yet user-friendly estimation methods (including non-linear), part of GiveWin 2. software. The accompanying books transcend forecasting, and a flexible language for Autometrics can be seen as the third generation, the old ideas of `textbooks' and `computer programming your own estimators. The taking many features of the earlier manuals' by linking the learning of econometric philosophy behind TSP is that of a command- implementations, but also differing in some methods and concepts to the outcomes driven language tailored to econometric aspects. Starting with OxMetrics 5, Autometrics achieved when they are applied. The problems, whatever the platform used. For is part of PcGive - now automatic model econometric techniques of PcGive include: those working in a Windows environment, TSP selection can be activated with as little as a VAR, cointegration, simultaneous equations can be installed as a module of OxMetrics - single click. models, ARFIMA, logit, probit, GARCH TSP/GiveWin. This eases the use of the modelling, static and dynamic panel data command-line environment by providing 1.2 An example models, X12ARIMA, and more. context sensitive help, syntax highlighting, and To illustrate the use of Autometrics, we use the • STAMP™ is designed to model and forecast a dialog-driven command builder. tutorial data set of PcGive, taking 12 lags of all time series, based on structural time series variables. models. These models use advanced Four of these modules have been grouped in a The initial model is formulated in PcGive as any techniques, such as Kalman filtering, but are single product OxMetrics™ Enterprise Edition. other dynamic model: set up so as to be easy to use -- at the most OxMetrics™ Enterprise Edition is a single basic level all that is required is some product that includes and integrates all the appreciation of the concepts of trend, seasonal important components for theoretical and and irregular. The hard work is done by the empirical research in econometrics, time series program, leaving the user free to concentrate analysis and forecasting, applied economics and on formulating models, then using them to financial time series: Ox Professional, PcGive, make forecasts G@RCH, and STAMP. • G@RCH ™ is dedicated to the estimation and OxMetrics™ is distributed and published by forecasting of ARCH and GARCH-type models. Timberlake Consultants Ltd G@RCH provides a user-friendly interface (with (www.timberlake.co.uk). Timberlake Consultants rolling menus) as well as some graphical Limited, with head office in the U.K., is features. celebrating its 25th anniversary in 2007. The • SsfPack™ provides Ox with additional facilities Company has a growing number of offices and when carrying out computations involving the agents worldwide, including Brazil, Japan, statistical analysis of univariate and Poland, Portugal, Spain and the USA. We also multivariate models in state space form. have partners in other countries to help with the SsfPack allows for a full range of different distribution of the OxMetrics software. This general unrestricted model, or GUM, is the regression can sometimes work (experiment 7 3 Comparison with PcGets starting point for the automatic model selection has high t-values), but also fail badly compared to procedure. The model settings dialog now has an Autometrics. Autometrics and PcGets have much in common: Autometrics section: both are algorithms for automatic model 1 Hoover, K.D. & Perez, S.J. (1999), Econometrics Journal, 2, selection within the general-to-specific 167–191. Lovell, M. C. (1983), Review of Economics and Statistics, 65, 1–12. framework. However, there are some important Hendry, D. F. & Krolzig, H.-M. (1999) Econometrics Journal, 2, differences too: 202–219. • Strategy PcGets has two strategies: Liberal and 2 More variables than observations Conservative. These correspond to 5% and 1% with carefully calibrated presearches. One exciting development is that Autometrics can Expert mode allows for different settings, but handle more variables than observations. looses the calibration between the various The main choice is the significance level of the Traditionally, this is considered infeasible — search components. reduction, kept at 5%. indeed, up to now PcGive would refuse to In Autometrics, the nominal size of the Autometrics finds four terminal models, and estimate such an equation. However, recently reduction is specified directly, with the chooses: David Hendry, Søren Johansen and Carlos Santos remainder adjusted accordingly. considered adding a dummy variable for each Coefficient Std.Error t-value observation. They study the theoretical • Presearch CONS_1 0.821298 0.03277 25.1 properties, and give a feasible algorithm (which Autometrics relies much less on presearch. CONS_4 0.145121 0.05118 2.84 differs from the one Autometrics uses). CONS_5 -0.119526 0.04076 -2.93 Simulation experiments show almost the same INC 0.519890 0.02719 19.1 Now, when Autometrics is switched on, PcGive operating characteristics with and without INC_1 -0.293790 0.03266 -9.00 will switch to blocking mode when there are presearch. INFLAT -1.00600 0.09926 -10.1 insufficient observations (usually k > 0.8T ). This • Path search INFLAT_8 0.390943 0.1237 3.16 happens automatically, whether such a model OUTPUT_12 -0.0557579 0.01388 -4.02 Autometrics does not implement the multiple- was formulated on purpose or by accident! path search. This was introduced by Hoover– The entries with lag 2 or more are not in the DGP. Some caution though: the blocking algorithm Perez to mimic the human modeller. However, However, the DGP also has a break, which is not does not fit the Gets framework anymore, and the objective of using the computer is to allowed for in the model. complex interactions may be missed. improve on that where possible. With presearch lag reduction switched off, To illustrate the procedure, we added 20 white Autometrics uses a tree search, systematically PcGive finds six terminal models. But the one noise variables to the dataset, labelled z. traversing the whole model space. with the lowest Schwarz criterion is the same as The GUM is: Several strategies are used to speed this up, before. One of the objectives of Autometrics was resulting in more frequent use of F-tests rather to reduce the dependence on presearch. than t-tests, as well as cutting off irrelevant paths. Never is the same model
Load more

Recommended publications
  • Introduction, Structure, and Advanced Programming Techniques
    APQE12-all Advanced Programming in Quantitative Economics Introduction, structure, and advanced programming techniques Charles S. Bos VU University Amsterdam [email protected] 20 { 24 August 2012, Aarhus, Denmark 1/260 APQE12-all Outline OutlineI Introduction Concepts: Data, variables, functions, actions Elements Install Example: Gauss elimination Getting started Why programming? Programming in theory Questions Blocks & names Input/output Intermezzo: Stack-loss Elements Droste 2/260 APQE12-all Outline OutlineII KISS Steps Flow Recap of main concepts Floating point numbers and rounding errors Efficiency System Algorithm Operators Loops Loops and conditionals Conditionals Memory Optimization 3/260 APQE12-all Outline Outline III Optimization pitfalls Maximize Standard deviations Standard deviations Restrictions MaxSQP Transforming parameters Fixing parameters Include packages Magic numbers Declaration files Alternative: Command line arguments OxDraw Speed 4/260 APQE12-all Outline OutlineIV Include packages SsfPack Input and output High frequency data Data selection OxDraw Speed C-Code Fortran Code 5/260 APQE12-all Outline Day 1 - Morning 9.30 Introduction I Target of course I Science, data, hypothesis, model, estimation I Bit of background I Concepts of I Data, Variables, Functions, Addresses I Programming by example I Gauss elimination I (Installation/getting started) 11.00 Tutorial: Do it yourself 12.30 Lunch 6/260 APQE12-all Introduction Target of course I Learn I structured I programming I and organisation I (in Ox or other language) Not: Just
    [Show full text]
  • Zanetti Chini E. “Forecaster's Utility and Forecasts Coherence”
    ISSN: 2281-1346 Department of Economics and Management DEM Working Paper Series Forecasters’ utility and forecast coherence Emilio Zanetti Chini (Università di Pavia) # 145 (01-18) Via San Felice, 5 I-27100 Pavia economiaweb.unipv.it Revised in: August 2018 Forecasters’ utility and forecast coherence Emilio Zanetti Chini∗ University of Pavia Department of Economics and Management Via San Felice 5 - 27100, Pavia (ITALY) e-mail: [email protected] FIRST VERSION: December, 2017 THIS VERSION: August, 2018 Abstract We introduce a new definition of probabilistic forecasts’ coherence based on the divergence between forecasters’ expected utility and their own models’ likelihood function. When the divergence is zero, this utility is said to be local. A new micro-founded forecasting environment, the “Scoring Structure”, where the forecast users interact with forecasters, allows econometricians to build a formal test for the null hypothesis of locality. The test behaves consistently with the requirements of the theoretical literature. The locality is fundamental to set dating algorithms for the assessment of the probability of recession in U.S. business cycle and central banks’ “fan” charts Keywords: Business Cycle, Fan Charts, Locality Testing, Smooth Transition Auto-Regressions, Predictive Density, Scoring Rules and Structures. JEL: C12, C22, C44, C53. ∗This paper was initiated when the author was visiting Ph.D. student at CREATES, the Center for Research in Econometric Analysis of Time Series (DNRF78), which is funded by the Danish National Research Foundation. The hospitality and the stimulating research environment provided by Niels Haldrup are gratefully acknowledged. The author is particularly grateful to Tommaso Proietti and Timo Teräsvirta for their supervision.
    [Show full text]
  • Econometrics Oxford University, 2017 1 / 34 Introduction
    Do attractive people get paid more? Felix Pretis (Oxford) Econometrics Oxford University, 2017 1 / 34 Introduction Econometrics: Computer Modelling Felix Pretis Programme for Economic Modelling Oxford Martin School, University of Oxford Lecture 1: Introduction to Econometric Software & Cross-Section Analysis Felix Pretis (Oxford) Econometrics Oxford University, 2017 2 / 34 Aim of this Course Aim: Introduce econometric modelling in practice Introduce OxMetrics/PcGive Software By the end of the course: Able to build econometric models Evaluate output and test theories Use OxMetrics/PcGive to load, graph, model, data Felix Pretis (Oxford) Econometrics Oxford University, 2017 3 / 34 Administration Textbooks: no single text book. Useful: Doornik, J.A. and Hendry, D.F. (2013). Empirical Econometric Modelling Using PcGive 14: Volume I, London: Timberlake Consultants Press. Included in OxMetrics installation – “Help” Hendry, D. F. (2015) Introductory Macro-econometrics: A New Approach. Freely available online: http: //www.timberlake.co.uk/macroeconometrics.html Lecture Notes & Lab Material online: http://www.felixpretis.org Problem Set: to be covered in tutorial Exam: Questions possible (Q4 and Q8 from past papers 2016 and 2017) Felix Pretis (Oxford) Econometrics Oxford University, 2017 4 / 34 Structure 1: Intro to Econometric Software & Cross-Section Regression 2: Micro-Econometrics: Limited Indep. Variable 3: Macro-Econometrics: Time Series Felix Pretis (Oxford) Econometrics Oxford University, 2017 5 / 34 Motivation Economies high dimensional, interdependent, heterogeneous, and evolving: comprehensive specification of all events is impossible. Economic Theory likely wrong and incomplete meaningless without empirical support Econometrics to discover new relationships from data Econometrics can provide empirical support. or refutation. Require econometric software unless you really like doing matrix manipulation by hand.
    [Show full text]
  • Econometric Theory
    Econometric Theory John Stachurski January 10, 2014 Contents Preface v I Background Material1 1 Probability2 1.1 Probability Models.............................2 1.2 Distributions................................. 16 1.3 Dependence................................. 25 1.4 Asymptotics................................. 30 1.5 Exercises................................... 39 2 Linear Algebra 49 2.1 Vectors and Matrices............................ 49 2.2 Span, Dimension and Independence................... 59 2.3 Matrices and Equations........................... 66 2.4 Random Vectors and Matrices....................... 71 2.5 Convergence of Random Matrices.................... 74 2.6 Exercises................................... 79 i CONTENTS ii 3 Projections 84 3.1 Orthogonality and Projection....................... 84 3.2 Overdetermined Systems of Equations.................. 90 3.3 Conditioning................................. 93 3.4 Exercises................................... 103 II Foundations of Statistics 107 4 Statistical Learning 108 4.1 Inductive Learning............................. 108 4.2 Statistics................................... 112 4.3 Maximum Likelihood............................ 120 4.4 Parametric vs Nonparametric Estimation................ 125 4.5 Empirical Distributions........................... 134 4.6 Empirical Risk Minimization....................... 137 4.7 Exercises................................... 149 5 Methods of Inference 153 5.1 Making Inference about Theory...................... 153 5.2 Confidence Sets..............................
    [Show full text]
  • International Journal of Forecasting Guidelines for IJF Software Reviewers
    International Journal of Forecasting Guidelines for IJF Software Reviewers It is desirable that there be some small degree of uniformity amongst the software reviews in this journal, so that regular readers of the journal can have some idea of what to expect when they read a software review. In particular, I wish to standardize the second section (after the introduction) of the review, and the penultimate section (before the conclusions). As stand-alone sections, they will not materially affect the reviewers abillity to craft the review as he/she sees fit, while still providing consistency between reviews. This applies mostly to single-product reviews, but some of the ideas presented herein can be successfully adapted to a multi-product review. The second section, Overview, is an overview of the package, and should include several things. · Contact information for the developer, including website address. · Platforms on which the package runs, and corresponding prices, if available. · Ancillary programs included with the package, if any. · The final part of this section should address Berk's (1987) list of criteria for evaluating statistical software. Relevant items from this list should be mentioned, as in my review of RATS (McCullough, 1997, pp.182- 183). · My use of Berk was extremely terse, and should be considered a lower bound. Feel free to amplify considerably, if the review warrants it. In fact, Berk's criteria, if considered in sufficient detail, could be the outline for a review itself. The penultimate section, Numerical Details, directly addresses numerical accuracy and reliality, if these topics are not addressed elsewhere in the review.
    [Show full text]
  • Hands-On & Exercise: Stata Hands-On: Eviews & Stata
    @ COMP. LAB. 3, SCHOOL OF MATHEMATICAL SCIENCES, USM PENANG 13 (Sat) & 14 (Sun) MARCH 2021 The OBJECTIVE of this course is to introduce participants with VAR model and panel data structures and also to equip them with core skills in analyzing techniques for various types of panel data HANDS-ON & EXERCISE: STATA COURSE CONTENTS SHORT PANEL DATA ANALYSIS FACILITATORS: ✓ Fixed & Random Effects ✓ Pooled OLS, Hausman Test Dr Zainudin Arsad (USM) & ✓ Arellano-Bond Difference GMM ✓ Blundell-Bond System GMM Ass. Prof. Dr LAW S. HOOK (UPM) ECONOMETRIC ANALYSIS WITH STRUCTURAL BREAK: ✓ Long-run Models: FMOLS/DOLS/CCR ✓ Quandt-Andrews, Bai-Perron Tests ✓ Gregory & Hansen (1996) Test ✓ Johansen, Mosconi and Nieldsen (2000) Cointegration Test WHO SHOULD ATTEND Academics and Graduate Students as well as Researchers, Analysts and Consultants in various business disciplines that may include Private & Public Sector Organizations, Banks & Financial Institutions and Regulatory Authorities. HANDS-ON: EVIEWS & STATA FACILITATOR: Professor Dr. Eng Yoke Kee (UTAR) 3 (Sat) & 4 (Sun) APRIL 2021 SHORT PANEL DATA ANALYSIS DAY 1 : SATURDAY, 13 MARCH 2021 8:15 AM REGISTRATION 8:45 AM SESSION 1 – THE NATURE OF PANEL DATA ▪ Nature and Benefits od Panel Data; Examining & Arranging Your Dataset 10:30 AM TEA BREAK 10:45 AM SESSION 2 – STATIC LINEAR PANEL MODELS ▪ Panel Data Basics: Pooled OLS, Fixed and Random Effects 12:45 PM LUNCH 2:00 PM SESSION 3 – SELECTING PANEL DATA MODELS ▪ Poolability F-test, Breusch-Pagan LM Test, Hausman Test 3.45 PM TEA BREAK 4.00 PM SESSION 4 – ISSUES ON PANEL DATA MODELS: ROBUST ESTIMATES ▪ Diagnostic Tests and Robust Standard Errors 5:45 PM Q & A (SESSIONS 1 - 4) DAY 2 : SUNDAY, 14 MARCH 2021 8:30 AM SESSION 5 – DYNAMIC PANEL DATA APPROACH ▪ Why Dynamic Panel? 10:15 AM TEA BREAK 10:30 AM SESSION 6 – MULTIVARIATE DYNAMIC MODELS WITH PERSISTENT SERIES ▪ Arellano-Bond Difference Estimator, 1-step vs.
    [Show full text]
  • Rtadf: Testing for Bubbles with Eviews
    JSS Journal of Statistical Software November 2017, Volume 81, Code Snippet 1. doi: 10.18637/jss.v081.c01 Rtadf: Testing for Bubbles with EViews Itamar Caspi Bank of Israel Abstract This paper presents Rtadf (right-tail augmented Dickey-Fuller), an EViews add-in that facilitates the performance of time series based tests that help detect and date-stamp asset price bubbles. The detection strategy is based on a right-tail variation of the standard augmented Dickey-Fuller (ADF) test where the alternative hypothesis is of a mildly explo- sive process. Rejection of the null in each of these tests may serve as empirical evidence for an asset price bubble. The add-in implements four types of tests: standard ADF, rolling window ADF, supremum ADF (SADF; Phillips, Wu, and Yu 2011) and general- ized SADF (GSADF; Phillips, Shi, and Yu 2015). It calculates the test statistics for each of the above four tests, simulates the corresponding exact finite sample critical values and p values via Monte Carlo methods, under the assumption of Gaussian innovations, and produces a graphical display of the date stamping procedure. Keywords: rational bubble, ADF test, sup ADF test, generalized sup ADF test, mildly explo- sive process, EViews. 1. Introduction Empirical identification of asset price bubbles in real time, and even in retrospect, is surely not an easy task, and it has been the source of academic and professional debate for several decades.1 One strand of the empirical literature suggests using time series estimation tech- niques while exploiting predictions made by finance theory in order to test for the existence of bubbles in the data.
    [Show full text]
  • Econometrics II Econ 8375 - 01 Fall 2018
    Econometrics II Econ 8375 - 01 Fall 2018 Course & Instructor: Instructor: Dr. Diego Escobari Office: BUSA 218D Phone: 956.665.3366 Email: [email protected] Web Page: http://faculty.utrgv.edu/diego.escobari/ Office Hours: MT 3:00 p.m. { 5:00 p.m., and by appointment Lecture Time: R 4:40 p.m. { 7:10 p.m. Lecture Venue: Weslaco Center for Innovation and Commercialization 2.206 Course Objective: The course objective is to provide students with the main methods of modern time series analysis. Emphasis will be placed on appreciating its scope, understanding the essentials un- derlying the various methods, and developing the ability to relate the methods to important issues. Through readings, lectures, written assignments and computer applications students are expected to become familiar with these techniques to read and understand applied sci- entific papers. At the end of this semester, students will be able to use computer based statistical packages to analyze time series data, will understand how to interpret the output and will be confident to carry out independent analysis. Prerequisites: Applied multivariate data analysis I and II (ISQM/QUMT 8310 and ISQM/QUMT 8311) Textbooks: Main Textbooks: (E) Walter Enders, Applied Econometric Time Series, John Wiley & Sons, Inc., 4th Edi- tion, 2015. ISBN-13: 978-1-118-80856-6. Econometrics II, page 1 of 9 Additional References: (H) James D. Hamilton, Time Series Analysis, Princeton University Press, 1994. ISBN-10: 0-691-04289-6 Classic reference for graduate time series econometrics. (D) Francis X. Diebold, Elements of Forecasting, South-Western Cengage Learning, 4th Edition, 2006.
    [Show full text]
  • Introduction to Eviews 2.0
    Introduction to EViews 2.0 Overview of product EViews provides regression and forecasting tools on Windows computers. With EViews you can develop a statistical relation from your data and then use the relation to forecast future values of the data. Areas where EViews can be useful include: · Sales forecasting · Cost analysis and forecasting · Financial analysis · Macroeconomic forecasting · Simulation · Scientific data analysis and evaluation EViews is a new version of a set of tools for manipulating time series data originally developed in the Time Series Processor software for large computers. The immediate predecessor of EViews was MicroTSP, first released in 1981. Though EViews was developed by economists and most of its uses are in economics, there is nothing in its design that limits its usefulness to economic time series. Even quite large cross-section projects can be handled in EViews. The basic data object within EViews is the time series. Each series has a name, and you can request operations on all the observations just by mentioning the name of the series. EViews provides convenient visual ways to enter time series from the keyboard or from disk files, to create new series from existing ones, to display and print series, and to carry out statistical analysis of the relations among series. EViews uses the visual features of modern Windows software. You can use your mouse to guide the operation with standard Windows menus and dialogs. Results appear in windows and can be manipulated with standard Windows techniques. Alternatively, you may use EViews' powerful command language. You can enter and edit commands in the command window.
    [Show full text]
  • Gretl User's Guide
    Gretl User’s Guide Gnu Regression, Econometrics and Time-series Allin Cottrell Department of Economics Wake Forest university Riccardo “Jack” Lucchetti Dipartimento di Economia Università Politecnica delle Marche December, 2008 Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.1 or any later version published by the Free Software Foundation (see http://www.gnu.org/licenses/fdl.html). Contents 1 Introduction 1 1.1 Features at a glance ......................................... 1 1.2 Acknowledgements ......................................... 1 1.3 Installing the programs ....................................... 2 I Running the program 4 2 Getting started 5 2.1 Let’s run a regression ........................................ 5 2.2 Estimation output .......................................... 7 2.3 The main window menus ...................................... 8 2.4 Keyboard shortcuts ......................................... 11 2.5 The gretl toolbar ........................................... 11 3 Modes of working 13 3.1 Command scripts ........................................... 13 3.2 Saving script objects ......................................... 15 3.3 The gretl console ........................................... 15 3.4 The Session concept ......................................... 16 4 Data files 19 4.1 Native format ............................................. 19 4.2 Other data file formats ....................................... 19 4.3 Binary databases ..........................................
    [Show full text]
  • Eviews 10 Getting Started Eviews 10 Getting Started Copyright © 1994–2017 IHS Global Inc
    EViews 10 Getting Started EViews 10 Getting Started Copyright © 1994–2017 IHS Global Inc. All Rights Reserved ISBN: 978-1-880411-36-0 This software product, including program code and manual, is copyrighted, and all rights are reserved by IHS Global Inc. The distribution and sale of this product are intended for the use of the original purchaser only. Except as permitted under the United States Copyright Act of 1976, no part of this product may be reproduced or distributed in any form or by any means, or stored in a database or retrieval system, without the prior written permission of IHS Global Inc. Disclaimer The authors and IHS Global Inc.assume no responsibility for any errors that may appear in this manual or the EViews program. The user assumes all responsibility for the selection of the pro- gram to achieve intended results, and for the installation, use, and results obtained from the pro- gram. Trademarks EViews® is a registered trademark of IHS Global Inc. Windows, Excel, PowerPoint, and Access are registered trademarks of Microsoft Corporation. PostScript is a trademark of Adobe Corpora- tion. X11.2 and X12-ARIMA Version 0.2.7, and X-13ARIMA-SEATS are seasonal adjustment pro- grams developed by the U. S. Census Bureau. Tramo/Seats is copyright by Agustin Maravall and Victor Gomez. Info-ZIP is provided by the persons listed in the infozip_license.txt file. Please refer to this file in the EViews directory for more information on Info-ZIP. Zlib was written by Jean-loup Gailly and Mark Adler. More information on zlib can be found in the zlib_license.txt file in the EViews directory.
    [Show full text]
  • Houtan BASTANI Philadelphia, PA 19143 Nationalities: USA, France, Iran [email protected]
    Houtan BASTANI Philadelphia, PA 19143 Nationalities: USA, France, Iran [email protected] EDUCATION MSc Economics, London School of Economics 2008-09 Graduated with Merit B.S. Economics and Computer Science, College of William & Mary 2000-03 Graduated Summa Cum Laude, Phi Beta Kappa College education financed through scholarships, grants, loans and work Candidate for B.S.E. Computer Science Engineering, University of Pennsylvania 1998-00 Transferred FELLOWSHIPS, HONORS AND AWARDS US Fulbright Research Fellow, Italy 2003-04 Elected member of Phi Beta Kappa honor society 2002 Frances L. and Edwin K. Cummings Scholar 2002 NSF REU Program 2001, 2002 Dean’s List 1999-03 WORK EXPERIENCE Research Software Engineer, Dynare, CEPREMAP, École Normale Supérieure since 2009 Focused on development of Dynare, primarily in C++ and MATLAB Programmed o OLS, FGLS, Gibbs Sampling for SUR models, VAR forecasts, … o Interpreter for Dynare’s macro processing language o Dynare's Reporting functionality o Dynare's test suite, run nightly upon commit o Integrated Sims, Waggoner and Zha MS-SBVAR code o JSON and Julia output of preprocessor o among others... Responsible for distribution on macOS: create installation packages and snapshots; created and maintain installation scripts on Homebrew Webmaster http://www.dynare.org Consultant, Organisation for Economic Co-operation and Development (OECD) 2018 Added Gandhi, Navarro and Rivers (2017) module to MultiProd codebase Fixed bug in Stata Gtools Gave class on Git Consultant, Copywrighte Design 2018 Created interactive in-store display using Python and Raspberry Pi Consultant, CQER, Federal Reserve Bank of Atlanta 2011 Worked for Dan Waggoner and Tao Zha, creating an interface for use with their Regime-Switching DSGE models.
    [Show full text]