Econometric Modeling of Financial Time Series Volatility Using Software Packages
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Seminar to Advanced Macroeconomics
Seminar to Advanced Macroeconomics Jaromír Baxa IES FSV UK, WS 2010/2011 Introduction Aim of the seminar: Overview over empirical methods used in macro to make your horizons wider. Easy applications of econometrics to macroeconomic topics discussed in the lectures Using econometric software Talking about your Project Tasks and discussion about Problem Sets Why Empirical Seminar? The Role of Empirical Work in Macro Correspondence between the theory and real data Forecasting and economic policy Finding empirical evidence to build new theories Fundamental ucertainty in econometrics: choice of variables => Robustness over different datasets, over different additional variables... => We should always keep in mind this uncertainty and ask: Are my results good because of the datasets? Methods Descriptive statistics, tests... Some nonparametric methods: tests, density estimates Linear Regression Panel data regression Principal Components method Time series: seasonal adjustment, trends... Dynamic models (very brief introduction) ... Software You can't do empirical work without it. There are many software packages for econometrics: Commercial: TSP, SAS, Stata, E-views, PC-Give, Gauss, S-Plus and many others Freeware/Open Source/Shareware without limitations: Gretl, R-Project, Ox See http://freestatistics.altervista.org/stat.php for comprehensive list. Use whatever you want to And bring your laptop with (if you can) Gretl Available in Room 016: TSP (GiweWin GUI), SPSS for Windows 10.0, R (with necessary libraries), Gretl, JMulti Gretl: http://gretl.sourceforge.net, GNU GPL licence, crossplatform. Have a look into documentation: manual as an textbook available. Don't forget to install seasonal adjustment methods, we will use them in a couple of weeks. -
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. -
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.............................. -
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. -
Regression Models by Gretl and R Statistical Packages for Data Analysis in Marine Geology Polina Lemenkova
Regression Models by Gretl and R Statistical Packages for Data Analysis in Marine Geology Polina Lemenkova To cite this version: Polina Lemenkova. Regression Models by Gretl and R Statistical Packages for Data Analysis in Marine Geology. International Journal of Environmental Trends (IJENT), 2019, 3 (1), pp.39 - 59. hal-02163671 HAL Id: hal-02163671 https://hal.archives-ouvertes.fr/hal-02163671 Submitted on 3 Jul 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution| 4.0 International License International Journal of Environmental Trends (IJENT) 2019: 3 (1),39-59 ISSN: 2602-4160 Research Article REGRESSION MODELS BY GRETL AND R STATISTICAL PACKAGES FOR DATA ANALYSIS IN MARINE GEOLOGY Polina Lemenkova 1* 1 ORCID ID number: 0000-0002-5759-1089. Ocean University of China, College of Marine Geo-sciences. 238 Songling Rd., 266100, Qingdao, Shandong, P. R. C. Tel.: +86-1768-554-1605. Abstract Received 3 May 2018 Gretl and R statistical libraries enables to perform data analysis using various algorithms, modules and functions. The case study of this research consists in geospatial analysis of Accepted the Mariana Trench, a hadal trench located in the Pacific Ocean. -
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. -
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. -
8 the Software Jmulti
P1: IML CB698-Driver CB698-LUTKEPOHI CB698-LUTKEPOHI-Sample.cls August 25, 2006 17:44 8 The Software JMulTi Markus Kr¨atzig 8.1 Introduction to JMulTi 8.1.1 Software Concept This chapter gives a general overview of the software by which the examples in this book can be reproduced; it is freely available via the Internet.1 The information given here covers general issues and concepts of JMulTi. Detailed descriptions on how to use certain methods in the program are left to the help system installed with the software. JMulTi is an interactive JAVA application designed for the specific needs of time series analysis. It does not compute the results of the statistical calcu- lations itself but delegates this part to a computational engine via a communi- cations layer. The range of its own computing functions is limited and is only meant to support data transformations to provide input for the various statistical routines. Like other software packages, JMulTi contains graphical user interface (GUI) components that simplify tasks common to empirical analysis – espe- cially reading in data, transforming variables, creating new variables, editing data, and saving data sets. Most of its functions are accessible by simple mouse interaction. Originally the software was designed as an easy-to-use GUI for complex and difficult-to-use econometric procedures written in GAUSS that were not available in other packages. Because this concept has proved to be quite fruit- ful, JMulTi has now evolved into a comprehensive modeling environment for multiple time series analysis. The underlying general functionality has been bundled in the software framework JStatCom, which is designed as a ready-made platform for the creation of various statistical applications by developers. -
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. -
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 .......................................... -
IASK Conference Nunes&Balsa .Pdf
Teaching Methodologies and Open Source Software: Empirical Application to Econometrics and Mathematics Alcina Nunes and Carlos Balsa Abstract — Nowadays, the software open source represents an important teaching resource. However, it is not sufficiently explored as an higher education teaching methodology. In subjects with a very specific goal, applied contents and attended by a small number of students, the commercial software is still preferred to the open source software. Aware of this reality, this paper presents a reflection about the use of open source software in Applied Econometrics and Mathematics. The adoption of two different software programmes – Gretl and Octave – allows the discussion about a comprehensive set of pedagogical, economical and technical advantages and some observed practical limitations. In practice, the refereed advantages are stressed when the students’ results are analysed. Their academic results benefit from the adoption of software which is executed, distributed and improved freely. Index Terms — Econometrics, Mathematics, Open-Source software , Teaching methodologies. —————————— —————————— 1 INTRODUCTION commercial software with which the teachers seem to have created strong personal and he access to open source software is professional links. Tnowadays a rather unexplored source of In the Escola Superior de Tecnologia e de academic pedagogic tools. Software Gestão do Instituto Politécnico de Bragança tools which are crucial to the promotion of, (ESTiG-IPB ) the use of open source software both theoretic and practical, learning is not uncommon. Indeed, the commercial processes in higher education teaching software is being substituted by open source institutions. software concerning operative systems, Along with commercial labels it is possible internet navigation, e-mail management and to find a large diversity of high quality free word processing programmes. -
Using the Vars Package
Using the vars package Dr. Bernhard Pfaff, Kronberg im Taunus September 9, 2007 Abstract The utilisation of the functions contained in the package ‘vars’ are ex- plained by employing a data set of the Canadian economy. The package’s scope includes functions for estimating vector autoregressive (henceforth: VAR) and structural vector autoregressive models (henceforth: SVAR). In addition to the two cornerstone functions VAR() and SVAR() for estimat- ing such models, functions for diagnostic testing, estimation of restricted VARs, prediction of VARs, causality analysis, impulse response analysis (henceforth: IRA) and forecast error variance decomposition (henceforth: FEVD) are provided too. In each section, the underlying concept and/or method is briefly explained, thereby drawing on the exibitions in Lutke-¨ pohl [2006], Lutkepohl,¨ Kr¨atzig, Phillips, Ghysels & Smith [2004], Lutke-¨ pohl & Breitung [1997], Hamilton [1994] and Watson [1994]. The package’s code is purely written in R and S3-classes with methods have been utilised. It is shipped with a NAMESPACE and a ChangeLog file. It has dependencies to MASS (see Venables & Ripley [2002]) and strucchange (see Zeileis, Leisch, Hornik & Kleiber [2002]). Contents 1 Introduction 2 2 VAR: Vector autoregressive models 2 2.1 Definition . 2 2.2 Estimation . 3 2.3 Restricted VARs . 7 2.4 Diagnostic testing . 9 2.5 Causality Analysis . 14 2.6 Forecasting . 17 2.7 Impulse response analysis . 19 2.8 Forecast error variance decomposition . 21 3 SVAR: Structural vector autoregressive models 23 3.1 Definition . 23 3.2 Estimation . 24 3.3 Impulse response analysis . 27 3.4 Forecast error variance decomposition . 28 4 VECM to VAR 28 1 1 Introduction Since the critique of Sims [1980] in the early eighties of the last cen- tury, VAR analysis has evolved as a standard instrument in econometrics for analysing multivariate time series.