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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. -
A Comparison of Six Numerical Software Packages for Educational Use in the Chemical Engineering Curriculum
SESSION 2520 A COMPARISON OF SIX NUMERICAL SOFTWARE PACKAGES FOR EDUCATIONAL USE IN THE CHEMICAL ENGINEERING CURRICULUM Mordechai Shacham Department of Chemical Engineering Ben-Gurion University of the Negev P. O. Box 653 Beer Sheva 84105, Israel Tel: (972) 7-6461481 Fax: (972) 7-6472916 E-mail: [email protected] Michael B. Cutlip Department of Chemical Engineering University of Connecticut Box U-222 Storrs, CT 06269-3222 Tel: (860)486-0321 Fax: (860)486-2959 E-mail: [email protected] INTRODUCTION Until the early 1980’s, computer use in Chemical Engineering Education involved mainly FORTRAN and less frequently CSMP programming. A typical com- puter assignment in that era would require the student to carry out the following tasks: 1.) Derive the model equations for the problem at hand, 2.) Find an appropri- ate numerical method to solve the model (mostly NLE’s or ODE’s), 3.) Write and debug a FORTRAN program to solve the problem using the selected numerical algo- rithm, and 4.) Analyze the results for validity and precision. It was soon recognized that the second and third tasks of the solution were minor contributions to the learning of the subject material in most chemical engi- neering courses, but they were actually the most time consuming and frustrating parts of computer assignments. The computer indeed enabled the students to solve realistic problems, but the time spent on technical details which were of minor rele- vance to the subject matter was much too long. In order to solve this difficulty, there was a tendency to provide the students with computer programs that could solve one particular type of a problem. -
Primena Statistike U Kliničkim Istraţivanjima Sa Osvrtom Na Korišćenje Računarskih Programa
UNIVERZITET U BEOGRADU MATEMATIČKI FAKULTET Dušica V. Gavrilović Primena statistike u kliničkim istraţivanjima sa osvrtom na korišćenje računarskih programa - Master rad - Mentor: prof. dr Vesna Jevremović Beograd, 2013. godine Zahvalnica Ovaj rad bi bilo veoma teško napisati da nisam imala stručnu podršku, kvalitetne sugestije i reviziju, pomoć prijatelja, razumevanje kolega i beskrajnu podršku porodice. To su razlozi zbog kojih želim da se zahvalim: . Mom mentoru, prof. dr Vesni Jevremović sa Matematičkog fakulteta Univerziteta u Beogradu, koja je bila ne samo idejni tvorac ovog rada već i dugogodišnja podrška u njegovoj realizaciji. Njena neverovatna upornost, razne sugestije, neiscrpni optimizam, profesionalizam i razumevanje, predstavljali su moj stalni izvor snage na ovom master-putu. Članu komisije, doc. dr Zorici Stanimirović sa Matematičkog fakulteta Univerziteta u Beogradu, na izuzetnoj ekspeditivnosti, stručnoj recenziji, razumevanju, strpljenju i brojnim korisnim savetima. Članu komisije, mr Marku Obradoviću sa Matematičkog fakulteta Univerziteta u Beogradu, na stručnoj i prijateljskoj podršci kao i spremnosti na saradnju. Dipl. mat. Radojki Pavlović, šefu studentske službe Matematičkog fakulteta Univerziteta u Beogradu, na upornosti, snalažljivosti i kreativnosti u pronalaženju raznih ideja, predloga i rešenja na putu realizacije ovog master rada. Dugogodišnje prijateljstvo sa njom oduvek beskrajno cenim i oduvek mi mnogo znači. Dipl. mat. Zorani Bizetić, načelniku Data Centra Instituta za onkologiju i radiologiju Srbije, na upornosti, idejama, detaljnoj reviziji, korisnim sugestijama i svakojakoj podršci. Čak i kada je neverovatno ili dosadno ili pametno uporna, mnogo je i dugo volim – skoro ceo moj život. Mast. biol. Jelici Novaković na strpljenju, reviziji, bezbrojnim korekcijama i tehničkoj podršci svake vrste. Hvala na osmehu, budnom oku u sitne sate, izvrsnoj hrani koja me je vraćala u život, nes-kafi sa penom i transfuziji energije kada sam bila na rezervi. -
Rapid Research with Computer Algebra Systems
doi: 10.21495/71-0-109 25th International Conference ENGINEERING MECHANICS 2019 Svratka, Czech Republic, 13 – 16 May 2019 RAPID RESEARCH WITH COMPUTER ALGEBRA SYSTEMS C. Fischer* Abstract: Computer algebra systems (CAS) are gaining popularity not only among young students and schol- ars but also as a tool for serious work. These highly complicated software systems, which used to just be regarded as toys for computer enthusiasts, have reached maturity. Nowadays such systems are available on a variety of computer platforms, starting from freely-available on-line services up to complex and expensive software packages. The aim of this review paper is to show some selected capabilities of CAS and point out some problems with their usage from the point of view of 25 years of experience. Keywords: Computer algebra system, Methodology, Wolfram Mathematica 1. Introduction The Wikipedia page (Wikipedia contributors, 2019a) defines CAS as a package comprising a set of algo- rithms for performing symbolic manipulations on algebraic objects, a language to implement them, and an environment in which to use the language. There are 35 different systems listed on the page, four of them discontinued. The oldest one, Reduce, was publicly released in 1968 (Hearn, 2005) and is still available as an open-source project. Maple (2019a) is among the most popular CAS. It was first publicly released in 1984 (Maple, 2019b) and is still popular, also among users in the Czech Republic. PTC Mathcad (2019) was published in 1986 in DOS as an engineering calculation solution, and gained popularity for its ability to work with typeset mathematical notation in combination with automatic computations. -
The Top Resources for Learning 13 Statistical Topics
The Analysis Factor Ebook: The Top Resources for Learning 13 Statistical Topics The Top Resources for Learning 11131333 Statistical Topics By Karen Grace-Martin Founder & President © Copyright 2008 Karen Grace-Martin, All rights reserved www.analysisfactor.com 1 The Analysis Factor Ebook: The Top Resources for Learning 13 Statistical Topics About the Author Karen Grace-Martin is the founder and president of The Analysis Factor . She is a professional statistical consultant with Masters degrees in both applied statistics and social psychology. Her career started in psychology research, where her frustration in applying statistics to her data led her to take more and more statistics classes. She soon realized that her favorite part of research was data analysis, leading to a career change. Her background in experimental research and working with real data has been invaluable in understanding the challenges that researchers face in using statistics and has spurred her passion for deciphering statistics for academic researchers. Karen was a professional statistical consultant at Cornell University for seven years before founding The Analysis Factor. Karen has worked with clients from undergraduate honors students on their first research project to tenured Ivy League professors, as well as non-profits and businesses. Her ability to understand what researchers need and to explain technical information at the level of the researcher’s understanding has been one of her strongest assets as a consultant. She treats all clients with respect, and derives genuine satisfaction from the relief she hears in their voices when they realize that someone can help them. Before consulting, Karen taught statistics courses for economics, psychology, and sociology majors at the University of California, Santa Barbara and Santa Barbara City College. -
Receiver Operating Characteristic (ROC) Methods in Diagnostic Imaging
8/2/2017 Receiver Operating Characteristic (ROC) Methods in Diagnostic Imaging Elizabeth A. Krupinski, PhD Department Radiology & Imaging Sciences Emory University Bit of History • Developed early 1950s based on principles SDT for eval radar operators detecting enemy aircraft & missiles • Contributions from engineering, psychology & mathematics • Lee Lusted introduced medicine 1960s with significant effort on gaining better understanding decision-making • Result of radiology studies after WWII to determine which of 4 radiographic & fluoroscopic techniques better for TB screening • Goal = single imaging technique outperform others • Found intra & inter-observer variation so high impossible determine • Necessary to build systems generate better images so radiologists’ performance could improve (i.e., reduce observer variability) & develop methods evaluate these new systems & assess impact on observer performance Basics • ROC traditionally binary decision task – target/signal (e.g., lesion, disease, missile) present versus target/signal absent, or in case classification rather than detection target/signal belongs to class 1 (e.g., cancer, enemy) or class 2 (e.g., not cancer, friend) • ROC analysis these two conditions must be mutually exclusive 1 8/2/2017 2 x 2 Matrix Decision = Target Decision = Target Present Absent Truth = Target Present True Positive (TP) False Negative (FN) Truth = Target Absent False Positive (FP) True Negative (TN) Common Performance Metrics • Sensitivity = TP/(TP + FN) • Specificity = TN/(TN + FP) • Accuracy = (TP -
Sigmaplot 11: Now with Total Sigmastat Integration
SigmaPlot 11: Now with Total SigmaStat Integration Imagine my joy as I discovered a complete package of publication-quality graphics software with analytic and presentation tools John A. Wass, Ph.D., in: Scientific Computing International, Jan/Feb 2009 The SYSTAT people who market this product have thrown me a curve. For years, I have bemoaned the fact that most of the upgrade and development efforts that went into the SigmaPlot/SigmaStat software seemed to be biased to the plot side. When I observed that the new package was merely named SigmaPlot, and I further failed to find SigmaStat integration features (the stuff that connects the two programs), the Figure 1: SigmaPlot graphics and wizards, including the Quick Start natural conclusion seemed to be that the statistical Menu (upper right) and the graph program was jettisoned in favor of the graphics. wizard (bottom center) The above introductory narrative is intended to alert the reader to this editor’s long- time love affair with SigmaStat. It was the first statistical software that I used, (seemingly) the first to make a seamless transition from DOS to Windows, and the very first to offer that wonderful Wizard to we befuddled amateur statisticians. My introduction to SigmaPlot came much later, and use of that was only stimulated when the two became integrated. Later on, a pharmacology menu was added and the usage of the plotting software was greatly extended. Of course, the new version has added further graphics and helps to make an already useful program even easier to use. It is now a complete package of publication-quality graphics software with analytic and presentation tools. -
An Introduction to the SAS System
An Introduction to the SAS System Dileep K. Panda Directorate of Water Management Bhubaneswar-751023 [email protected] Introduction The SAS – Statistical Analysis System (erstwhile expansion of SAS) - is the one of the most widely used Statistical Software package by the academic circles and Industry. The SAS software was developed in late 1960s at North Carolina State University and in 1976 SAS Institute was formed. The SAS system is a collection of products, available from the SAS Institute in North Carolina. SAS software is a combination of a statistical package, a data – base management system, and a high level programming language. The SAS is an integrated system of software solutions that performs the following tasks: Data entry, retrieval, and management Report writing and graphics design Statistical and mathematical analysis Business forecasting and decision support Operations research and project management Applications development At the core of the SAS System is the Base SAS software. The Base SAS software includes a fourth-generation programming language and ready-to-use programs called procedures. These integrated procedures handle data manipulation, information storage and retrieval, statistical analysis, and report writing. Additional components offer capabilities for data entry, retrieval, and management; report writing and graphics; statistical and mathematical analysis; business planning, forecasting, and decision support; operations research and project management; quality improvement; and applications development. In general, the Base SAS software has the following capabilities A data management facility A programming language Data analysis and reporting utilities Learning to use Base SAS enables you to work with these features of SAS. It also prepares you to learn other SAS products, because all SAS products follow the same basic rules. -
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. -
Kwame Nkrumah University of Science and Technology, Kumasi
KWAME NKRUMAH UNIVERSITY OF SCIENCE AND TECHNOLOGY, KUMASI, GHANA Assessing the Social Impacts of Illegal Gold Mining Activities at Dunkwa-On-Offin by Judith Selassie Garr (B.A, Social Science) A Thesis submitted to the Department of Building Technology, College of Art and Built Environment in partial fulfilment of the requirement for a degree of MASTER OF SCIENCE NOVEMBER, 2018 DECLARATION I hereby declare that this work is the result of my own original research and this thesis has neither in whole nor in part been prescribed by another degree elsewhere. References to other people’s work have been duly cited. STUDENT: JUDITH S. GARR (PG1150417) Signature: ........................................................... Date: .................................................................. Certified by SUPERVISOR: PROF. EDWARD BADU Signature: ........................................................... Date: ................................................................... Certified by THE HEAD OF DEPARTMENT: PROF. B. K. BAIDEN Signature: ........................................................... Date: ................................................................... i ABSTRACT Mining activities are undertaken in many parts of the world where mineral deposits are found. In developing nations such as Ghana, the activity is done both legally and illegally, often with very little or no supervision, hence much damage is done to the water bodies where the activities are carried out. This study sought to assess the social impacts of illegal gold mining activities at Dunkwa-On-Offin, the capital town of Upper Denkyira East Municipality in the Central Region of Ghana. The main objectives of the research are to identify factors that trigger illegal mining; to identify social effects of illegal gold mining activities on inhabitants of Dunkwa-on-Offin; and to suggest effective ways in curbing illegal mining activities. Based on the approach to data collection, this study adopts both the quantitative and qualitative approach. -
Information Technology Laboratory Technical Accomplishments
CONTENTS Director’s Foreword 1 ITL at a Glance 4 ITL Research Blueprint 6 Accomplishments of our Research Program 7 Foundation Research Areas 8 Selected Cross-Cutting Themes 26 Industry and International Interactions 36 Publications 44 NISTIR 7169 Conferences 47 February 2005 Staff Recognition 50 U.S. DEPARTMENT OF COMMERCE Carlos M. Gutierrez, Secretary Technology Administration Phillip J. Bond Under Secretary of Commerce for Technology National Institute of Standards and Technology Hratch G. Semerjian, Jr., Acting Director About ITL For more information about ITL, contact: Information Technology Laboratory National Institute of Standards and Technology 100 Bureau Drive, Stop 8900 Gaithersburg, MD 20899-8900 Telephone: (301) 975-2900 Facsimile: (301) 840-1357 E-mail: [email protected] Website: http://www.itl.nist.gov INFORMATION TECHNOLOGY LABORATORY D IRECTOR’S F OREWORD n today’s complex technology-driven world, the Information Technology Laboratory (ITL) at the National Institute of Standards and Technology has the broad mission of supporting U.S. industry, government, and Iacademia with measurements and standards that enable new computational methods for scientific inquiry, assure IT innovations for maintaining global leadership, and re-engineer complex societal systems and processes through insertion of advanced information technology. Through its efforts, ITL seeks to enhance productivity and public safety, facilitate trade, and improve the Dr. Shashi Phoha, quality of life. ITL achieves these goals in areas of Director, Information national priority by drawing on its core capabilities in Technology Laboratory cyber security, software quality assurance, advanced networking, information access, mathematical and computational sciences, and statistical engineering. utilizing existing and emerging IT to meet national Information technology is the acknowledged engine for priorities that reflect the country’s broad based social, national and regional economic growth. -
Best-Practice Recommendations for Defining, Identifying, and Handling
Article Organizational Research Methods 16(2) 270-301 ª The Author(s) 2013 Best-Practice Reprints and permission: sagepub.com/journalsPermissions.nav Recommendations for DOI: 10.1177/1094428112470848 orm.sagepub.com Defining, Identifying, and Handling Outliers Herman Aguinis1, Ryan K. Gottfredson1, and Harry Joo1 Abstract The presence of outliers, which are data points that deviate markedly from others, is one of the most enduring and pervasive methodological challenges in organizational science research. We provide evidence that different ways of defining, identifying, and handling outliers alter substantive research conclusions. Then, we report results of a literature review of 46 methodological sources (i.e., journal articles, book chapters, and books) addressing the topic of outliers, as well as 232 organizational science journal articles mentioning issues about outliers. Our literature review uncovered (a) 14 unique and mutually exclusive outlier defi- nitions, 39 outlier identification techniques, and 20 different ways of handling outliers; (b) inconsistencies in how outliers are defined, identified, and handled in various methodological sources; and (c) confusion and lack of transparency in how outliers are addressed by substantive researchers. We offer guidelines, including decision-making trees, that researchers can follow to define, identify, and handle error, inter- esting, and influential (i.e., model fit and prediction) outliers. Although our emphasis is on regression, structural equation modeling, and multilevel modeling, our general framework forms the basis for a research agenda regarding outliers in the context of other data-analytic approaches. Our recommenda- tions can be used by authors as well as journal editors and reviewers to improve the consistency and transparency of practices regarding the treatment of outliers in organizational science research.