Applied Econometrics Using MATLAB
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Stan: a Probabilistic Programming Language
JSS Journal of Statistical Software MMMMMM YYYY, Volume VV, Issue II. http://www.jstatsoft.org/ Stan: A Probabilistic Programming Language Bob Carpenter Andrew Gelman Matt Hoffman Columbia University Columbia University Adobe Research Daniel Lee Ben Goodrich Michael Betancourt Columbia University Columbia University University of Warwick Marcus A. Brubaker Jiqiang Guo Peter Li University of Toronto, NPD Group Columbia University Scarborough Allen Riddell Dartmouth College Abstract Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.2.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propa- gation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line, through R using the RStan package, or through Python using the PyStan package. All three interfaces support sampling and optimization-based inference. RStan and PyStan also provide access to log probabilities, gradients, Hessians, and data I/O. Keywords: probabilistic program, Bayesian inference, algorithmic differentiation, Stan. -
Introduction Rats Version 9.0
RATS VERSION 9.0 INTRODUCTION RATS VERSION 9.0 INTRODUCTION Estima 1560 Sherman Ave., Suite 510 Evanston, IL 60201 Orders, Sales Inquiries 800–822–8038 Web: www.estima.com General Information 847–864–8772 Sales: [email protected] Technical Support 847–864–1910 Technical Support: [email protected] Fax: 847–864–6221 © 2014 by Estima. All Rights Reserved. No part of this book may be reproduced or transmitted in any form or by any means with- out the prior written permission of the copyright holder. Estima 1560 Sherman Ave., Suite 510 Evanston, IL 60201 Published in the United States of America Preface Welcome to Version 9 of rats. We went to a three-book manual set with Version 8 (this Introduction, the User’s Guide and the Reference Manual; and we’ve continued that into Version 9. However, we’ve made some changes in emphasis to reflect the fact that most of our users now use electronic versions of the manuals. And, with well over a thousand example programs, the most common way for people to use rats is to pick an existing program and modify it. With each new major version, we need to decide what’s new and needs to be ex- plained, what’s important and needs greater emphasis, and what’s no longer topical and can be moved out of the main documentation. For Version 9, the chapters in the User’s Guide that received the most attention were “arch/garch and related mod- els” (Chapter 9), “Threshold, Breaks and Switching” (Chapter 11), and “Cross Section and Panel Data” (Chapter 12). -
1 Dynamisches Tool Zur (Grafischen) Datenanalyse
Statistik-Software Kommerzielle Software Freeware S-Plus www.insightful.com/products/splus/ R www.r-project.org/ SPSS www.spss.com/ PSPP www.gnu.org/software/pspp/pspp.html NCSS www.ncss.com/ Matlab www.mathworks.de/ Octave, Scilab www.scilab.org/ Eviews www.eviews.com PD Dr. Matthias Fischer Ox www.oxmetrics.net/ SAS www.sas.com/ [email protected] STATA www.stata.com/ LIMDEP www.limdep.com/ JMultiwww.jmulti.de/ Version 2.0 Matthias Fischer () Einf¨uhrung in R Version 2.0 1 / 95 Matthias Fischer () Einf¨uhrung in R Version 2.0 2 / 95 Warum R? R-Homepage http://www.r-project.org/ 1 Dynamisches Tool zur (grafischen) Datenanalyse 2 Taschenrechnerfunktion 3 Programmiersprache 4 Freeware-Version von S-PLUS 5 Alternative zu MATLAB, GAUSS, OX, etc. 6 Erg¨anzung zu Excel oder SPSS Download, Dokumentation, Newsgroups Matthias Fischer () Einf¨uhrung in R Version 2.0 3 / 95 Matthias Fischer () Einf¨uhrung in R Version 2.0 4 / 95 R-Installation 1/4 Start der Datei R-2.2.1-win32.exe Installation Matthias Fischer () Einf¨uhrung in R Version 2.0 5 / 95 Matthias Fischer () Einf¨uhrung in R Version 2.0 6 / 95 R-Installation 2/4 R-Installation 3/4 Matthias Fischer () Einf¨uhrung in R Version 2.0 7 / 95 Matthias Fischer () Einf¨uhrung in R Version 2.0 8 / 95 R-Installation 4/4 Literatur Matthias Fischer () Einf¨uhrung in R Version 2.0 9 / 95 Matthias Fischer () Einf¨uhrung in R Version 2.0 10 / 95 R-B¨ucher R-B¨ucher Everitt Behr An R and S-Plus Companion to Multivariate Analysis. -
The Evolution of Econometric Software Design: a Developer's View
Journal of Economic and Social Measurement 29 (2004) 205–259 205 IOS Press The evolution of econometric software design: A developer’s view Houston H. Stokes Department of Economics, College of Business Administration, University of Illinois at Chicago, 601 South Morgan Street, Room 2103, Chicago, IL 60607-7121, USA E-mail: [email protected] In the last 30 years, changes in operating systems, computer hardware, compiler technology and the needs of research in applied econometrics have all influenced econometric software development and the environment of statistical computing. The evolution of various representative software systems, including B34S developed by the author, are used to illustrate differences in software design and the interrelation of a number of factors that influenced these choices. A list of desired econometric software features, software design goals and econometric programming language characteristics are suggested. It is stressed that there is no one “ideal” software system that will work effectively in all situations. System integration of statistical software provides a means by which capability can be leveraged. 1. Introduction 1.1. Overview The development of modern econometric software has been influenced by the changing needs of applied econometric research, the expanding capability of com- puter hardware (CPU speed, disk storage and memory), changes in the design and capability of compilers, and the availability of high-quality subroutine libraries. Soft- ware design in turn has itself impacted applied econometric research, which has seen its horizons expand rapidly in the last 30 years as new techniques of analysis became computationally possible. How some of these interrelationships have evolved over time is illustrated by a discussion of the evolution of the design and capability of the B34S Software system [55] which is contrasted to a selection of other software systems. -
Stan: a Probabilistic Programming Language
JSS Journal of Statistical Software January 2017, Volume 76, Issue 1. http://www.jstatsoft.org/ Stan: A Probabilistic Programming Language Bob Carpenter Andrew Gelman Matthew D. Hoffman Columbia University Columbia University Adobe Creative Technologies Lab Daniel Lee Ben Goodrich Michael Betancourt Columbia University Columbia University Columbia University Marcus A. Brubaker Jiqiang Guo Peter Li York University NPD Group Columbia University Allen Riddell Indiana University Abstract Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propa- gation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces sup- port sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting. -
Rice Research Versus Rice Imports in Malaysia: a Dynamic Spatial Equilibrium Model
COPYRIGHT AND USE OF THIS THESIS This thesis must be used in accordance with the provisions of the Copyright Act 1968. Reproduction of material protected by copyright may be an infringement of copyright and copyright owners may be entitled to take legal action against persons who infringe their copyright. Section 51 (2) of the Copyright Act permits an authorized officer of a university library or archives to provide a copy (by communication or otherwise) of an unpublished thesis kept in the library or archives, to a person who satisfies the authorized officer that he or she requires the reproduction for the purposes of research or study. The Copyright Act grants the creator of a work a number of moral rights, specifically the right of attribution, the right against false attribution and the right of integrity. You may infringe the author’s moral rights if you: - fail to acknowledge the author of this thesis if you quote sections from the work - attribute this thesis to another author - subject this thesis to derogatory treatment which may prejudice the author’s reputation For further information contact the University’s Director of Copyright Services sydney.edu.au/copyright RICE RESEARCH VERSUS RICE IMPORTS IN MALAYSIA: A DYNAMIC SPATIAL EQUILIBRIUM MODEL Deviga Vengedasalam A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy Department of Agricultural and Resource Economics Faculty of Agriculture and Environment The University of Sydney New South Wales Australia February 2013 STATEMENT OF ORIGINALITY I hereby certify that the substance of the material used in this study is my own research and has not been submitted or is not currently being submitted for any other degree. -
A Study on the Utilization of E-Resources Among College Students
International Journal of Knowledge Engineering, Vol. 6, No. 1, June 2020 A Study on the Utilization of e-Resources among College Students Rebilyn G. Roman, Charles Spencer P. Trobada, Ferginia P. Gaton, Catherine Kate Gania, Solomon Ayodele Oluyinka, Heidie O. Cuenco, and Richard G. Daenos libraries more convenient. Libraries need to prioritize and Abstract—E-resources are information resources that can be acquire materials that are relevant to the fields of study of the accessed through the use of electronic devices. With the fast user community and supplement these materials with development of technology, it is imperative that libraries also e-resources. adapt to the changing times. Libraries are now incorporating e-resources to their collection to accommodate the needs of their The management of information in university libraries has clients. Gale is an electronic database that houses e-books and greatly changed due to the advent of electronic information e-journals. This study examined the factors that affect the resources [6]. With the use of electronic resources, this made utilization of the Gale electronic resources among college it possible for researchers to gather and use the information students. Questionnaires were randomly distributed to a total of needed anytime thus getting updated with the current trends 201 3rd year and 4th year students. The factors identified, and developments of the field [7]. E-resources serve as a Awareness, Usefulness and Challenges were derived from related studies. The collected data were analyzed using solution to save space and regulate the flow of information in SmartPLS. The results showed that the aforementioned factors libraries [8], [9]. -
Statistický Software
Statistický software 1 Software AcaStat GAUSS MRDCL RATS StatsDirect ADaMSoft GAUSS NCSS RKWard[4] Statistix Analyse-it GenStat OpenEpi SalStat SYSTAT The ASReml GoldenHelix Origin SAS Unscrambler Oxprogramming Auguri gretl language SOCR UNISTAT BioStat JMP OxMetrics Stata VisualStat BrightStat MacAnova Origin Statgraphics Winpepi Dataplot Mathematica Partek STATISTICA WinSPC EasyReg Matlab Primer StatIt XLStat EpiInfo MedCalc PSPP StatPlus XploRe EViews modelQED R SPlus Excel Minitab R Commander[4] SPSS 2 Data miningovýsoftware n Cca 20 až30 dodavatelů n Hlavníhráči na trhu: n Clementine, IBM SPSS Modeler n IBM’s Intelligent Miner, (PASW Modeler) n SGI’sMineSet, n SAS’s Enterprise Miner. n Řada vestavěných produktů: n fraud detection: n electronic commerce applications, n health care, n customer relationship management 3 Software-SAS n : www.sas.com 4 SAS n Společnost SAS Institute n Vznik 1976 v univerzitním prostředí n Dnes:největšísoukromásoftwarováspolečnost na světě (více než11.000 zaměstnanců) n přes 45.000 instalací n cca 9 milionů uživatelů ve 118 zemích n v USA okolo 1.000 akademických zákazníků (SAS používávětšina vyšších a vysokých škol a výzkumných pracovišť) 5 SAS 6 SAS 7 SAS q Statistickáanalýza: Ø Popisnástatistika Ø Analýza kontingenčních (frekvenčních) tabulek Ø Regresní, korelační, kovariančníanalýza Ø Logistickáregrese Ø Analýza rozptylu Ø Testováníhypotéz Ø Diskriminačníanalýza Ø Shlukováanalýza Ø Analýza přežití Ø … 8 SAS q Analýza časových řad: Ø Regresnímodely Ø Modely se sezónními faktory Ø Autoregresnímodely Ø -
Agricultural Policy Analysis Project
AGRICULTURAL POLICY ANALYSIS PROJECT Science and Technology, Office of Agriculture Under contract to the Agency for International Development, Bureau for Project Office 4250 Connecticut Avenue, N.W. Washington, D.C 20008 * Telephone: (202) 362-2800 AGRICULTURAL POLICY ANALYSIS TOOLS FOR ECONOMIC DEVELOPMENT APAP MAIN DOCUMENT NO. 2 By: LUTHER G. TWEETEN, Editor, et. al. NOVEMBER, 1988 Submitted to: Dr. William Goodwin U.S. Agency ior International Development S&T/AGR, SA-18 Reor 403 Washington, D.C. 20523 Prime Contractor: At Associates Inc., 55 Wheeler Street. Cambridge. Massachusetts 02138 (617) 492-7100 Subcontractors: Robert R. Nathan Associates, Inc. 1301 Pennsylvania Avenue, NW.. Washington. DC 20004 (202) 393-2700 Abel, Daft & Earigy, 1339 Wisconsin Avenue. NW.. Washington. DC 20007 (202) 342-7620 Oklahoma State University, Department o Agricultural Economics. Stillwater. Oklahoma 74078 (405) 624-6157 AGENCY FOR INTERNATIONAL DEVELOPMENT PPC/CDIE/DI REPORT PROCESSING FORM ENTER INFOPMATION ONLY IF NOT INCLUDED ON COVER OR TITLE PAGE OF DOCUMENT 1. Proiect/Subproiect Number 2. Contract/Grant Number 3. Publication Date APAP/936-4084 DAN-4084-C-00-3087-00 November, 1988 4. Document Title/Translated Title Agricultural Policy Analysis Tools for Economic Development L 5. Author(s) I. Luther G. Tweeten editor, et. al. 3. 6 Contributing Organization(s) IAbt Associates, In., Cambridge, MA ;Robert R. Nathan Associates, Inc., Washington, D.C. Abel, Daft and Earley, Inc., Alexandria, VA A.W Z State ni iv ept. f A-r. nllcwntpr OK 7. a.maon. ort '". SSmberonsorin A I. l-.6 ice_ 393 PAP Main Docunt[ AD/ST/AGR/EPP 10. Abstract (optional - .50 word limit) "Tools" provides approaches to and techniques useful in analyzing very real world problems in agricultural policy analysis. -
Models De Dades Dels SIG a Internet. Aspectes Teòrics I Aplicats
Models de dades dels SIG a Internet. Aspectes teòrics i aplicats Internet GIS Data models. Theoretical and applied aspects Tesi Doctoral Doctorat en Geografia Facultat de Filosofia i Lletres Universitat Autònoma de Barcelona Doctorand: Joan Masó Pau Director: Dr. Xavier Pons Fernández Juny del 2012 La portada l’aquest document es basa en una composició RGB en color fals obtinguda amb 3 canals espectrals (Infraroig proper, infraroig mitjà i vermell) que provenen d’un mosaic d’escenes Landsat 7 preses l’agost de 2003 i que es pot obtenir del servidor SatCat. (www.opengis.uab.cat/wms/satcat). Per a realitzar la portada s’ha escollit una regió de Catalunya que no contingués mar ni zones fora de l’àmbit. Per a la contraportada (a l’esquerra) i per a la portada (a la dreta), la regió s’ha dividit en sengles parts La imatge té 20 m de costat de píxel i ha estat tallada en tessel∙les de 256x256 píxels amb els procediments de preparació de capes del MiraMon per accelerar el rendiment en proporcionar una capa en un servei conforme a l’estàndard WMTS. La tessel∙lació consta de 55x55 tessel∙les de les qual finalment es fa servir una matriu de 24x17, començant a la tessel∙la (12,12) i acabant a la tessel∙la (28,35). “You're being confused by irrelevant data. Ignore it.” Seven of Nine from: Survival Instinct (1999) Star Trek Voyager series “This was just a first step. In time you’ll take another. Small moves, Ellie. Small moves.” Ted Arroway from: Contact (1997) movie ÍNDEX/TABLE OF CONTENTS Índex general/Table of contents ÍNDEX/TABLE OF CONTENTS ........................................................................................... -
State Space Methods in RATS
JSS Journal of Statistical Software May 2011, Volume 41, Issue 9. http://www.jstatsoft.org/ State Space Methods in RATS Thomas Doan Estima Abstract This paper uses several examples to show how the econometrics program RATS can be used to analyze state space models. It demonstrates Kalman filtering and smoothing, es- timation of hyperparameters, unconditional and conditional simulation. It also provides a more complicated example where a dynamic simultaneous equations model is transformed into a proper state space representation and its unknown parameters are estimated. Keywords: ARMA model, Kalman filter, state space methods, unobserved components, soft- ware tools. 1. Introduction RATS (Estima 2010) is a general-purpose econometrics and programming package, with a specialty in time series analysis. The instruction in RATS for handling state space models is called DLM (for dynamic linear models). This was introduced with version 5.00 in 2001. Since then, there have been many improvements. With version 8, the instruction has the following features: All component matrices can be fixed matrices, or time-varying formulas, or can be computed using functions of arbitrary complexity. Multiple observables (yt with dimension > 1) are permitted, with proper handling of situations where some components are missing, but some are present. Non-stationary roots in the transition matrix are treated with the \exact" (limit) meth- ods of Koopman(1997) and Durbin and Koopman(2001). The transition matrix is analyzed automatically for stationary and non-stationary roots. The ergodic variance for the stationary (linear combinations of) states is computed using the efficient Schur decomposition method described in Doan(2010). 2 State Space Methods in RATS The calculations of the Kalman filter and smoother can switch (under user control) to use the last calculated values of the Kalman gain and predictive variances. -
TMS320C6652 and TMS320C6654 Fixed and Floating-Point Digital Signal Processor Datasheet
Product Order Technical Tools & Support & Folder Now Documents Software Community TMS320C6652, TMS320C6654 SPRS841E –MARCH 2012–REVISED OCTOBER 2019 TMS320C6652 and TMS320C6654 Fixed and Floating-Point Digital Signal Processor 1 Device Overview 1.1 Features 1 • One TMS320C66x DSP Core Subsystem – 32-Bit DDR3 Interface (CorePac) – DDR3-1066 – C66x Fixed- and Floating-Point CPU Core: Up – 4GB of Addressable Memory Space to 850 MHz for C6654 and 600 MHz for C6652 – 16-Bit EMIF • Multicore Shared Memory Controller (MSMC) – Universal Parallel Port – Memory Protection Unit for DDR3_EMIF – Two Channels of 8 Bits or 16 Bits Each • Multicore Navigator – Supports SDR and DDR Transfers – 8192 Multipurpose Hardware Queues with – Two UART Interfaces Queue Manager – Two Multichannel Buffered Serial Ports – Packet-Based DMA for Zero-Overhead (McBSPs) Transfers – I2C Interface • Peripherals – 32 GPIO Pins – PCIe Gen2 (C6654 Only) – SPI Interface – Single Port Supporting 1 or 2 Lanes – Semaphore Module – Supports up to 5 GBaud Per Lane – Eight 64-Bit Timers – Gigabit Ethernet (GbE) Subsystem (C6654 – Two On-Chip PLLs Only) • Commercial Temperature: – One SGMII Port (C6654 Only) – 0°C to 85°C – Supports 10-, 100-, and 1000-Mbps • Extended Temperature: Operation – –40°C to 100°C 1.2 Applications • Power Protection Systems • Medical Imaging • Avionics and Defense • Other Embedded Systems • Currency Inspection and Machine Vision • Industrial Transportation Systems 1.3 Description The C6654 and C6652 are high performance fixed- and floating-point DSPs that are based on TI's KeyStone multicore architecture. Incorporating the new and innovative C66x DSP core, this device can run at a core speed of up to 850 MHz for C6654 and 600 MHz for C6652.