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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 -
Date Created Size MB . تماس بگیر ید 09353344788
Name Software ( Search List Ctrl+F ) Date created Size MB برای سفارش هر یک از نرم افزارها با شماره 09123125449 - 09353344788 تماس بگ ریید . \1\ Simulia Abaqus 6.6.3 2013-06-10 435.07 Files: 1 Size: 456,200,192 Bytes (435.07 MB) \2\ Simulia Abaqus 6.7 EF 2013-06-10 1451.76 Files: 1 Size: 1,522,278,400 Bytes (1451.76 MB) \3\ Simulia Abaqus 6.7.1 2013-06-10 584.92 Files: 1 Size: 613,330,944 Bytes (584.92 MB) \4\ Simulia Abaqus 6.8.1 2013-06-10 3732.38 Files: 1 Size: 3,913,689,088 Bytes (3732.38 MB) \5\ Simulia Abaqus 6.9 EF1 2017-09-28 3411.59 Files: 1 Size: 3,577,307,136 Bytes (3411.59 MB) \6\ Simulia Abaqus 6.9 2013-06-10 2462.25 Simulia Abaqus Doc 6.9 2013-06-10 1853.34 Files: 2 Size: 4,525,230,080 Bytes (4315.60 MB) \7\ Simulia Abaqus 6.9.3 DVD 1 2013-06-11 2463.45 Simulia Abaqus 6.9.3 DVD 2 2013-06-11 1852.51 Files: 2 Size: 4,525,611,008 Bytes (4315.96 MB) \8\ Simulia Abaqus 6.10.1 With Documation 2017-09-28 3310.64 Files: 1 Size: 3,471,454,208 Bytes (3310.64 MB) \9\ Simulia Abaqus 6.10.1.5 2013-06-13 2197.95 Files: 1 Size: 2,304,712,704 Bytes (2197.95 MB) \10\ Simulia Abaqus 6.11 32BIT 2013-06-18 1162.57 Files: 1 Size: 1,219,045,376 Bytes (1162.57 MB) \11\ Simulia Abaqus 6.11 For CATIA V5-6R2012 2013-06-09 759.02 Files: 1 Size: 795,893,760 Bytes (759.02 MB) \12\ Simulia Abaqus 6.11.1 PR3 32-64BIT 2013-06-10 3514.38 Files: 1 Size: 3,685,099,520 Bytes (3514.38 MB) \13\ Simulia Abaqus 6.11.3 2013-06-09 3529.41 Files: 1 Size: 3,700,856,832 Bytes (3529.41 MB) \14\ Simulia Abaqus 6.12.1 2013-06-10 3166.30 Files: 1 Size: 3,320,102,912 Bytes -
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. -
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. -
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). -
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. -
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. -
Overview-Of-Statistical-Analytics-And
Brief Overview of Statistical Analytics and Machine Learning tools for Data Scientists Tom Breur 17 January 2017 It is often said that Excel is the most commonly used analytics tool, and that is hard to argue with: it has a Billion users worldwide. Although not everybody thinks of Excel as a Data Science tool, it certainly is often used for “data discovery”, and can be used for many other tasks, too. There are two “old school” tools, SPSS and SAS, that were founded in 1968 and 1976 respectively. These products have been the hallmark of statistics. Both had early offerings of data mining suites (Clementine, now called IBM SPSS Modeler, and SAS Enterprise Miner) and both are still used widely in Data Science, today. They have evolved from a command line only interface, to more user-friendly graphic user interfaces. What they also share in common is that in the core SPSS and SAS are really COBOL dialects and are therefore characterized by row- based processing. That doesn’t make them inherently good or bad, but it is principally different from set-based operations that many other tools offer nowadays. Similar in functionality to the traditional leaders SAS and SPSS have been JMP and Statistica. Both remarkably user-friendly tools with broad data mining and machine learning capabilities. JMP is, and always has been, a fully owned daughter company of SAS, and only came to the fore when hardware became more powerful. Its initial “handicap” of storing all data in RAM was once a severe limitation, but since computers now have large enough internal memory for most data sets, its computational power and intuitive GUI hold their own. -
Bab 1 Pendahuluan
BAB 1 PENDAHULUAN Bab ini akan membahas pengertian dasar statistik dengan sub-sub pokok bahasan sebagai berikut : Sub Bab Pokok Bahasan A. Sejarah dan Perkembangan Statistik B. Tokoh-tokoh Kontributor Statistika C. Definisi dan Konsep Statistik Modern D. Kegunaan Statistik E. Pembagian Statistik F. Statistik dan Komputer G. Soal Latihan A. Sejarah dan Perkembangan Statistik Penggunaan istilah statistika berakar dari istilah-istilah dalam bahasa latin modern statisticum collegium (“dewan negara”) dan bahasa Italia statista (“negarawan” atau “politikus”). Istilah statistik pertama kali digunakan oleh Gottfried Achenwall (1719-1772), seorang guru besar dari Universitas Marlborough dan Gottingen. Gottfried Achenwall (1749) menggunakan Statistik dalam bahasa Jerman untuk pertama kalinya sebagai nama bagi kegiatan analisis data kenegaraan, dengan mengartikannya sebagai “ilmu tentang negara/state”. Pada awal abad ke- 19 telah terjadi pergeseran arti menjadi “ilmu mengenai pengumpulan dan klasifikasi data”. Sir John Sinclair memperkenalkan nama dan pengertian statistics ini ke dalam bahasa Inggris. E.A.W. Zimmerman mengenalkan kata statistics ke negeri Inggris. Kata statistics dipopulerkan di Inggris oleh Sir John Sinclair dalam karyanya: Statistical Account of Scotland 1791-1799. Namun demikian, jauh sebelum abad XVIII masyarakat telah mencatat dan menggunakan data untuk keperluan mereka. Pada awalnya statistika hanya mengurus data yang dipakai lembaga- lembaga administratif dan pemerintahan. Pengumpulan data terus berlanjut, khususnya melalui sensus yang dilakukan secara teratur untuk memberi informasi kependudukan yang selalu berubah. Dalam bidang pemerintahan, statistik telah digunakan seiring dengan perjalanan sejarah sejak jaman dahulu. Kitab perjanjian lama (old testament) mencatat adanya kegiatan sensus penduduk. Pemerintah kuno Babilonia, Mesir, dan Roma mengumpulkan data lengkap tentang penduduk dan kekayaan alam yang dimilikinya. -
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. -
Stock Market Prediction Using Ensemble of Graph Theory, Machine Learning and Deep Learning Models
San Jose State University SJSU ScholarWorks Master's Projects Master's Theses and Graduate Research Spring 5-20-2019 STOCK MARKET PREDICTION USING ENSEMBLE OF GRAPH THEORY, MACHINE LEARNING AND DEEP LEARNING MODELS Pratik Patil San Jose State University Follow this and additional works at: https://scholarworks.sjsu.edu/etd_projects Part of the Artificial Intelligence and Robotics Commons, and the Other Computer Sciences Commons Recommended Citation Patil, Pratik, "STOCK MARKET PREDICTION USING ENSEMBLE OF GRAPH THEORY, MACHINE LEARNING AND DEEP LEARNING MODELS" (2019). Master's Projects. 692. DOI: https://doi.org/10.31979/etd.38nc-j52r https://scholarworks.sjsu.edu/etd_projects/692 This Master's Project is brought to you for free and open access by the Master's Theses and Graduate Research at SJSU ScholarWorks. It has been accepted for inclusion in Master's Projects by an authorized administrator of SJSU ScholarWorks. For more information, please contact [email protected]. STOCK MARKET PREDICTION USING ENSEMBLE OF GRAPH THEORY, MACHINE LEARNING AND DEEP LEARNING MODELS A Project Report Presented to Dr. Ching seh Wu Department of Computer Science San José State University In Partial Fulfillment Of the Requirements for the Class CS 298 By Pratik Patil May 2019 © 2019 Pratik Patil ALL RIGHTS RESERVED The Designated Thesis Committee Approves the Thesis Titled STOCK MARKET PREDICTION USING ENSEMBLE OF GRAPH THEORY, MACHINE LEARNING AND DEEP LEARNING MODELS by Pratik Patil APPROVED FOR THE DEPARTMENT OF COMPUTER SCIENCE SAN JOSÉ STATE UNIVERSITY May 2019 Dr. Ching seh Wu Department of Computer Science Dr. Katerina Potika Department of Computer Science Dr. Marjan Orang Department of Economics ACKNOWLEDGEMENT This has been one long and arduous journey, but nevertheless a worthwhile life experience because of the many great Professors at SJSU and beloved friends. -
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.