Essays in Applied Microeconomics
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Python for Economists Alex Bell
Python for Economists Alex Bell [email protected] http://xkcd.com/353 This version: October 2016. If you have not already done so, download the files for the exercises here. Contents 1 Introduction to Python 3 1.1 Getting Set-Up................................................. 3 1.2 Syntax and Basic Data Structures...................................... 3 1.2.1 Variables: What Stata Calls Macros ................................ 4 1.2.2 Lists.................................................. 5 1.2.3 Functions ............................................... 6 1.2.4 Statements............................................... 7 1.2.5 Truth Value Testing ......................................... 8 1.3 Advanced Data Structures .......................................... 10 1.3.1 Tuples................................................. 10 1.3.2 Sets .................................................. 11 1.3.3 Dictionaries (also known as hash maps) .............................. 11 1.3.4 Casting and a Recap of Data Types................................. 12 1.4 String Operators and Regular Expressions ................................. 13 1.4.1 Regular Expression Syntax...................................... 14 1.4.2 Regular Expression Methods..................................... 16 1.4.3 Grouping RE's ............................................ 18 1.4.4 Assertions: Non-Capturing Groups................................. 19 1.4.5 Portability of REs (REs in Stata).................................. 20 1.5 Working with the Operating System.................................... -
Social Economics: Organizations John B
Marquette University e-Publications@Marquette Economics Faculty Research and Publications Economics, Department of 1-1-1998 Social Economics: Organizations John B. Davis Marquette University, [email protected] Published version. "Social Economics: Organizations," in Encyclopedia of Political Economy. Eds. Phillip Anthony O'Hara. London: Taylor & Francis (Routledge), 1998: 1038-1040. Publisher Link. © 1998 Taylor & Francis (Routledge). Used with permission. social economics: organizations Selected references ment, social security, credit policy, the Federal Reserve and postwar reconstruction). ThoUgh Apter, David E. (1993) "Social Democracy," in early contributors were influenced by neoclas_ William Outhwaite and Tom Bottomore sical economists, solidarism, a theoretical (eds), Blackwell Dictionary of Twentieth orientation critical of neoclassical economics Century Social Thought, Oxford: Blackwell. with origins in the work of Heinrich Pesch and Bernstein, Eduard (1898) Evolutionary Social Goetz Briefs in interwar Germany, became ism, New York: Schocken, 1961. increasing influential. Members of the ASE Milner, Henry (1990) Sweden: Social Democ tended to reject liberalism and laissez-faire, and racy in Practice, Oxford: Oxford University argued for a social market economy and an Press. economic process embedded in the larger living Nettl, IP. (1973) "Social Democracy in Ger context of society. many and Revisionism," in Dictionary of the In 1970 the members of the Association History of Ideas, vo l. 4, Studies of Selected elected to give up their strict identification with Pivotal Ideas, ed. Philip P. Wiener, New York: Catholic social thought, and gave the organiza Charles Scribner's Sons. tion its current name to refle~t the increasingly Pierson, Christopher (1991) Beyond the Welfare pluralistic character of its membership. -
TSP 5.0 Reference Manual
TSP 5.0 Reference Manual Bronwyn H. Hall and Clint Cummins TSP International 2005 Copyright 2005 by TSP International First edition (Version 4.0) published 1980. TSP is a software product of TSP International. The information in this document is subject to change without notice. TSP International assumes no responsibility for any errors that may appear in this document or in TSP. The software described in this document is protected by copyright. Copying of software for the use of anyone other than the original purchaser is a violation of federal law. Time Series Processor and TSP are trademarks of TSP International. ALL RIGHTS RESERVED Table Of Contents 1. Introduction_______________________________________________1 1. Welcome to the TSP 5.0 Help System _______________________1 2. Introduction to TSP ______________________________________2 3. Examples of TSP Programs _______________________________3 4. Composing Names in TSP_________________________________4 5. Composing Numbers in TSP _______________________________5 6. Composing Text Strings in TSP_____________________________6 7. Composing TSP Commands _______________________________7 8. Composing Algebraic Expressions in TSP ____________________8 9. TSP Functions _________________________________________10 10. Character Set for TSP ___________________________________11 11. Missing Values in TSP Procedures _________________________13 12. LOGIN.TSP file ________________________________________14 2. Command summary_______________________________________15 13. Display Commands _____________________________________15 -
Towards a Fully Automated Extraction and Interpretation of Tabular Data Using Machine Learning
UPTEC F 19050 Examensarbete 30 hp August 2019 Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Per Hedbrant Master Thesis in Engineering Physics Department of Engineering Sciences Uppsala University Sweden Abstract Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Teknisk- naturvetenskaplig fakultet UTH-enheten Motivation A challenge for researchers at CBCS is the ability to efficiently manage the Besöksadress: different data formats that frequently are changed. Significant amount of time is Ångströmlaboratoriet Lägerhyddsvägen 1 spent on manual pre-processing, converting from one format to another. There are Hus 4, Plan 0 currently no solutions that uses pattern recognition to locate and automatically recognise data structures in a spreadsheet. Postadress: Box 536 751 21 Uppsala Problem Definition The desired solution is to build a self-learning Software as-a-Service (SaaS) for Telefon: automated recognition and loading of data stored in arbitrary formats. The aim of 018 – 471 30 03 this study is three-folded: A) Investigate if unsupervised machine learning Telefax: methods can be used to label different types of cells in spreadsheets. B) 018 – 471 30 00 Investigate if a hypothesis-generating algorithm can be used to label different types of cells in spreadsheets. C) Advise on choices of architecture and Hemsida: technologies for the SaaS solution. http://www.teknat.uu.se/student Method A pre-processing framework is built that can read and pre-process any type of spreadsheet into a feature matrix. Different datasets are read and clustered. An investigation on the usefulness of reducing the dimensionality is also done. -
Socioeconomics: Choice and Challenges Jeffrey L
University of Florida Levin College of Law UF Law Scholarship Repository Faculty Publications Faculty Scholarship 2-1-2004 Socioeconomics: Choice and Challenges Jeffrey L. Harrison University of Florida Levin College of Law, [email protected] Follow this and additional works at: http://scholarship.law.ufl.edu/facultypub Part of the Law Commons Recommended Citation Jeffrey L. Harrison, Socioeconomics: Choice and Challenges, 41 San Diego L. Rev. 257 (2004), available at http://scholarship.law.ufl.edu/facultypub/181 This Article is brought to you for free and open access by the Faculty Scholarship at UF Law Scholarship Repository. It has been accepted for inclusion in Faculty Publications by an authorized administrator of UF Law Scholarship Repository. For more information, please contact [email protected]. Socioeconomics: Choice and Challenges JEFFREY L. HARRISON* TABLE OF CONTENTS I. INTRO DUCTION ................................................................................................... 257 II. EXPLAINING CHOICES AND PREFERENCES ........................................................... 257 III. C HA LLENG ES ...................................................................................................... 263 A. M oving from "See, e.g." to "See". .......................................................... 264 B. Socioeconomics as an End ....................................................................... 265 C. A Positive Messagefor Teaching and Beyond .......................................... 267 I. INTRODUCTION -
Data Workflows with Stata and Python
In [1]: from IPython.display import IFrame import ipynb_style from epstata import Stpy import pandas as pd from itertools import combinations from importlib import reload In [2]: reload(ipynb_style) ipynb_style.clean() #ipynb_style.presentation() #ipynb_style.pres2() Out[2]: Data Workflows in Stata and Python (http://www.stata.com) (https://www.python.org) Data Workflows in Stata and Python Dejan Pavlic, Education Policy Research Initiative, University of Ottawa Stephen Childs (presenter), Office of Institutional Analysis, University of Calgary (http://ucalgary.ca) (http://uottawa.ca/en) (http://socialsciences.uottawa.ca/irpe- epri/eng/index.asp) Introduction About this talk Objectives know what Python is and what advantages it has know how Python can work with Stata Please save questions for the end. Or feel free to ask me today or after the conference. Outline Introduction Overall Motivation About Python Building Blocks Running Stata from Python Pandas Python language features Workflows ETL/Data Cleaning Stata code generation Processing Stata output About Me Started using Stata in grad school (2006). Using Python for about 3 years. Post-Secondary Education sector University of Calgary - Institutional Analysis (https://oia.ucalgary.ca/Contact) Education Policy Research Initiative (http://socialsciences.uottawa.ca/irpe-epri/eng/index.asp) - University of Ottawa (a Stata shop) Motivation Python is becoming very popular in the data world. Python skills are widely applicable. Python is powerful and flexible and will help you get more done, -
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 .......................................... -
Education and Income Inequality: a Meta-Regression Analysis
Education and Income Inequality: A Meta-Regression Analysis Abdul Jabbar Abdullah* Hristos Doucouliagos Elizabeth Manning - FIRST DRAFT - Please do not quote without permission from the authors September 2011 Abstract This paper revisits the literature that investigates the effects of education on inequality. Specifically, the paper provides a comprehensive quantitative review of the extant econometrics literature through a meta-regression analysis of 64 empirical studies that collectively report 868 estimates of the effects of education on inequality. We find that education affects the two tails of the distribution of incomes; it reduces the income share of top earners and increases the share of the bottom earners, but has no effect on the share of the middle class. Inequality in education widens income inequality. Education has a larger negative effect on inequality in Africa. The heterogeneity in reported estimates can be largely explained by differences in the specification of the econometric model. JEL Codes: I24, C01 Keywords: Education, inequality, meta-regression analysis Number of words: 12,683 * Corresponding author. Abdullah: Universiti Teknologi Mara Sarawak Campus, Malaysia and PhD Candidate Deakin University, [email protected] Doucouliagos: School of Accounting, Economic and Finance, Deakin University, [email protected] Manning: School of Accounting, Economic and Finance, Deakin University, [email protected] 1 Education and Income Inequality: A Meta-Regression Analysis 1. Introduction Income inequality is a critically important social, political and economic issue in the study of economic development. Inequality can affect economic growth and development and it can impact upon political stability and class and ethnic tensions.1 The empirical literature identifies various factors that shape inequality, such as urbanization, the level of development, political regime, government intervention and land inequality. -
Introduction to Stata
Introduction to Stata Christopher F Baum Faculty Micro Resource Center Boston College August 2011 Christopher F Baum (Boston College FMRC) Introduction to Stata August 2011 1 / 157 Strengths of Stata What is Stata? Overview of the Stata environment Stata is a full-featured statistical programming language for Windows, Mac OS X, Unix and Linux. It can be considered a “stat package,” like SAS, SPSS, RATS, or eViews. Stata is available in several versions: Stata/IC (the standard version), Stata/SE (an extended version) and Stata/MP (for multiprocessing). The major difference between the versions is the number of variables allowed in memory, which is limited to 2,047 in standard Stata/IC, but can be much larger in Stata/SE or Stata/MP. The number of observations in any version is limited only by memory. Christopher F Baum (Boston College FMRC) Introduction to Stata August 2011 2 / 157 Strengths of Stata What is Stata? Stata/SE relaxes the Stata/IC constraint on the number of variables, while Stata/MP is the multiprocessor version, capable of utilizing 2, 4, 8... processors available on a single computer. Stata/IC will meet most users’ needs; if you have access to Stata/SE or Stata/MP, you can use that program to create a subset of a large survey dataset with fewer than 2,047 variables. Stata runs on all 64-bit operating systems, and can access larger datasets on a 64-bit OS, which can address a larger memory space. All versions of Stata provide the full set of features and commands: there are no special add-ons or ‘toolboxes’. -
Statistical Software
Statistical Software A. Grant Schissler1;2;∗ Hung Nguyen1;3 Tin Nguyen1;3 Juli Petereit1;4 Vincent Gardeux5 Keywords: statistical software, open source, Big Data, visualization, parallel computing, machine learning, Bayesian modeling Abstract Abstract: This article discusses selected statistical software, aiming to help readers find the right tool for their needs. We categorize software into three classes: Statisti- cal Packages, Analysis Packages with Statistical Libraries, and General Programming Languages with Statistical Libraries. Statistical and analysis packages often provide interactive, easy-to-use workflows while general programming languages are built for speed and optimization. We emphasize each software's defining characteristics and discuss trends in popularity. The concluding sections muse on the future of statistical software including the impact of Big Data and the advantages of open-source languages. This article discusses selected statistical software, aiming to help readers find the right tool for their needs (not provide an exhaustive list). Also, we acknowledge our experiences bias the discussion towards software employed in scholarly work. Throughout, we emphasize the software's capacity to analyze large, complex data sets (\Big Data"). The concluding sections muse on the future of statistical software. To aid in the discussion, we classify software into three groups: (1) Statistical Packages, (2) Analysis Packages with Statistical Libraries, and (3) General Programming Languages with Statistical Libraries. This structure -
The Use of PSPP Software in Learning Statistics. European Journal of Educational Research, 8(4), 1127-1136
Research Article doi: 10.12973/eu-jer.8.4.1127 European Journal of Educational Research Volume 8, Issue 4, 1127 - 1136. ISSN: 2165-8714 http://www.eu-jer.com/ The Use of PSPP Software in Learning Statistics Minerva Sto.-Tomas Darin Jan Tindowen* Marie Jean Mendezabal University of Saint Louis, PHILIPPINES University of Saint Louis, PHILIPPINES University of Saint Louis, PHILIPPINES Pyrene Quilang Erovita Teresita Agustin University of Saint Louis, PHILIPPINES University of Saint Louis, PHILIPPINES Received: July 8, 2019 ▪ Revised: August 31, 2019 ▪ Accepted: October 4, 2019 Abstract: This descriptive and correlational study investigated the effects of using PSPP in learning Statistics on students’ attitudes and performance. The respondents of the study were 200 Grade 11 Senior High School students who were enrolled in Probability and Statistics subject during the Second Semester of School Year 2018-2019. The respondents were randomly selected from those classes across the different academic strands that used PSPP in their Probability and Statistics subject through stratified random sampling. The results revealed that the students have favorable attitudes towards learning Statistics with the use of the PSPP software. The students became more interested and engaged in their learning of statistics which resulted to an improved academic performance. Keywords: Probability and statistics, attitude, PSPP software, academic performance, technology. To cite this article: Sto,-Tomas, M., Tindowen, D. J, Mendezabal, M. J., Quilang, P. & Agustin, E. T. (2019). The use of PSPP software in learning statistics. European Journal of Educational Research, 8(4), 1127-1136. http://doi.org/10.12973/eu-jer.8.4.1127 Introduction The rapid development of technology has brought remarkable changes in the modern society and in all aspects of life such as in politics, trade and commerce, and education. -
Econsoc 10-3 | Economic Sociology Of
economic sociology_the european electronic newsletter Volume 10, Number 3| July 2009 10.3 Editor Andrea Mennicken, London School of Economics and Political Science Book Review Editor Brooke Harrington, Max Planck Institute for the Study of Societies Editorial Board Patrik Aspers, Max Planck Institute for the Study of Societies, Cologne, and Stockholm University Jens Beckert, Max Planck Institute for the Study of Societies, Cologne Johan Heilbron, Centre de Sociologie Européenne, Paris Richard Swedberg, Cornell University, Ithaca Table of Contents Note from the editor_2 Law in Economy and Society: Introductory Comments by Laura Ford and Richard Swedberg_3 The Economy of Legal Practice as a Symbolic Market by Yves Dezalay and Bryant G. Garth_8 Semantic Legal Ordering: Property and its Social Effects by Laura Ford_14 The Legal Constitution of Market Society: Probing the Economic Sociology of Law by Sabine Frerichs_20 Intersections between Economic Sociology and Law: Interview with Gunther Teubner_26 Economic Sociology in France: Interview with Philippe Steiner_29 Book Reviews_34 PhD Projects_39 http://econsoc.mpifg.de Note from the editor 2 Note from the editor Dear reader, interview, Steiner discusses the state of the art of eco- nomic sociology in France and gives insight into his recent This issue of the Newsletter focuses on intersections be- work on the market for human body organs. From No- tween economic sociology and law. The “economic sociol- vember 2009, Philippe Steiner will take over the editorship ogy of law” is a field of study that, as Laura Ford and Rich- of the Newsletter with associate editors Sidonie Naulin and ard Swedberg argue in the introductory essay in this News- Nicolas Milicet (Université Paris-Sorbonne).