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Political Instability and Economic Growth
Political Instability and Economic Growth The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Alesina, Alberto, Sule Ozler, Nouriel Roubini, and Phillip Swagel. 1996. Political instability and economic growth. Journal of Economic Growth 1(2): 189-211. Published Version doi:10.1007/BF00138862 Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:4553024 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA NBER WORKING PAPER SERIES POLITICAL INSTABILITY AND ECONOMIC GROWTH Albert0 Alesina Sule 0zler Nouriel Roubini Phillip Swagel Working Paper No. 4173 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 September 1992 We are grateful to John Londregan for generously sharing his data set and for useful conversations, Kala Krishna for letting us use her PC, Jennifer Widner for much needed help in understanding political events in Africa, Gary King for providing some data, Robert Barro, Rudi Dornbusch, John Helliwell, Ed Learner, Nancy Marion, Ronald Rogowski, Howard Rosenthal, and participants in seminars at University of Maryland, Pennsylvania, Princeton, Utah, UCLA and NBER for very useful comments and Gina Raimondo and Jane Willis for excellent research assistance. For financial support we thank National Fellows Program at Hoover Institution, Sloan, UCLA Academic Senate and Yale Social Science Research Fund, and especially IRIS at University of Maryland. This paper is part of NBER’s research programs in Growth, International Finance and Macroeconomics, and International Trade and Investment. -
How Plural Is the Plural Economy of Bolivia? Constructing a Plural Economy Indicator with Fuzzy Sets
How plural is the plural economy of Bolivia? Constructing a plural economy indicator with fuzzy sets Rolando Gonzales Martínez* * The author thanks the editorial review and the comments from three anonymous referees. The usual disclaimer holds. 10 ROLANDO GONZALES MARTÍNEZ ABSTRACT An indicator that measures the compliance with the constitutional principles of a plural economy is proposed. An inference system based on fuzzy sets was used to calculate the indicator. The fuzzy system summarizes the principles of income redistribution and environmental sustainability into an overall measure of plural economy that allows to objectively judge the change towards a plural economy in Bolivia. JEL Classification: C02, P40 Keywords: Plural economy indicator, fuzzy sets Revista de Análisis, Enero - Junio 2012, Volumen N° 16, pp. 9-29 HOW PLURAL IS THE PLURAL ECONOMY OF BOLIVIA? CONSTRUCTING A PLURAL ECONOMY INDICATOR WITH FUZZY SETS 11 ¿Cuán plural es la economía plural de Bolivia? Construyendo un indicador de economía plural con conjuntos difusos RESUMEN Se propone un indicador que mide el cumplimiento de los principios constitucionales de una economía plural. Para calcular el indicador se usó un sistema de inferencia basado en conjuntos difusos. El sistema difuso resume los principios de la redistribución del ingreso y la sostenibilidad ambiental en una medida global de economía plural que permite juzgar objetivamente el cambio hacia una economía plural en Bolivia. Clasificación JEL: C02, P40 Palabras clave: Indicador de economía plural, conjuntos difusos Revista de Análisis, Enero - Junio 2012, Volumen N° 16, pp. 9-29 12 ROLANDO GONZALES MARTÍNEZ I. Introduction As part of a series of changes of the economic model of Bolivia, the government of this country enacted in 2009 a new State Constitution with the principles of a plural economy. -
R G M B.Ec. M.Sc. Ph.D
Rolando Kristiansand, Norway www.bayesgroup.org Gonzales Martinez +47 41224721 [email protected] JOBS & AWARDS M.Sc. B.Ec. Applied Ph.D. Economics candidate Statistics Jobs Events Awards Del Valle University University of Alcalá Universitetet i Agder 2018 Bolivia, 2000-2004 Spain, 2009-2010 Norway, 2018- 1/2018 Ph.D scholarship 8/2017 International Consultant, OPHI OTHER EDUCATION First prize, International Young 12/2016 J-PAL Course on Evaluating Social Programs Macro-prudential Policies Statistician Prize, IAOS Massachusetts Institute of Technology - MIT International Monetary Fund (IMF) 5/2016 Deputy manager, BCP bank Systematic Literature Reviews and Meta-analysis Theorizing and Theory Building Campbell foundation - Vrije Universiteit Halmstad University (HH, Sweden) 1/2016 Consultant, UNFPA Bayesian Modeling, Inference and Prediction Multidimensional Poverty Analysis University of Reading OPHI, Oxford University Researcher, data analyst & SKILLS AND TECHNOLOGIES 1/2015 project coordinator, PEP Academic Research & publishing Financial & Risk analysis 12 years of experience making economic/nancial analysis 3/2014 Director, BayesGroup.org and building mathemati- Consulting cal/statistical/econometric models for the government, private organizations and non- prot NGOs Research & project Mathematical 1/2013 modeling coordinator, UNFPA LANGUAGES 04/2012 First award, BCG competition Spanish (native) Statistical analysis English (TOEFL: 106) 9/2011 Financial Economist, UDAPE MatLab Macro-economic R, RStudio 11/2010 Currently, I work mainly analyst, MEFP Stata, SPSS with MatLab and R . SQL, SAS When needed, I use 3/2010 First award, BCB competition Eviews Stata, SAS or Eviews. I started working in LaTex, WinBugs Python recently. 8/2009 M.Sc. scholarship Python, Spyder RECENT PUBLICATIONS Disjunction between universality and targeting of social policies: Demographic opportunities for depolarized de- velopment in Paraguay. -
End-User Probabilistic Programming
End-User Probabilistic Programming Judith Borghouts, Andrew D. Gordon, Advait Sarkar, and Neil Toronto Microsoft Research Abstract. Probabilistic programming aims to help users make deci- sions under uncertainty. The user writes code representing a probabilistic model, and receives outcomes as distributions or summary statistics. We consider probabilistic programming for end-users, in particular spread- sheet users, estimated to number in tens to hundreds of millions. We examine the sources of uncertainty actually encountered by spreadsheet users, and their coping mechanisms, via an interview study. We examine spreadsheet-based interfaces and technology to help reason under uncer- tainty, via probabilistic and other means. We show how uncertain values can propagate uncertainty through spreadsheets, and how sheet-defined functions can be applied to handle uncertainty. Hence, we draw conclu- sions about the promise and limitations of probabilistic programming for end-users. 1 Introduction In this paper, we discuss the potential of bringing together two rather distinct approaches to decision making under uncertainty: spreadsheets and probabilistic programming. We start by introducing these two approaches. 1.1 Background: Spreadsheets and End-User Programming The spreadsheet is the first \killer app" of the personal computer era, starting in 1979 with Dan Bricklin and Bob Frankston's VisiCalc for the Apple II [15]. The primary interface of a spreadsheet|then and now, four decades later|is the grid, a two-dimensional array of cells. Each cell may hold a literal data value, or a formula that computes a data value (and may depend on the data in other cells, which may themselves be computed by formulas). -
Bayesian Data Analysis and Probabilistic Programming
Probabilistic programming and Stan mc-stan.org Outline What is probabilistic programming Stan now Stan in the future A bit about other software The next wave of probabilistic programming Stan even wider adoption including wider use in companies simulators (likelihood free) autonomus agents (reinforcmenet learning) deep learning Probabilistic programming Probabilistic programming framework BUGS started revolution in statistical analysis in early 1990’s allowed easier use of elaborate models by domain experts was widely adopted in many fields of science Probabilistic programming Probabilistic programming framework BUGS started revolution in statistical analysis in early 1990’s allowed easier use of elaborate models by domain experts was widely adopted in many fields of science The next wave of probabilistic programming Stan even wider adoption including wider use in companies simulators (likelihood free) autonomus agents (reinforcmenet learning) deep learning Stan Tens of thousands of users, 100+ contributors, 50+ R packages building on Stan Commercial and scientific users in e.g. ecology, pharmacometrics, physics, political science, finance and econometrics, professional sports, real estate Used in scientific breakthroughs: LIGO gravitational wave discovery (2017 Nobel Prize in Physics) and counting black holes Used in hundreds of companies: Facebook, Amazon, Google, Novartis, Astra Zeneca, Zalando, Smartly.io, Reaktor, ... StanCon 2018 organized in Helsinki in 29-31 August http://mc-stan.org/events/stancon2018Helsinki/ Probabilistic program -
Software Packages – Overview There Are Many Software Packages Available for Performing Statistical Analyses. the Ones Highlig
APPENDIX C SOFTWARE OPTIONS Software Packages – Overview There are many software packages available for performing statistical analyses. The ones highlighted here are selected because they provide comprehensive statistical design and analysis tools and a user friendly interface. SAS SAS is arguably one of the most comprehensive packages in terms of statistical analyses. However, the individual license cost is expensive. Primarily, SAS is a macro based scripting language, making it challenging for new users. However, it provides a comprehensive analysis ranging from the simple t-test to Generalized Linear mixed models. SAS also provides limited Design of Experiments capability. SAS supports factorial designs and optimal designs. However, it does not handle design constraints well. SAS also has a GUI available: SAS Enterprise, however, it has limited capability. SAS is a good program to purchase if your organization conducts advanced statistical analyses or works with very large datasets. R R, similar to SAS, provides a comprehensive analysis toolbox. R is an open source software package that has been approved for use on some DoD computer (contact AFFTC for details). Because R is open source its price is very attractive (free). However, the help guides are not user friendly. R is primarily a scripting software with notation similar to MatLab. R’s GUI: R Commander provides limited DOE capabilities. R is a good language to download if you can get it approved for use and you have analysts with a strong programming background. MatLab w/ Statistical Toolbox MatLab has a statistical toolbox that provides a wide variety of statistical analyses. The analyses range from simple hypotheses test to complex models and the ability to use likelihood maximization. -
Richard C. Zink, Ph.D
Richard C. Zink, Ph.D. SAS Institute, Inc. • SAS Campus Drive • Cary, North Carolina 27513 • Office 919.531.4710 • Mobile 919.397.4238 • Blog: http://blogs.sas.com/content/jmp/author/rizink/ • [email protected] • @rczink SUMMARY Richard C. Zink is Principal Research Statistician Developer in the JMP Life Sciences division at SAS Institute. He is currently a developer for JMP Clinical, an innovative software package designed to streamline the review of clinical trial data. Richard joined SAS in 2011 after eight years in the pharmaceutical industry, where he designed and analyzed clinical trials in diverse therapeutic areas including infectious disease, oncology, and ophthalmology, and participated in US and European drug submissions and FDA advisory committee hearings. Richard is the Publications Officer for the Biopharmaceutical Section of the American Statistical Association, and the Statistics Section Editor for the Therapeutic Innovation & Regulatory Science (formerly Drug Information Journal). He holds a Ph.D. in Biostatistics from the University of North Carolina at Chapel Hill, where he serves as an Adjunct Assistant Professor of Biostatistics. His research interests include data visualization, the analysis of pre- and post-market adverse events, subgroup identification for patients with enhanced treatment response, and the assessment of data integrity in clinical trials. He is author of Risk-Based Monitoring and Fraud Detection in Clinical Trials Using JMP and SAS, and co-editor of Modern Approaches to Clinical Trials Using -
Winbugs for Beginners
WinBUGS for Beginners Gabriela Espino-Hernandez Department of Statistics UBC July 2010 “A knowledge of Bayesian statistics is assumed…” The content of this presentation is mainly based on WinBUGS manual Introduction BUGS 1: “Bayesian inference Using Gibbs Sampling” Project for Bayesian analysis using MCMC methods It is not being further developed WinBUGS 1,2 Stable version Run directly from R and other programs OpenBUGS 3 Currently experimental Run directly from R and other programs Running under Linux as LinBUGS 1 MRC Biostatistics Unit Cambridge, 2 Imperial College School of Medicine at St Mary's, London 3 University of Helsinki, Finland WinBUGS Freely distributed http://www.mrc-bsu.cam.ac.uk/bugs/welcome.shtml Key for unrestricted use http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/WinBUGS14_immortality_key.txt WinBUGS installation also contains: Extensive user manual Examples Control analysis using: Standard windows interface DoodleBUGS: Graphical representation of model A closed form for the posterior distribution is not needed Conditional independence is assumed Improper priors are not allowed Inputs Model code Specify data distributions Specify parameter distributions (priors) Data List / rectangular format Initial values for parameters Load / generate Model specification model { statements to describe model in BUGS language } Multiple statements in a single line or one statement over several lines Comment line is followed by # Types of nodes 1. Stochastic Variables that are given a distribution 2. Deterministic -
Institutions and Agricultural Growth in Bolivia and New Zealanda
Research Programme Consortium on Improving Institutions p g for Pro-Poor Growth INSTITUTIONS AND AGRICULTURAL GROWTH IN BOLIVIA AND NEW ZEALANDA STEVE WIGGINSA DISCUssION PAPER SERIES NUMBER TWENTY-ONE OCTOBER 2008 IPPG A Many thanks to the stimulating and encouraging comments received on this paper from colleagues in IPPG — Paul Hare, Adrian Leftwich, and Kunal Sen. Some of the ideas about Bolivia owe much to the guidance of George Gray, Alexander Schejtman and Carlos Toranzo. The interpretations here are not necessarily those of my helpers, and certainly not whatever omissions and misjudgement remain in this essay. B Overseas Development Institute Paper prepared for the DFID-funded Research Programme, Institutions and Pro-Poor Growth (IPPG). The authors are grateful to DFID for the funding that made this research possible. The views expressed in this paper are entirely those of the author and in no way represent either the official policy of DFID or the policy of any other part of the UK Government. Material published by the IPPG may be reproduced free of charge in any format or medium provided it is reproduced faithfully, not used in a misleading context and properly attributed to the author(s). Copyright exists in all other original material published by members of the Programme and may belong to the author or to the University of Manchester depending on the circumstances of publication. Enquiries should be sent to the editor at the above address. IPPG Programme Office, IDPM, School of Environment & Development University of Manchester, Arthur Lewis Building, 2.023, Oxford Road Manchester M13 9PL; Telephone 0161 306 6438; [email protected] www.ippg.org.uk IPPG SUMMARY This essay compares the experiences of agricultural development and overall development in two similar but very different countries: Bolivia and New Zealand. -
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. -
Stable with Fn.Batch Shape
NumPyro Documentation Uber AI Labs Sep 26, 2021 CONTENTS 1 Getting Started with NumPyro1 2 Pyro Primitives 11 3 Distributions 23 4 Inference 95 5 Effect Handlers 167 6 Contributed Code 177 7 Bayesian Regression Using NumPyro 179 8 Bayesian Hierarchical Linear Regression 205 9 Example: Baseball Batting Average 215 10 Example: Variational Autoencoder 221 11 Example: Neal’s Funnel 225 12 Example: Stochastic Volatility 229 13 Example: ProdLDA with Flax and Haiku 233 14 Automatic rendering of NumPyro models 241 15 Bad posterior geometry and how to deal with it 249 16 Example: Bayesian Models of Annotation 257 17 Example: Enumerate Hidden Markov Model 265 18 Example: CJS Capture-Recapture Model for Ecological Data 273 19 Example: Nested Sampling for Gaussian Shells 281 20 Bayesian Imputation for Missing Values in Discrete Covariates 285 21 Time Series Forecasting 297 22 Ordinal Regression 305 i 23 Bayesian Imputation 311 24 Example: Gaussian Process 323 25 Example: Bayesian Neural Network 329 26 Example: AutoDAIS 333 27 Example: Sparse Regression 339 28 Example: Horseshoe Regression 349 29 Example: Proportion Test 353 30 Example: Generalized Linear Mixed Models 357 31 Example: Hamiltonian Monte Carlo with Energy Conserving Subsampling 361 32 Example: Hidden Markov Model 365 33 Example: Hilbert space approximation for Gaussian processes. 373 34 Example: Predator-Prey Model 389 35 Example: Neural Transport 393 36 Example: MCMC Methods for Tall Data 399 37 Example: Thompson sampling for Bayesian Optimization with GPs 405 38 Indices and tables 413 Python Module Index 415 Index 417 ii CHAPTER ONE GETTING STARTED WITH NUMPYRO Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU. -
JASP: Graphical Statistical Software for Common Statistical Designs
JSS Journal of Statistical Software January 2019, Volume 88, Issue 2. doi: 10.18637/jss.v088.i02 JASP: Graphical Statistical Software for Common Statistical Designs Jonathon Love Ravi Selker Maarten Marsman University of Newcastle University of Amsterdam University of Amsterdam Tahira Jamil Damian Dropmann Josine Verhagen University of Amsterdam University of Amsterdam University of Amsterdam Alexander Ly Quentin F. Gronau Martin Šmíra University of Amsterdam University of Amsterdam Masaryk University Sacha Epskamp Dora Matzke Anneliese Wild University of Amsterdam University of Amsterdam University of Amsterdam Patrick Knight Jeffrey N. Rouder Richard D. Morey University of Amsterdam University of California, Irvine Cardiff University University of Missouri Eric-Jan Wagenmakers University of Amsterdam Abstract This paper introduces JASP, a free graphical software package for basic statistical pro- cedures such as t tests, ANOVAs, linear regression models, and analyses of contingency tables. JASP is open-source and differentiates itself from existing open-source solutions in two ways. First, JASP provides several innovations in user interface design; specifically, results are provided immediately as the user makes changes to options, output is attrac- tive, minimalist, and designed around the principle of progressive disclosure, and analyses can be peer reviewed without requiring a “syntax”. Second, JASP provides some of the recent developments in Bayesian hypothesis testing and Bayesian parameter estimation. The ease with which these relatively complex Bayesian techniques are available in JASP encourages their broader adoption and furthers a more inclusive statistical reporting prac- tice. The JASP analyses are implemented in R and a series of R packages. Keywords: JASP, statistical software, Bayesian inference, graphical user interface, basic statis- tics.