Previous Teaching

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Course: BAYESIAN ECONOMETRICS – Doctoral Program in Business Economics
Professor: Hedibert Freitas Lopes – www.hedibert.org

Syllabus

Objective

The end of the course goal is to allow the student to critically decide between a Bayesian, a frequentist or Bayesian-frequentist compromise when facing real world problems in the fields of micro-econometrics, macro-econometrics, marketing and finance.  With this end in mind, we will visit well known Bayesian issues, such as prior specification and model comparison and model averaging, but also study regularization, “small n, large p” issues, Bayesian statistical learning (additive regression trees) and large-scale factor models.

Course description

Basic ingredients: prior, posterior, and predictive distributions, sequential Bayes, conjugate analysis, exchangeability, principles of data reduction and decision theory.  Model criticism: Bayes factor, computing marginal likelihoods, Savage-Dickey ratio, reversible jump MCMC, Bayesian model averaging and deviance information criterion.  Modern computation via (Markov chain) Monte Carlo methods: Monte Carlo integration, sampling-importance resampling, Gibbs sampler, Metropolis-Hastings algorithms.  Mixture models, Hierarchical models, Bayesian regularization, Instrumental variables modeling, Large-scale (sparse) factor modeling, Bayesian additive regression trees (BART) and related topics, Dynamic models, Sequential Monte Carlo algorithms, Bayesian methods in microeconometrics, macroeconometrics, marketing and finance

Course notes (+ R code & references)

Miscellaneous

 

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    Course: ECONOMETRICS III – Doctoral Program in Business Economics
    Professor: Hedibert Freitas Lopes – www.hedibert.org

    Syllabus

    Objective

    The main goal of the course is to make the student familiar with and able to implement univariate and multivariate time series models by using both frequentist and Bayesian approaches. All classroom examples and implementations as well as projects will be carried out by the open-source statistical software R.

    Course description

    Brief review of frequentist inference followed by the introduction of key ingredients of Bayesian inference, model selection and criticism. An introduction to the main Monte Carlo methods for Bayesian inference: MC integration, resampling, MCMC and sequential MC. Univariate time series models, including AR(F)IMA models, state-space models, Markov switching models, GARCH and stochastic volatility models. Multivariate time series models, including Bayesian VARs and factor-augmented VARs, dynamic factor models, time-varying covariance models.

    Bibliography

    Teaching assistant: Paloma Uribe (paloma dot uribe at gmail dot com)

    Office hours: Thursdays from 1pm to 2pm (Room 604)

    Day-to-day announcements

    Homework assignments & Exams

    April, 18th to 24th 2017:

    April, 25th to 30th 2017:

    May, 1st to May 7th 2017:

    May 8th to May 15th 2017:

    This week we started talking about two relatively simple and well-known time series models with time-varying variances: ARCH(1) and GARCH(1,1) models.  We also considered a standard Bayesian approach to posterior inference regarding the GARCH(1,1) model.  Chapter 3 of Tsay’s (2010) book is good enough as an introduction to the subject, particularly sections 3.4 (ARCH model) and 3.5 (GARCH model).  A few exercises from his book are worth trying to fix most the ideas.  I recommend the following exercises: 3.1, 3.2, 3.3, 3.4, 3.5 and 3.6.

    May, 16th to May 23rd 2017:

    May, 24th to June 5th 2017:

    • AR(1) plus noise – Comparing block move (FFBS) with single move MCMC schemes:  HTML

    June 6th to June 15th 2017:

    COURSE SLIDES AND TEACHING MATERIAL

    PART I: Basic univariate time series

    PART II: Basic Bayes

    PART III: More univariate time series

    1. ARCH/GARCH models
    2. EGARCH, GARCH-M, TGARCH
    3. Bayesian GARCH
    4. Dynamic models (aka state-space models) and stochastic volatility (SV) models

    PART IV: Multivariate time series

    1. Vector autoregressive models
    2. Large BVAR, FAVAR, TVP-BVAR & BFAVAR
    3. Factor models (Standard factor analysis, Spatial dynamic factors, Factor stochastic volatility)
    4. Time-varying covariance models

    Additional material

    1. Bayesian Statistics (a very brief introduction) – Ken Rice, April, 2014
    2. Lopes and Salazar (2006) Bayesian model uncertainty in smooth transition autoregressions, Journal of Time Series Analysis, 27, 99-117.
    3. Huerta and Lopes (2000) Bayesian forecasting and inference in latent structure for the Brazilian industrial production index, Brazilian Review of Econometrics, 20, 1-26.
    4. Kleibergen and Hoek (2000) Bayesian Analysis of ARMA Models. Tinbergen Institute Discussion Paper.
    5. Marriott, Ravishanker, Gelfand and Pai (1995) Bayesian Analysis of ARMA Processes: Complete Sampling Based Inference under Exact Likelihoods. Bayesian Statistics and Econometrics: Essays in honor of Arnold Zellner. Berry, Chaloner and Geweke, eds., John Wiley & sons, 241-254.

     

    R stuff

    Introduction to R (by Paloma Uribe, in Portuguese) 

    Radford Neal’s 13 lectures about R

    McLeod, Yu and Mahdi’s (2012) Time Series Analysis with R

    Disciplina: ECONOMETRIA – Turma 4ECO (Economia)
    Período Letivo: 2016/2
    Professor: Hedibert Freitas Lopes – www.hedibert.org
    Monitora: Paloma Vaissman Uribe – PalomaVU@insper.edu.br

    Programa de Ensino 

    Conteudo das aulas

    1. Apresentacao do curso
    2. Regressao Linear Simples – Parte 1: Minimos Quadrados Ordinarios & R2
    3. Regressao Linear Simples – Parte 2: formas funcionais
    4. Regressao Linear Simples – Parte 3: Suposicoes, propriedades e teste-t
    5. Analise de residuos
    6. Regressao linear multipla
    7. Regressao linear multipla – Vies de omissao de variable
    8. Regressao linear multipla – Teste F Parcial
    9. Heteroscedasticidade
    10. Endogeneidade: variaveis instrumentais (worked examples)
    11. Endogeneidade: estimacao
    12. Endogeneidade: tests
    13. Basic time series  – Codigo R

    Listas de exercicios

    Lista 1 (Regressao linear simples): Wooldridge – 2.2, 2.3, 2.4, 2.5, 2.7, 2.9, 2.11

    Lista 2 (Regressao linear multipla): Wooldridge – 3.3, 3.4, 3.5, 3.7, 3.9, 4.2, 4.3, 4.6, 4.9, 4.11

    Lista 3 (Heteroscedasticidade): Wooldridge – 8.1, 8.2, 8.3, 8.4, 8.5 + refazer os exemplos 8.1 (pg 250-251), 8.2 (pg 252), 8.4 (pg 257) e 8.7 (pg 268-269)

    Lista 4 (Endogeneidade): Wooldridge – 15.1, 15.2, 15.3, 15.7, 15.8 e 15.10 + refazer os exemplos 5.1 (pg 476-477), 5.2 (pg 477-478), 5.3 (pg 480-481) e 5.4 (pg 484-486).

    Lista 5 (Series temporais): Wooldridge – Refazer os exemplos 10.1, 10.2, 10.3, 10.4, 10.7, 10.9, 11.3, 11.4, 11.5, 11.6 e 11.7 + problemas 11.1, 11.2, 11.3, 11.4 e 11.5

    Atividade 2

    Dados por escola do ENEM2015 – Analise exploratoria dos dados  –  Codigo R

    Econometria em R

    1. Tutorial de R: aula 1
    2. Tutorial de R: aula 2
    3. Tutorial de R: regressao
    4. Tutorial de R: R2
    5. Introducao ao uso do R (Paloma Uribe)
    6. Using R for Introductory Econometrics (Florian Heiss)
    7. Econometric and time series modeling using R (Cribari-Neto)
    8. Introduction to programmingEconometrics with R (Bruno Rodrigues)
    9. Econometrics in R: Past, Present, and Future (Achim Zeileis & Roger Koenker)
    10. CRAN Task View: Econometrics (Achim Zeileis)
    11. R-Econometrics – Learn R for applied economics in a comprehensive way

    Econometria em outras linguagens/pacotes

    1. PYTHON: Introductory Econometrics – Jeffrey M. Wooldridge: Capítulos 2 ao 8 usando PYTHON
    2. Kevin Sheppard’s Python for Econometrics
    3. STATA: Introductory Econometrics – Jeffrey M. Wooldridge: Capítulos 2 ao 18 usando STATA
    4. Statistical Analysis in R, MATLAB, SAS, STATA and SPSS

    Mais conjuntos de dados

    Bayesian Statistical Learning: Readings in Statistics and Econometrics

    Organizer: Hedibert Freitas Lopes and Paulo Marques

    Email: hedibertFL at insper.edu.br

    In this Second Readings in Statistics and Econometrics we will study and discuss, through a series of well established papers, the broad topic of Statistical Learning with an emphasis on its natural Bayesian solutions. The 5 lectures and 8 seminars will take place on Fridays between 10am and 12pm from January 29th to April 8th 2016. Paulo and I will give lectures discussing traditional Statistical Learning techniques, alternated with seminars given by the participants on papers presenting Bayesian counterparts to the techniques discussed in the lectures.

    Outline of the meetings (5 lectures and 8 seminars)

    Books

    Papers

    Causality: Readings in Statistics and Econometrics

    Organizer: Hedibert Freitas Lopes

    Email: hedibertFL at insper.edu.br

    In this First Readings in Statistics and Econometrics we will study and discuss, through a series of well established papers, the broad topic of causality.  The lectures are held at INSPER on Tuesdays, from 7:30am to 9:30am, from September 29th to December 1st, 2015, at classroom Paulo Renato de Souza, 2nd floor.

    Annotated bibliographyHere you will find links to textbooks and edited books, special issues, articles with discussion and web material: slides of lectures, discussion of causality, video lectures and more (in chronological order).

    Annotated bibliographyOnly articles and book chapters (in alphabetical order).

    Outline of the lectures

        • September 29th – Hedibert Lopes – INSPER
          Haavelmo (1943) The statistical implications of a system of simultaneous equations. Econometrica, 11, 1-12.
          slides of the lecture
        • October 6th – Hedibert Lopes – INSPER
          Rubin (1974) Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 56, 688-701.
          slides of the lecture
        • October 13th – Andre Yoshizumi, IME/USP
          Holland (1986) Statistics and causal inference (with discussion). JASA, 81, 945-970.
          slides of the lecture
        • October 20th – Paloma Uribe, IME/USP
          Pearl (1995) Causal diagrams for empirical research (with discussion). Biometrika, 82, 669-710.
          slides of the lecture + slides of Joao M.P.De Mello’s talk
        • November 3rd – Sergio Firpo, EESP/FGV
          Angrist, Imbens and Rubin (1996) Identi cation of causal effects using instrumental variables (with discussion). JASA, 91, 444-472.
          slides of the lecture
        • November 10th – Julio Trecenti, IME/USP
          Dawid (2000) Causal inference without counterfactuals (with discussion). JASA, 95, 407-424.
          slides of the lecture
        • November 24th – Manasses Nobrega, UFABC
          Vansteelandt and Goetghebeur (2003) Causal inference with generalized structural mean
          models. JRSS-B, 65, 817-835.
        • December 1st – Hedibert Lopes – INSPER
          Heckman and Pinto (2015) Causal analysis after Haavelmo. Econometric Theory, 31,115-151.
          slides of the lecture

     

    Books & special issues

    Articles with discussion

    From the web

    Disciplina: ECONOMETRIA – Turma 4ECO (Economia)
    Período Letivo: 2014/1

    Professor: Hedibert Freitas Lopes – www.hedibert.org

    Objetivo: Apresentar uma abordagem introdutória a Econometria dando ênfase tanto à base estatística quanto a aplicações econômicas.  Será discutido, em detalhes, o significado e as implicações das suposições do modelo linear geral. Ainda, serão descritos e aplicados testes de violações das hipóteses do modelo linear geral, bem como serão apresentados e aplicados estimadores alternativos ao de mínimos quadrados ordinários (MQO). Ao final desse curso, o aluno deverá ser capaz de utilizar técnicas estatísticas adequadas para mensurar quantidades de interesse e realizar previsões.

    Programa de Ensino 

    12/02/2014: Apresentacao do curso + Primeiro exemplo

    14/02/2014: Regressao Linear Simples – Parte 1: Minimos Quadrados Ordinarios & R2

    19/02/2014: Regressao Linear Simples – Parte 2: formas funcionais

    21/02/2014: Regressao Linear Simples – Parte 3: Suposicoes, propriedades e teste-t

    26/02/2014: Regressao Linear Multipla – Parte 1: Estimacao

    28/02/2014: Regressao Linear Multipla – Parte 2: R2 ajustado

    07/03/2014: Regressao Linear Multipla – Parte 3: Suposicoes e propriedades

        • Data on monthly earnings, education, several demographic variables, and IQ scores for 935 men in 1980. (Dataset)  (Codigo R)
        • Data on 4,137 US college students.(Dataset)  (Codigo R)

    12/03/2014: Regressao Linear Multipla – Parte 4: Inferencia

    14/03/2014: Regressao Linear Multipla – Parte 5: Interacao e funcao quadratica

    19,21&26/03/2014: Regressao Linear Multipla – Parte 6: Informacao qualitativa atraves de variaveis dummy

    11/04/2014: Regressao Linear Multipla – Parte 7: Teste F parcial

    16/04/2014: Regressao Linear Multipla – Parte 8: Teoria assintotica

    23/04/2014: Regressao Linear Multipla – Parte 9: Teste do multiplicador de Lagrange

        • R code for hprice1 example
        • Sawyer (2002) The Method of Lagrange Multipliers.
        • Buse (1982) The Likelihood Ratio, Wald, and Lagrange Multiplier Tests: An Expository Note.  The American Statistician, Vol. 36, No. 3, Part 1 (Aug., 1982), pp. 153-157.
        • Engle (1983) Wald, Likelihood Ratio, and Lagrange Multiplier Tests in Econometrics.  In Intriligator and Griliches. Handbook of Econometrics II. Elsevier. pp. 796–801.

    25/04/2014: Regressao Linear Multipla – Parte 10: Regression specification error test  (RESET)

    30/04/2014: Real-time analysis of three datasets

    07-09/05/2014: Regressao Linear Multipla – Parte 11: Homocedasticidade

    14/05/2014: Trabalho em grupo

    16 a 30/05/2014:  Endogeneidade & Equacoes simultaneas

    Listas de exercicios + gabaritos

    Conjuntos de dados

     

    Disciplina: ANALISE MULTIVARIADA (MPA)

    Período Letivo: 2015/1

    Professor: Hedibert Freitas Lopes – www.hedibert.org

    Monitor: Leandro Augusto Ferreira

    Objetivo: O objetivo do curso é apresentar os conceitos e métodos de análise multivariada de dados, aplicando-os a dados reais e interpretando os resultados de forma prática. No curso de análise multivariada são utilizados conceitos de estatística básica e inferência, com ênfase na resolução de problemas reais e interpretação dos resultados.  Na maioria dos estudos, a complexidade dos fenômenos estudados faz com seja necessário coletar informações sobre um conjunto de variáveis. A análise multivariada permite o estudo simultâneo de um conjunto de variáveis, aproveitando a estrutura de correlação existente entre as mesmas.  Nesta disciplina são apresentadas técnicas de análise de dados quantitativos e qualitativos, discutindo aplicações nas áreas de marketing, operações, recursos humanos e finanças.

    Programa de Ensino 

    03/02/2015: Análise Exploratória de Dados Multivariados

    06/02/2015: Inferencia Multivariada – MANOVA

    10/02/2015: Análise de Componentes Principais

    24/02/2015: Análise Fatorial – parte 1 + Análise Fatorial – parte 2

    03/03/2015: Regressao logistica + Analise discriminante

    10/03/2015: Correlacao canonica

    17/03/2015: Cluster analysis

    24/03/2015: Trabalho em sala de aula (terminado em casa) 31/03/2015: Correspondence analysis & multidimensional scaling

    10/04/2015: Structural equation modeling

     

    Conjunto de dados:

    Course: ECONOMETRICS III – Doctoral Program in Business Economics
    Professor: Hedibert Freitas Lopes – www.hedibert.org

    Syllabus

    Objective

    The main goal of the course is to make the student familiar with and able to implement univariate and multivariate time series models by using both frequentist and Bayesian approaches. All classroom examples and implementations as well as projects will be carried out by the open-source statistical software R.

    Course description

    Brief review of frequentist inference followed by the introduction of key ingredients of Bayesian inference, model selection and criticism. An introduction to the main Monte Carlo methods for Bayesian inference: MC integration, resampling, MCMC and sequential MC. Univariate time series models, including AR(F)IMA models, state-space models, Markov switching models, GARCH and stochastic volatility models. Multivariate time series models, including Bayesian VARs and factor-augmented VARs, dynamic factor models, time-varying covariance models.

    Bibliography

     

    Course material

    1. Bayesian ingredients
      • GL2006: Examples 2.1, 2.4, 2.5, section 2.3.2, problems 2.1, 2.2, 2.5, 2.8a, 2.10 and 2.11.
    2. Monte Carlo methods
    3. Markov chain Monte Carlo methods
    4. Autoregressive (AR) models and moving average (MA) models
      • Tsay (2010): Sections 2.1-2.6, sections 2.7 and 2.11, and sections 3.1-3.8
    5. Unit-root nonstationarity and long-memory processes
    6. Seasonal models
    7. ARCH/GARCH models (EGARCH, GARCH-M, TGARCH) (Bayesian GARCH)
    8. Dynamic models (aka state-space models) and stochastic volatility (SV) models
    9. Homework (due June 1st)  (solution)
    10. Vector autoregressive models
    11. Large BVAR, FAVAR, TVP-BVAR & BFAVAR
    12. Sequential Monte Carlo methods
    13. Factor models (Standard factor analysis, Spatial dynamic factors, Factor stochastic volatility)
    14. Time-varying covariance models

    Additional material

    1. Bayesian Statistics (a very brief introduction) – Ken Rice, April, 2014
    2. Lopes and Salazar (2006) Bayesian model uncertainty in smooth transition autoregressions, Journal of Time Series Analysis, 27, 99-117.
    3. Huerta and Lopes (2000) Bayesian forecasting and inference in latent structure for the Brazilian industrial production index, Brazilian Review of Econometrics, 20, 1-26.
    4. Kleibergen and Hoek (2000) Bayesian Analysis of ARMA Models. Tinbergen Institute Discussion Paper.
    5. Marriott, Ravishanker, Gelfand and Pai (1995) Bayesian Analysis of ARMA Processes: Complete Sampling Based Inference under Exact Likelihoods. Bayesian Statistics and Econometrics: Essays in honor of Arnold Zellner. Berry, Chaloner and Geweke, eds., John Wiley & sons, 241-254.

     

    Disciplina: ECONOMETRIA AVANCADA – ANALISE DE SERIES TEMPORAIS

    Período Letivo: 2015/1

    Professor: Hedibert Freitas Lopes – www.hedibert.org

    Monitor: Paloma Vaissman Uribe

    Objetivo: O objetivo do curso é apresentar os conceitos e métodos de análise multivariada de dados, aplicando-os a dados reais e interpretando os resultados de forma prática. No curso de análise multivariada são utilizados conceitos de estatística básica e inferência, com ênfase na resolução de problemas reais e interpretação dos resultados.  Na maioria dos estudos, a complexidade dos fenômenos estudados faz com seja necessário coletar informações sobre um conjunto de variáveis. A análise multivariada permite o estudo simultâneo de um conjunto de variáveis, aproveitando a estrutura de correlação existente entre as mesmas.  Nesta disciplina são apresentadas técnicas de análise de dados quantitativos e qualitativos, discutindo aplicações nas áreas de marketing, operações, recursos humanos e finanças.

    Programa de Ensino 

    Introducao ao R by Paloma Uribe (Material apresentado na 1a monitoria) 

    Lista de exercicios

    Trabalhos em grupo

    Prova intermediaria: Prova (solucao)

    Prova final: Solucao

    Notas de aula

    Codigo R

    Textos complementares

    Conjuntos de dados

    Alguns sites interessantes para o curso

    Business Statistics 41000-81/82

    Spring Quarter 2013

    Chicago Booth

    Instructor: Hedibert Freitas Lopes, PhD

    Office: Harper Center Suite 357

    Phone: +1 (773) 834-5458

    Email: hedibertFL@insper.edu.br

    Webpage: http://www.hedibert.org

    Teaching Assistant

    Samir Warty (spwarty@ChicagoBooth.edu)

    Office hours: Sundays 3pm-4:30pm (April 28th and June 9th: 3pm-5pm)

    Location: Gleacher 203 (April 28th and June 9th: Gleacher 204)

    Course notes

    Course syllabus

    Course notes (2 per page) (3 per page)

    Old exams

    Homework assignments

    Midterm and final exams

        • 41000-81: Midterm – 04/29/2013 – 6:00pm-8:00pm
        • 41000-81: Final – 06/10/2013 – 6:30pm-9:30pm
        • 41000-82: Midterm – 04/30/2013 – 6:00pm-8:00pm
        • 41000-82: Final – 06/11/2013 – 6:30pm-9:30pm

    Additional class material

    Class 8: May 20th and 21st

    Class 7: May 13th and 14th

    Class 5: April 29th and 39th

    Class 4: April 22nd and 23rd

    Class 3: April 15th and 16th

    Class 2: April 8th and 9th

    Class 1: April 1st and 2nd

    Data sets

    Disciplina: ECONOMETRIA – Turma 4ECO (Economia)
    Período Letivo: 2016/1
    Professor: Hedibert Freitas Lopes – www.hedibert.org
    Monitora: Paloma Vaissman Uribe – PalomaVU@insper.edu.br

    Programa de Ensino 

    Conteudo das aulas

    1. Apresentacao do curso
    2. Regressao Linear Simples – Parte 1: Minimos Quadrados Ordinarios & R2
    3. Regressao Linear Simples – Parte 2: formas funcionais
    4. Regressao Linear Simples – Parte 3: Suposicoes, propriedades e teste-t
    5. Analise de residuos
    6. Atividade 1 – Dados da PNAD 2009 + readme.txt + Analise exploratoria dos dados
      • Madalozzo and Mauriz (2012) Does investing in education reduce the gender wage gap? A Brazilian population study. Population Review, Volume 51, Number 2, pp. 59-84.
      • Bertrand, Kamenica and Pan (2015) Gender Identity and relative income within households.  Quarterly Journal of Economics.
      • USA data: Median usual weekly earnings (second quartile), Employed full time, Wage and salary workers.  U.S. Bureau of Labor Statistics, United States Department of Labor.
      • pnad2009.R: Codigo R para os dados brasileiros.
      • earnings.R: Codigo R para os dados americanos.
    7. Regressao linear multipla
    8. Regressao linear multipla – Teste F Parcial + Teste RESET
    9. Heteroscedasticidade
    10. Dados para atividade 2: sleep75.csv
    11. Endogeneidade: variaveis instrumentais (worked examples)
    12. Endogeneidade: estimacao
    13. Endogeneidade: tests
    14. Basic time series  – Codigo R

    Listas de exercicios

    1. Lista 1
    2. Lista 2
    3. Lista 3
    4. Lista 4

    Econometria em R

    1. Introducao ao uso do R (Paloma Uribe)
    2. Using R for Introductory Econometrics (Florian Heiss)
    3. Econometric and time series modeling using R (Cribari-Neto)
    4. Introduction to programmingEconometrics with R (Bruno Rodrigues)
    5. Econometrics in R: Past, Present, and Future (Achim Zeileis & Roger Koenker)
    6. CRAN Task View: Econometrics (Achim Zeileis)
    7. R-Econometrics – Learn R for applied economics in a comprehensive way

     

    Econometria em outras linguagens/pacotes

    1. PYTHON: Introductory Econometrics – Jeffrey M. Wooldridge: Capítulos 2 ao 8 usando PYTHON
    2. Kevin Sheppard’s Python for Econometrics
    3. STATA: Introductory Econometrics – Jeffrey M. Wooldridge: Capítulos 2 ao 18 usando STATA
    4. Statistical Analysis in R, MATLAB, SAS, STATA and SPSS


    Mais conjuntos de dados

    Bayesian Econometrics 41913-01 Spring 2013

    Hedibert Freitas Lopes

        • Associate Professor of Econometrics and Statistics
        • The University of Chicago Booth School of Business
        • 5807 Woodlawn Avenue Chicago, Illinois 60637
        • Phone: +1 773 834-5458
        • Fax: +1 773 702-0458
        • Email: hlopes@ChicagoBooth.edu
        • Office hours: by appointment

    Course notes + R code + references

    Midterm exam (take-home)

    Homework assignments

    Miscellaneous

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