Training Calendar

Stata Econometrics Winter School 2019

Faculty of Economics - University of Porto - CEF.UP 5 days (21st January 2019 - 25th January 2019) Stata Intermediate, Introductory
Delivered by: Miguel Portela (Universidade do Minho, Escola de Economia e Gestão); João Cerejeira (Universidade do Minho, Escola de Economia e Gestão); Anabela Carneiro (Universidade do Porto, Faculdade de Economia); Paulo Guimarães (Universidade do Porto, Faculdade de Economia and Banco de Portugal)
Econometrics, Statistics, Winter School

Overview

Our fifth annual Stata Winter School runs in Oporto between 21-25 January 2019 The Stata Winter School comprises a series of four separate short courses that allows the flexibility to attend one, a combination of or all courses consecutively.

The courses forming the 2019 Stata Winter School are:

  • Day 1: Introduction to Stata
  • Day 2: Data Analysis, Linear Regression, and Spatial Econometrics
  • Day 3: Managing Output Files
  • Day 4: Linear Panel Data Models
  • • Day 5: More Topics in Linear Panel Data Models

Timberlake Portugal and Faculdade de Economia da Universidade do Porto are jointly organizing a Winter School in Econometrics. The Winter School aims to provide the full set of tools and techniques that any modern applied economist needs to know. Participants will learn the techniques properly using the software Stata.

Target

Academic Staff, Masters / PHD students and professionals that need to analyse data. It aims to offer an effective way to reach an advanced level of econometric analysis. Therefore, in order to get the most out of the course, basic knowledge of statistics and econometrics is required. The course will be taught in English.

Agenda

Day 1 - Introduction to Stata: Miguel Portela

  • Introduction to the Stata: from menus to 'do' files
  • Handling data with different data types: Stata, ASCII, Excel, CSV, Web Data and ODBC
  • Data reshaping
  • Combining different data sets: merge and fuzzy merge
  • Exploratory data analysis: descriptive statistics and graphic manipulation




Day 2 - Data Analysis, Linear Regression, and Spatial Econometrics: João Cerejeira

  • Bivariate inferential statistics
  • Simple and multiple linear regression
  • Specification issues: regression with indicator variables, nonlinear relationships and regression diagnostics
  • Regression analysis and causality: internal and external validity
  • Spatial data analysis and spatial econometrics

Day 3 – Managing Output Files: Miguel Portela and Paulo Guimarães


  • Exporting Output Results (Tables and Graphs): Excel, Word, PDF, Latex
  • Creating and modifying files from within Stata: Excel, Word
  • Literate Programming with Stata
  • Markdown/Pandoc
  • Markstat: Outputting to HTML/Word/PDF/Latex/Beamer
  • Automating the Production of Papers and Reports: Examples

Day 4 - Linear Panel Data Models: Anabela Carneiro

  • Introduction to Panel Data Analysis
  • Fixed Effects Model
  • Test for the Presence of Fixed Effects
  • Random Effects Model
  • Test for Random effects: the Hausman test
  • Pooled OLS Model
  • Comparison of Estimators

Day 5 - More Topics in Linear Panel Data Models

Dynamic Panel Data Models: Miguel Portela

  • Endogeneity
  • Generalized Method of Moments (GMM)
  • Arellano and Bond first-differences estimator
  • System GMM
  • Specification tests: Sargan, Hansen & unit root tests

High-Dimensional Fixed Effects (HDFE): Paulo Guimarães


  • The Linear Model with one HDFE
  • The Linear Model with two HDFE
  • Spell” fixed effects
  • Estimation with two HDFE
  • Identification of the fixed effects
  • Multiple HDFE

References

Financial Econometrics using Stata (Simona Boffelli and Giovanna Urga);
An Introduction to Modern Econometrics using Stata (Christopher Baum)
An Introduction to Stata Programming, 2nd Edition (Christopher Baum)
Microeconometrics using Stata (A. Colin Cameron and Pravin K. Trivedi)
Introduction to Time Series using Stata (Sean Becketti)
Data Analysis using Stata, 3rd Edition (Ulrich Kohler and Frauke Kreuter)
Regression Models for Categorical Dependent Variables using Stata, 3rd Edition (J. Scott Long and Jeremy Freese)

Prerequisites

DAY 1:

  • No prior knowledge of Stata required. Knowledge of using other statistical software is an advantage but not necessary.

DAY 2:

  • Prior knowledge of Stata is not essential but very helpful;

DAY 3:

  • A basic understanding of Stata and familiarity with regression analysis are required.

DAY 4:

  • Basic knowledge of Stata and panel data models.

DAY 5:

  • Basic knowledge of Stata and panel data and linear models.

Terms and Conditions

  • Student registrations: Attendees must provide proof of full time student status at the time of booking to qualify for student registration rate (valid student ID card or authorised letter of enrolment).
  • Additional discounts are available for multiple registrations.
  • Cost includes course materials, lunch and refreshments.
  • Delegates are provided with temporary licences for the software(s) used in the course and will be instructed to download and install the software prior to the start of the course. (Alternatively, laptops can be hired for a fee of £10.00 (ex. VAT) per day).
  • If you need assistance in locating hotel accommodation in the region, please notify us at the time of booking.
  • Payment of course fees required prior to the course start date.
  • Registration closes 5-calendar days prior to the start of the course.
    • 100% fee returned for cancellations made over 28-calendar days prior to start of the course.
    • 50% fee returned for cancellations made 14-calendar days prior to the start of the course.
    • No fee returned for cancellations made less than 14-calendar days prior to the start of the course.

The number of delegates is restricted. Please register early to guarantee your place.

  •  CommercialAcademicStudent
    1-Day (21/01/2019 - 25/01/2019)
    2-Day (21/01/2019 - 25/01/2019)
    3-Day (21/01/2019 - 25/01/2019)
    4-Day (21/01/2019 - 25/01/2019)
    5-Day (21/01/2019 - 25/01/2019)

All prices exclude VAT or local taxes where applicable.

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