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Regression Analysis & Causality using Stata

  • Location: Cass Business School
  • Duration: 2 days (19th October 2017 - 20th October 2017)
  • Software: Stata
  • Level: Introductory
  • Delivered By: Pierre Hoonhout
  • Topic: Data visualization, Econometrics, Microeconomics, Regression analysis
Regression Analysis & Causality using Stata

Course Overview

The course is designed for academic staff, including master/PhD students, who have a basic knowledge of statistics/econometrics and/or Stata; and those who deal with different types of data and projects in their day-to-day work. The course will also be of interest to non-academic participants who regularly apply data analysis from an econometric perspective.

This course will focus on a number of applications available within Stata, including:

  • Organising and handling data
  • Data analysis
  • Linear regression: OLS and GLS
  • Causal inference with Stata: differences-in-differences and instrumental variables
  • Producing analysis output: graphs and tables

Course Agenda

Day 1 - Overview & Linear Regression

Session 1: - Stata Refresh
  • The grammar of Stata
  • From command line to ‘do files’
  • Import, reshape and combine data
  • Statistics & Graphics: an introduction
Session 2: Linear Regression & Stata (I)
  • Computing linear regression estimates
  • Presenting and discussing regression estimates
  • Sampling distribution of regression estimates
  • Hypothesis tests
  • Specification issues: graphically analysing regression data
Session 3: Linear Regression & Stata (II)
  • Interaction terms and marginal effects.
  • Heteroskedasticity: causes and test;
  • The robust estimator of the VCE;
  • The GLS and FGLS estimator.
Session 4: Exercise & output export
  • Hands-on: regression analysis exercise
  • Producing Analysis Output
  • Graphs and regression tables from Stata to Word and Tex
  • Combining Stata and Excel: playing Excel with Stata

Day 2 - Causality & Panel Data Analysis

Session 5: Causal analysis (I): From Regression to Causality
  • Defining causality
  • Regression and causality
  • Differences-in-Differences approach to causal analysis
Session 6: Causal analysis (II): Instrumental Variables Methods
  • Instrumental variables estimators
  • 2SLS (Two stage least squares method)
  • Conditions for instrument validity
  • The problem of weak Instruments
  • Testing overidentification restrictions
  • Interpreting Stata IV output
Session 7: Panel data (I): Formulation and Estimation
  • Longitudinal data management
  • Panel data regression: dealing with endogeneity issues
  • Data structure & formulation of the model
  • Fixed and Random Effects in Static Models
  • Discussion of key issues
Session 8: Panel data (II): inference & extensions
  • Hausman test for the validity of the random effects model
  • Hypothesis testing, Test for the presence of fixed effects, Wald tests, testing multiple hypothesis
  • Heteroscedasticity, Autocorrelation, Robust Estimation
  • High dimensional panel models

Daily Timetable

Subject to minor changes

09:00-09:20   Registration
09:30-11:00   Session 1
11:00-11:15   Tea/coffee break
11:15-12:45   Session 2
12:45-14:00   Lunch
14:00-15:15   Session 3
15:15-15:30   Tea/coffee break
15:30-17:00   Session 4

Prerequisites

  • A basic knowledge of statistics and regression analysis is assumed.
  • Previous experience with Stata is recommended.

Terms & 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.
  • Attendees 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, we can also provide laptops for an additional cost of £12.00 per day.
  • If you need assistance in locating hotel accommodation for the course, 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 seats available is restricted. Please register early to guarantee your place.

  •  CommercialAcademicStudent
    2-days (19/10/2017 - 20/10/2017)

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