Foundation of Econometrics with Applications - Co-Developed with Lancaster University Management School

Lancaster University Management School 3 days Stata Intermediate, Introductory

Overview

The aim of this course is to provide students with the theoretical knowledge of regression analysis and with the practical skills to implement regression analysis with econometric software. The module prepares students for further courses in econometrics.

By the end of this module, students should be able to:

• Perform data analysis using descriptive statistics and graphical tools;
• Build and estimate linear regression models to understand the relationships between economic and financial variables;
• Run diagnostic tests to check for correct specification of estimated models and propose solutions to correct for misspecification;
• Test empirically the validity of economic and financial theories.

Day 1

Session 1 - The Simple Linear Regression Model

• Definition of the simple linear regression model
• Deriving the ordinary least squares (OLS) estimates
• The Gauss-Markov theorem and its assumptions
• Properties of the ordinary least squares (OLS) estimator

Session 2 - The Multiple Linear Regression Model

• Definition of the multiple linear regression model
• The ceteris paribus interpretation
• Deriving the ordinary least squares (OLS) estimates in a multivariate context

Session 3 - Inference in the Multiple Linear Regression Model

• Testing Hypotheses about a Single Population Parameter: The t Test
• Testing Hypotheses about a Single Linear Combination of the Parameters
• Testing Multiple Linear Restrictions: The F Test

Lab Session 1 Introduction to econometric software to estimate linear regression models and conduct inference

• Analysing datasets
• Building and estimating regression models
• Testing hypothesis
• Evaluate goodness of fit of regression models

Day 2

Session 4 - Heteroscedasticity

• Evaluating the implication for the OLS estimator of heteroscedasticity
• Testing for heteroscedasticity
• Robust standard errors
• Generalised least squares estimation

Session 5 - Serial Correlation

• Evaluating the implication for the OLS estimator of serial correlation
• Testing for serial correlation
• Robust standard errors
• Correcting for serial correlation with strictly exogenous regressors

Lab Session 2 - Testing and dealing with Heteroscedasticity and Serial Correlation with econometric software

• Detecting heteroscedasticity and serial correlation
• Estimating robust standard errors

Session 6 - Further Issues in Classical Linear Regression Model I

• Logarithmic functional forms
• Models with quadratics and with interactions
• Dummy variables

Day 3

Session 7 - Further Issues in Classical Linear Regression Model II

• Multicollinearity
• Adoption of the wrong functional form: the RESET test
• Omission of important variables and inclusion of irrelevant ones
• Parameter stability: the Chow test

Lab Session 3 - Using econometric software to detect issues in linear regression model

• Checking for multicollinearity
• Running the RESET test and interpreting the result
• Running the Chow test and interpreting the result

Sesison 8: Instrumental Variables Estimation and Two Stage Least Squares

• Endogeneity
• Instrumental Variable (IV) estimation
• Two Stage Least Squares estimator (2SLS)
• Testing for Endogeneity and for overidentifying restrictions

Lab Session 4 - 2SLS estimation with econometric software

• Selection of instruments and 2SLS estimation
• Implementing the Hausman test
• Implementing the Sargan test
• Testing for heteroscedasticity in 2SLS estimation

Principal texts for post-course reading

• Jeffrey M. Wooldridge (2019). Introductory Econometrics: A Modern Approach, 7th Edition. CENGAGE.

Daily Timetable

TimeSession / Description
09:00-09:20 Arrival & Registration
09:30-11:00 Session
11:00-11:15 Tea/coffee break
11:15-12:45 Session
12:45-14:00 Lunch
14:00-15:15 Session
15:15-15:30 Tea/coffee break (Feedback Session)
15:30-17:00 Session

Prerequisites

• Basic prior knowledge of Stata is needed.
• Analytical thinking is required.
• All costs exclude local taxes, where applicable.
• 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 enrollment).
• Additional discounts are available for multiple registrations.
• Cost includes course materials, lunch and refreshments.
• 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.