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.
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
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
Session 7 - Further Issues in Classical Linear Regression Model II
- 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
- 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.
|Time||Session / Description|
||Arrival & Registration
||Tea/coffee break (Feedback Session)
- 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.