Studying at York
Applied Quantitative Research Methods - ECO00075M
- Department: Economics and Related Studies
- Credit value: 10 credits
- Credit level: M
- Academic year of delivery: 2022-23
Module summary
The module provides an introduction to a range of statistical techniques commonly used in quantitative analysis. Special attention is given to the ideas behind the statistical methods, how to apply them using real world examples and how to interpret the estimation and test results.
Module will run
| Occurrence | Teaching period |
|---|---|
| A | Spring Term 2022-23 |
| B | Summer Term 2022-23 |
Module aims
The module provides an introduction to a range of statistical techniques commonly used in quantitative analysis. Special attention is given to the ideas behind the statistical methods, how to apply them using real world examples and how to interpret the estimation and test results.
Module learning outcomes
On completing the module a student will be able to:
- estimate regression models, use specification tests, choose between different models and interpret results;
- define the concept of heteroscedasticity, multicollinearity, autocorrelation, omitted variables, measurement error, endogeneity, dummy variables, instrumental variables techniques, binary choice models;
- apply these quantitative methods to empirical applications such as the estimation of low birth weight, level of education, pollution levels, wage equations, house prices, intergenerational mobility;
- use a statistical computer package Stata to estimate regression models and test the validity of model’s assumptions.
Module content
The module starts with ordinary least squares procedures for the linear regression model. The basic techniques of regression analysis are studied, then we will examine a number of possible problems with the basic regression model and discuss how to cope with these problems. A set of empirical applications will be discussed and the statistical software Stata will be used to estimate regression models, test hypothesis and choose between different models. Specific topics covered include: the OLS estimation for multiple regression models, dummy variables, specification tests, instrumental variable estimation, heteroskedasticity, omitted variable, measurement error issues, time series data models, autocorrelation, endogeneity issues, maximum likelihood estimation method and binary choice models.
Indicative assessment
| Task | % of module mark |
|---|---|
| Online Exam -less than 24hrs (Centrally scheduled) | 100 |
Special assessment rules
None
Indicative reassessment
| Task | % of module mark |
|---|---|
| Online Exam -less than 24hrs (Centrally scheduled) | 100 |
Module feedback
Feedback will be given in the form of a mark for the exam by week 10 of the Summer term. They will also receive a cohort feedback report following the mark release and will have the opportunity to view their exam script, markers comments, paper and solutions in supervised script viewing sessions.
Indicative reading
Dougherty, C., Introduction to Econometrics, Oxford University Press, 4th Edition, 2011
Cameron, A.C., and P.K. Trivedi, Microeconometrics Using Stata, Stata Press, First or Revised Edition, 2010