## LUBS3370 Applied Econometrics

### 10 creditsClass Size: 150

Email: m.a.nasir@leeds.ac.uk

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2022/23

### Pre-requisites

 LUBS2575 Statistics and Econometrics

This module is not approved as a discovery module

### Objectives

This course aims to further equip students with a good range of advanced skills and tools for data analysis. Its focus is on the use of data and econometric analysis to answer real-world questions and examine predictions of economic/finance theory. The goal of this course is for students to learn how to integrate statistical tools with research designs that are useful in conducting empirical elements of research in economics/finance.

Learning outcomes
On completion of this module, students will be able to:

Knowledge learning outcomes:

- Demonstrate critical appreciation of relevant econometric techniques used to analyse different types of real-world problems;
- Judge the validity of different econometric modelling techniques using appropriate statistical and economics tests;
- Justify conclusions using economic arguments with appropriate rigour; and
- Critically assess and accurately apply econometric techniques used to analyse and solve complex economic problems using regression packages.

Skills outcomes:

Transferable:

- Effectively interpret and communicate the outcomes of regression analysis, both in economic and statistical terms;
- Conduct economic research through research design, data analysis, synthesis and reporting; and
- Solve complex problems and make decisions by applying the fundamental tools of econometric analysis in real-world settings.

### Syllabus

Indicative content:

1. Time Series

Stationary Time-Series: Autoregressive and Moving Average Models and Forecasting
Non-Stationary Time Series: Unit root and Co-integration, Vector autoregressive model

2. Instrumental Variables

Endogeneity, finding suitable instrumental variables and Two Stage Least Squares estimation

3. Panel Data Models

Introduction to panel data, Pooled model, Fixed Effects and Random Effects

4. Limited Dependent Variable Models

Models for Binary dependent Variables: Probit and Logit models
Models for Polychotomous Variables: Multinomial models and Ordinal Models

### Teaching methods

 Delivery type Number Length hours Student hours Lecture 14 1.00 14.00 Seminar 4 1.00 4.00 Supervised Workshop 4 1.00 4.00 Private study hours 78.00 Total Contact hours 22.00 Total hours (100hr per 10 credits) 100.00

### Private study

This could include a variety of activities, such as reading, watching videos, question practice and exam preparation.

### Opportunities for Formative Feedback

Your teaching methods could include a variety of delivery models, such as face-to-face teaching, live webinars, discussion boards and other interactive activities. There will be opportunities for formative feedback throughout the module.

### Methods of assessment

Exams
 Exam type Exam duration % of formal assessment Standard exam (closed essays, MCQs etc) 2 hr 00 mins 100.00 Total percentage (Assessment Exams) 100.00

The resit for this module will be 100% by 2 hour examination.