Course page

ECONOMETRICS

provided by City University of London

Course Details

(last updated: September 2022)


  • Term in which taught: Term 1

  • Application deadline: Please apply by September 18 in order to join this course

  • Pre-requisites: MSc level econometrics module or equivalent. Undergraduate level Macroeconomics, Microeconomics, Calculus, Statistics, Probability and Matrix Algebra.

  • Summary course description: The course aims to cover basic topics in econometrics. The course will deliver a comprehensive list of empirical methods that allow researchers to analyse data. Such tools are essential for graduate students who aspire to conduct careful, state-of-the-art empirical research. In addition, the course will provide general guidance on formulating and executing (empirical) research ideas.

Further details about this course (click to expand)

Taught sessions: 10 sessions of 3 hours each.

Assessment for both parts will be based on two components: (1) in-class participation and presentations; (2) One short written (referee) report on the paper presented.

Provisionally, the syllabus for the module is as shown, but there may be some changes:

Part I: Estimation methods and Time Series of stationary data

· Review of Estimation Methods (OLS, MLE, GMM)

· ARMA processes

· Volatility models: ARCH, GARCH processes

· Vector processes, VAR (reduced form, structural form and identification issues)

· Kalman Filter and Score Driven models

· (depending on time) Financial Econometrics selected topics on the Econometrics of HF Data and Option Pricing.


Part II: Selected topics in Micro-econometrics

· Models for Qualitative Dependent Variables

o Binary and Multinomial Response Models

o Logit and Probit Models

o Multinomial Logit and Probit Models

· Models for Limited Dependent Variables

o Censored and truncated data

o Tobit model

o Sample selection model

· Panel data models

o Systems of Simultaneous Equations,

o Static Linear Panel Data Models,

o Dynamic Linear Panel Data Models,

o Nonlinear Panel Data Models

· Selected topics in statistics:

o (depending on time) Non/semi-parametric statistics, identification, quantile regression, consistency of maximum likelihood estimators, etc.


How to Apply

In order to apply for this course, you need to (1) send an Application Email to the Instructor and to (2) fill the Confirmation Form at the bottom of this page. Please complete both steps according to the instructions reported below.

1. Application Email: instructions

Step 1 of 2: Application Email. Send an email to the Instructor of this course and to the Academic Officer of the host university: names and contact links are reported above in the section 'Course Details' (right-click on the name to copy their email address, or left-click on the name to create a new email with your default software). Please send your application email using your university email account. Sample email:


To: [INSTRUCTOR]

Cc: [ACADEMIC OFFICER]

Object: Application to your online PhD course (ShOT/SEDOT)


Dear Prof [INSTRUCTOR],

My name is [STUDENT NAME], PhD student at [HOME UNIVERSITY]. I would like to attend online your PhD course in [COURSE TITLE], shared through the ShOT/SEDOT network. Please let me know if I can join the course, I look forward to hearing from you.

2. Confirmation Form: instructions

Step 2 of 2: Confirmation Form. Please answer all the questions in the Confirmation Form below and press the Submit button in the last page. All fields are required. You will receive an automated notification by email.