ECM605NU-Econometrics 2

Module Provider: School of Politics, Economics and International Relations
Number of credits: 20 [10 ECTS credits]
Level:7
Semesters in which taught: Semester 2 module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites: ECM604NU Econometrics 1
Modules excluded:
Current from: 2023/4

Module Convenor: Dr Shixuan Wang
Email: shixuan.wang@reading.ac.uk

NUIST Module Lead: Wanying Xie
Email: xie_wanyinglai@163.com

Type of module:

Summary module description:

This module will teach students about advanced econometric methods in time series and panel data, and empirical applications of those in macroeconomics and finance. The module is designed as two parts: the first part will focus on time series data, while the second part will deal with panel data. Each topic will be demonstrated by a mixture of 1) econometric method, 2) Monte Carlo simulations, and 3) real world applications. In addition, students will develop their econometric software skills with an introduction to R during the computer workshops.

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Aims:

The aim of this module is to provide students with a more extensive knowledge and understanding of econometrics in both time series and panel data, especially applying the techniques for research. Additionally, the module will teach students how to implement those econometric techniques, using R.

Assessable learning outcomes:

By the end of the course students should be able to:

• Understand the special econometrics techniques in time series and panel data.


• Implement and apply those econometric methods using R.


• Read relevant academic papers and understand the suitability of the methods employed.

Additional outcomes:

Students will develop their research and data handling skills and be able to critically evaluate methods and approaches chosen by research papers.

Outline content:

Time series topics may include autoregressive moving-average models, unit root/stationarity tests, model selection and diagnostics, forecasting, and cointegration.

Panel data topics may include pooled regression, Fama-MacBeth regression, fixed effects model, random effects model, and differences-in-differences model.

Brief description of teaching and learning methods:

Teaching will be a combination of lectures and computer classes.