Time Series 2012 Spring

General Information

• Lectures: 10-17 week (6:00-8:30pm, Monday, Thursday)
• Classroom: Antai 204
• Office Hour: Wednesday 1:00-2:00pm or by appointment
• Office: North Building #413
• Phone: 021-52301191
• Email: jhqian At gmail.com

Prerequisites

• Econometrics I (Graduate level)

Overview

This course is intended for doctoral students majoring in economics and finance. It introduces basic models in time series, including ARMA, ARCH/GARCH, VAR, etc. More advanced subjects, such as state space models and nonstationary time series, will also be covered. During the first part of the course, some training in Matlab implementation will be provided, so that at the end of the course, students are expected to be able to do estimation and forecasting usingn Matlab.

Textbook

I'll make handouts available online (see below). The additional textbook is Hamilton (1994), Time Series Analysis, Princeton University Process.

Outline

• Introduction
• Conditional Mean Models (ARMA,etc.)
• Conditional Variance Models (ARCH/GARCH, etc.)
• Multivariate Time Series Models (VAR, DCC, etc.)
• State Space Models
• Integrated Time Series (Unit Root, Cointegration, etc.)

Grading

Labs

• Lab 1: Introduction to Matlab (Matlab codes: test_opt.m)
• Lab 2: ARMA modeling (data: cpi.xls) (Matlab codes: armamle.m, test_armamle, arma.m, test_arma.m, armaforecast.m, test_armaforecast.m)
• Lab 3: ARCH/GARCH modeling (data: SP500.mat) (Matlab codes: garch.m test_garch.m)
• Lab 4: State space modeling (data: cpippi.txt, shanghai.txt)

Last updated: April 19, 2012