課程資訊
課程名稱
時間序列分析
Time Series Analysis 
開課學期
100-1 
授課對象
社會科學院  經濟學研究所  
授課教師
林金龍 
課號
ECON5007 
課程識別碼
323 U0600 
班次
 
學分
全/半年
半年 
必/選修
選修 
上課時間
星期一6,7,8(13:20~16:20) 
上課地點
社科23 
備註
限學士班三年級以上 或 限碩士班以上
總人數上限:50人 
Ceiba 課程網頁
http://ceiba.ntu.edu.tw/1001tsa1 
課程簡介影片
 
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課程概述

This course focuses exclusively on Time Series Analysis (TSA)
designated for advanced undergraduate or graduate students majoring
in economics, finance, business, statistics, and engineering.

Conventional time series modeling strategy, cointegration, causality testing and volatility models are the four main topics.

The course starts with a lecture introducing stochastic process, time series
model and statistical package R. I then spend 3 lectures covering conventional univariate time analysis, including identification, estimation, diagnostic checking and forecasting of a time series model. Unit root and cointegration econometrics makes the second part. The third and main part comprises univariate ARCH/GARCH,multivariate GARCH models and stochastic volatility models.

 

課程目標
Similar to any other filed of economics and finance, intuition and
creative ideas constitute the flesh and bone of time series analysis (TSA).
I am aiming at equipping the students with proper tools for advanced empirical work and lay the foundation for theoretical research in TSA. In
additional to econometric theory, I also emphasize computational
aspects of these complicated econometric techniques. R is the main statistical packages used in this course.

 
課程要求
Students should have taken courses on statistics.  
預期每週課後學習時數
 
Office Hours
另約時間 
參考書目
Ruey S. Tsay, 2010, Analysis of Financial Time Series Third edition, New
York: John Wiley
Soren Johansen, 1995, Likelihood-based inference in cointegrated vector
autoregressive models, Oxford: Oxford University Press
Lon-mu Liu, 2006, Time Series Analysis and forecasting, Second Edition, Scientific Computing Associates
Clive Granger, 1986, Forecasting Economic Time Series, Second Edition, Academic
Press  
指定閱讀
Jonathan D. Cryer and Kung-Sik Chan, 2008
Time Series Analysis With Applications in R, Second Edition, Springer
(On-line fulltext available from the link at the NTU library)

Lecture Notes  
評量方式
(僅供參考)
 
No.
項目
百分比
說明
1. 
Homework and empirical project  
30% 
 
2. 
midterm  
30% 
 
3. 
Final 
40% 
 
 
課程進度
週次
日期
單元主題
第2週
9/19  Introduction to Stochastic Process, Time series and R 
第3週
9/26  ARIMA modelling I / Identification  
第4週
10/03  ARIMA modelling II / estimation and diagnostic checking 
第5週
10/10  ARIMA modelling III / Forecasting  
第6週
10/17  ARIMA modelling IV / Empirical Examples 
第7週
10/24  VAR and Impulse response analysis 
第8週
10/31  VAR and Impulse response analysis 
第9週
11/07  Cointegration I 
第10週
11/14  midterm exam 
第11週
11/21  Cointegration II 
第12週
11/28  Cointegration III/ Causality Testing I 
第13週
12/05  Causality testing II 
第14週
12/12  Univariate GARCH I 
第15週
12/19  Univariate GARCH II 
第16週
12/26  Multivariate GARCH I 
第17週
1/02  Multivariate GARCH II