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 經濟學系 |
授課教師 |
林金龍 |
課號 |
ECON5007 |
課程識別碼 |
323 U0600 |
班次 |
|
學分 |
3 |
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 |
|
核心能力關聯 |
核心能力與課程規劃關聯圖 |
課程大綱
|
為確保您我的權利,請尊重智慧財產權及不得非法影印
|
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 |
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 |
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 |
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% |
|
|
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 |
|