EC 572/472 Time Series Analysis and Forecasting

Winter 2017, 6:40-8:30pm TTH (CH-307)
Prof. K.-P. Lin (CH 241G, 725-3931)
Office Hours: 3:30-4:30 TTH & by appointment
(Last updated: 1/15/2017)

This course covers the methodology and applications of econometric time series analysis and forecasting, with focus on issues and problems of predicting the U.S. economic and financial markets. Basic understanding of econometric analysis is required (EC 469, 570 or equivalent). Knowledge of calculus, algebra, probability theory and statistics are essential for this course. Familiar with computer programming and econometric packages will be useful. R will be used throughout the course.

Texts and Software

• Required:
  • Paul S.P. Cowpertwait and Andrew V. Metcalfe, Introductory Time Series with R, Springer-Verlag, New York, 2009.
  • Rob J. Hyndman and George Athanasopoulos, Forecasting: Principles and Practice, Oneline, Open Access Texbooks
  • R and RStudio.
  • CRAN Task View: Time Series
• Recommended:
  • Walter Zucchini, Oleg Nenadic, Time Series Analysis with R
  • Avril Coghlan, A Little Book of R for Time Series
  • Christian Kleiber and Achim Zeileis, Applied Econometrics with R, Springer-Verlag, New York, 2008.
  • W. Enders, Applied Econometric Time Series, 4th ed., Willey, 2015.
  • T. S. Tsay, Analysis of Financial Time Series, 3rd ed, Wiley, 2010.
  • W. H. Greene, Econometric Analysis, 7th ed., Part V: Time Series and Macroeconometrics, Prentice Hall, 2011.

Topics

1. Time Series Decomposition and Smoothing
   1. Trend and Seasonal Components of Time Series
   2. Exponential Smoothing of Time Series
   3. Forecasting with Exponential Smoothing
2. Least Squares Prediction
   1. Forecasting with Classical Regression Models
   2. Forecast Error Statistics and Evaluation
3. Time Series Analysis I: Introduction
   1. Covariance Stationarity
   2. Trend in Time Series
   3. Unit Root Problem: Estimation and Testing
4. Time Series Analysis II: ARIMA Models
   1. Identification
   2. Estimation and Diagnostic Checking
   3. Forecasting
   4. ARMA Analysis of Regression Residuals
5. Time Series Analysis III: Advanced Topics
   1. ARCH and GARCH Model Estimation
   2. Multi-Equation Time Series Models*
   3. Dynamic Linear Models*

Case Study and Homework

Expectation

1. There will be a mid-term (February 16, in class) and a final exam (March 21, 7:30pm). In addition, 3 or 4 homeworks will be assigned periodically (due every 2 weeks in average).
2. A course project is required for graduate students taking this course EC572. The project model constructed must be capable of performing (structural) econometric and time series forecasts. Here are some examples you could consider for the course project:
   1. Unemployment, inflation, and growth
   2. Productivity, wage, and employment
   3. Price indexes (CPI, PPI, etc.)
   4. Term structure of interest rates
   5. Government deficit
   6. Trade balance and strong/weak dollars
3. For graduate students taking EC572, a one-page project proposal is due on or before March 2 for approval. Final report of the project is then due on or before March 21.
4. Grade distribution of this course looks like this:

   	EC472	EC572
   Mid-Term	40%	30%
   Final	40%	30%
   Project		20%
   Homeworks	20%	20%