Intermediate Time Series Regression

INTERMEDIATE TIME SERIES REGRESSION
 
This workshop covers the fundamentals of regression using time series data. The bulk of the workshop will cover regression using stationary, weakly dependent data with an emphasis on serial correlation robust standard errors. Time series regression with nonstationary data will be briefly covered if time permits. Empirical examples from microeconomics, macroeconomics and climatology will be explored using the software Stata. This is an intermediate level workshop where it is assumed participants have knowledge and experience with regression methods but no knowledge of time series analysis is assumed. In addition to the slides posted on Angel, recommended background reading is chapters 10-12,18 in Introductory Econometrics (3rd or 4th editions) by Wooldridge. More advanced coverage can be found in Hamilton’s text Time Series Analysis.  
Topics:
  • Gauss Markov Time Series Regression with Strict Exogeneity
    • Empirical Application: Fulton Street Fish Market
  • Deterministic Time Trends and Spurious Regression
    • Empirical Application: Global Temperatures and Economic Activity
  • Stationarity, Weak Dependence and Weak Exogeneity
  • Serial Correlation: AR(1) Model
  • Testing for Serial Correlation
  • Serial Correlation Robust Standard Errors, aka Newey-West
    • Empirical Application: Federal Reserve Interest Rate Policy
  • Regression with Nonstationary Time Series Data: Unit Root Data, Spurious Regression, Cointegration
Instructor: Tim Vogelsang, Addy Professor of Economics with research interests in econometrics and time series analysis.
 
Audience: Faculty and graduate students experienced with regression methods but with little or no knowledge of time series statistics.


Instructor: Dr. Timothy Vogelsang, Tim Vogelsang, Addy Professor of Economics with research interests in econometrics and time series analysis.

Date: Friday, September 23, 2011

Time: 1:00 PM - 4:00 PM

Location: 106 Farrall Ag. Eng. Hall

Audience: Faculty and graduate students experienced with regression methods but with little or no knowledge of time series statistics.


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