Clive W. J. Granger, Nobel Laureat Has Died at 74

Posted by Andrew at 5/31/2009 6:28 AM
Categories: Work,America,Economics
The majority of my research (what little I have done) and publishing, has been due to the seminal work of C.W.J. Granger.

When I first studied Times Series Analysis at Iowa State University, his work had yet to begin.  Thus when he developed the concepts of stationarity and co-integration, I eagerly enjoined!

I appreciate his contributions as have so many others.

The article in the New York Times is linked here. 
(Bloomberg.com just links the NY Times article and have yet to find an article in the Wall Street Journal).

 

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  • 6/2/2009 4:02 AM Nancy wrote:
    Stationarity is not in my dictionary!
    Reply to this
    1. 6/2/2009 6:44 AM Andrew wrote:
      LAUGHING OUT LOUD!!!  Of course!

      OK - from Wikipedia:

      In the mathematical sciences, a stationary process (or strict(ly) stationary process or strong(ly) stationary process) is a stochastic process whose joint probability distribution does not change when shifted in time or space. As a result, parameters such as the mean and variance, if they exist, also do not change over time or position.

      Stationarity is used as a tool in time series analysis, where the raw data are often transformed to become stationary, for example, economic data are often seasonal and/or dependent on the price level. Processes are described as trend stationary if they are a linear combination of a stationary process and one or more processes exhibiting a trend. Transforming this data to leave a stationary data set for analysis is referred to as de-trending.


      Reply to this
      1. 6/2/2009 6:51 AM Andrew wrote:
        Maybe that one wasn't fair?!?  There seem to be more words to look up than explained, eh?!?  That's how I felt when I first began the foray into the word of statistics and econometrics!!!

        Here's a little easier definition (from the Engineering Statistics Handbook):
        A stationary process has the property that the mean, variance and autocorrelation structure do not change over time. Stationarity can be defined in precise mathematical terms, but for our purpose we mean a flat looking series, without trend, constant variance over time, a constant autocorrelation structure over time and no periodic fluctuations (seasonality).

        Reply to this
  • 6/3/2009 5:15 AM Nancy wrote:
    I am really really really sorry I said anything. Now my head hurts.
    Reply to this
    1. 6/3/2009 5:59 AM Andrew wrote:
      Yeah - let that one go - NOT IMPORTANT (except for those REALLY wonkish types!!!) - grin

      Reply to this
  • 6/5/2009 3:23 PM Nate Nichols wrote:
    Just wait 'til Drew starts talking about heteroskedasticity. :/
    Reply to this
    1. 6/9/2009 12:21 AM Nancy wrote:
      Let's not go anywhere near that...
      Reply to this
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