Quants : Q1 Case Study 3

mail2arungoel
Good Student
Posts: 29
Joined: Mon Apr 23, 2012 9:45 pm

Quants : Q1 Case Study 3

Postby mail2arungoel » Thu Apr 26, 2012 9:28 am

Kindly explain the solution, particularly how the value of X= 130 has been taken in Oct 2011 to arrive at the result. The regression equation is of a lagged variable and X=9.1 in May 2011 ; the equation is X(t) = 4.073 + 0.034 * X(t-1)

Regards,
Arun

content.pristine
Finance Junkie
Posts: 356
Joined: Wed Apr 11, 2012 11:26 am

Re: Quants : Q1 Case Study 3

Postby content.pristine » Fri Apr 27, 2012 1:31 pm

Hi Arun,

The result of the regression equation is a time series, not an autoregression.
Therefore, we use y=b0+b1(t)
Here t=130, as we use t=125 from May 2011.
The trick here is to follow the regression equation, not add 5*b1, since the May 2011 term would include its error term.. When we forecast a variable, we do not take into account its error term..

Hope this helps 8-)

mail2arungoel
Good Student
Posts: 29
Joined: Mon Apr 23, 2012 9:45 pm

Re: Quants : Q1 Case Study 3

Postby mail2arungoel » Fri Apr 27, 2012 7:25 pm

content.pristine wrote:Hi Arun,

The result of the regression equation is a time series, not an autoregression.
Therefore, we use y=b0+b1(t)
Here t=130, as we use t=125 from May 2011.
The trick here is to follow the regression equation, not add 5*b1, since the May 2011 term would include its error term.. When we forecast a variable, we do not take into account its error term..

Hope this helps 8-)

"yes, sure and a great help too; makes me feel silly, but greatly enriched! Arun :geek: "


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