# Regression analysis model

A very important topic… Regression model problems.

There are many regression problem that are hard to conceptualize. Lets start with the simpler ones:

The easiest is definitely Heteroskedasticity where the standard errors increase as the dependant variable X increase. This cause the small X value to have small errors but biggerX value to have large error. Thus model become invalid as time series becomes bigger. Use Breush-Pagen to test (have no clue of the formula…)

Autocorrelation or also serial correlation/ lagged correlation: Patterns within a linear regression lines which causes additional errors. Use Durbin-Watson to test If DW = 2 then no problem, if DW is higher than 2, the standard error is overstated if lesser than 2, standard deviation is understated. Correct with Hansen Method.

Multicollinearity : Only applicable to multiple regression. High correlation between X. High R Square so indicate a nice correlation but, F stat and T stat are very low thus making the whole equation insignificant (F stat) or the regression insignificant (T stat). Solution would be to decrease a few regression parameters (Xs).

Cointegration : Tested on the CFAI 12AM and apparantly some scientist called Engle-Granger found the solution. (have no clue what this is). Dont remeber seeing this in Schweser12 or Stalla 2011.

Problem with non-linear models :

Covariance stationary : VAR is constant across time (isnt this a good thing(, perform Dickey fuller test.

Unit root : what the hell is that anyways?

Thanks

i would review multicollinearity (among others) if i were you. there is still time.

on a side not: this is why i can’t do study groups - people who “think out loud” are trying to “think things through” (at times erroneously), while those who are listening just get confused.

Lemiman, that is the point of the post, to get the right answer.

Why dont you just post the right answer?

Multicollinearity : F significant, T insignificant.

Multicollinearity : F significant, T insignificant. R^2 significant, t-stat biased downward