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