Testing for Unit roots Consider an AR(1): y=a+p +e,t Let Ho: p=1, (assume there is a unit root) Define 0=p-1 and subtract y, from both sides to obtain Ay,=a+ B+e, Unfortunately, a simple t-test is inappropriate, since this is an I(1) process ADickey-Fuller Test uses the t-statistic, but different critical values Economics 20- Prof anderson

Simultaneity Simultaneity is a specific type of endogeneity problem in which the explanatory variable is jointly determined with the dependent variable 2 As with other types of endogeneity, IV estimation can solve the problem o Some special issues to consider with simultaneous equations modelS(SEM) Economics 20- Prof anderson

Fixed Effects estimation When there is an observed fixed effect. an alternative to first differences is fixed effects estimation Consider the average over time of y Bx1+…+Bxik+a1+l The average of a, will be ai so if you subtract the mean. a will be differenced out just as when doing first differences Economics 20- Prof anderson

Testing for AR(IS eria Correlation Want to be able to test for whether the errors are serially correlated or not Want to test the null thatp=0 in u,=pu, 1 +et=2.. where u is the model error

Time series vs Cross sectional e Time series data has a temporal ordering unlike cross-section data Will need to alter some of our assumptions to take into account that we no longer have a random sample of individuals Instead. we have one realization of a stochastic(i.e. random) process Economics 20- Prof anderson

What is Heteroskedasticity Recall the assumption of homoskedastic implied that conditional on the explanator variables the variance of the unobserved error u was constant If this is not true that is if the variance of u is different for different values of thex's. then the errors are heteroskedastic

Redefining variables Changing the scale of the y variable will lead to a corresponding change in the scale of the coefficients and standard errors. so no change in the significance or interpretation Changing the scale of one x variable will lead to a change in the scale of that coefficient and standard error, so no change in the significance or interpretation Economics 20- Prof anderson

Assumptions of the classical Linear Model (Clm) e So far, we know that given the Gauss Markov assumptions, OLS IS BLUE e In order to do classical hypothesis testing we need to add another assumption(beyond the Gauss-Markov assumptions) Assume that u is independent of x,x2…,xk and u is normally distributed with zero mean and variance 0: u- Normal(0, 02) Economics 20- Prof anderson

Some Terminology o In the simple linear regression model where y=Bo+ Bx+ u, we typically refer to y as the a Dependent variable, or a Left-Hand Side Variable. or Explained variable, or Regressand

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