Reddit reviews Basic Econometrics

http://www.amazon.com/Basic-Econometrics-Damodar-Gujarati/dp/0073375772

Uhm... in econometrics, I'd go with Woolridge (he has another book mostly on panel data but that is a graduate level textbook). I used Gujarati in my undergrad classes and didn't like it very much.

If you are more into macro-oriented time series econometrics, Enders is a great book, very practical and with a lot of examples. If you end up doing applied microeconometrics in Stata, Cameron and Trivedi have a great book for that.

I just stumbled upon this website that seems to have good info on econometrics texts which, as you can see, can be pretty pricey.

If you want to know just the practical use:

The closest the R2 goes to 1, the better the model. For example, an R2 of, say, 0.82 means: "82% of the variation of the dependent variable is explained by the variation of the dependent variables". Conversely, the less the R2, the less the predictive power of the dependent variables.

In order to evaluate the significance of each variable, you have to check the P values. The practical meaning is: the lower the p-value, the more useful is the variable. For example, a p-value of, say, 0.01 means the variable is very significant, and therefore should be implemented in the model. The level of significance of course depends on your work.

edit: If you want to know the theory, take a look at this. It is one of the best books. https://www.amazon.com/Basic-Econometrics-Damodar-Gujarati/dp/0073375772

First, I need to make a disclaimer that I am not an expect. I am unsure if you are using a text to reference your question, but I would recommend [Basic Econometrics] (http://www.amazon.com/Basic-Econometrics-Damodar-Gujarati/dp/0073375772). That text will be much more helpful than I will be.

I'll start will the question you asked in your other post.

> I have read that Multicollinearity is different from Endogeneity, because in the former case, there is not supposedly a causal relationship between the multicollinear variables. But in my model I know for sure that there is a causal relationship between my variable of interest and a control variable, but both are also related to my dependent that I really can't leave the control variable out. Does this 'difference' between multicollinearity and endogeneity make sense?

Your first statement that there is not supposedly a causal relationship between collinear variable is correct. With endogenity, we are concerned about causal relationships, because it is the existence of the causal relationship that is causing our variable to be correlated with the error term. However, with multicollinearity we are not concerned about the causality between the two variables. It is the fact that these two variables are correlated together, and not that one causes the other, that is going to cause the issues of multicollinearity that I described before. We could have two completely unrelated variables in our analysis, but it they by chance are correlated together we are going to get the problems of multicollinearity. Does that make sense?

So, if I understand this correctly, you are concerned that your variables representing fiscal consolidations and the economy may be correlated together and cause collinear issues? What does your correlation matrix say? Also, if you are not trying to determine causal affects, then why are you concerned about using an IV approach? I thought the purpose of an IV was to try to determine causal relationships. Rather than looking for an IV variable, I think what you might be looking for is a [proxy variable] (https://en.wikipedia.org/wiki/Proxy_(statistics). A variable that can stand in your control variables place and represent it, but is not correlated with your key variable. IV and proxy are closely related, but there are less requirements for finding a suitable proxy variable.