What is the relationship between ordinary least squares and poisson model? I am a beginner in learning Statistics with some knowledge of machine learning, some concepts really confused me recently.
Starting from the linear regression and OLS, I think the former is a model with parameters/coefficient and the latter is the method I used to estimate the parameters for the linear regression model. So the relationship between them is the model and estimating method.
However, I read papers using the Gravity Model to describe some phenomena or test hypotheses, some of them estimate the parameters by OLS, but the others use "models" like Poisson, negative binomial to "estimate" the parameters.
I am confused, how can I use one "model" to estimate the parameters of another?
It must be something wrong with my concepts system, and I really need someone to correct me. 
In addition, I want to use econometrics methods/models to perform my research (not in the field of statistics or econometrics), is there any books with practical examples and code I can read? Since textbooks I read pay a lot of attention to prove something and lack a macro view of the whole system, I am still not able to perform actual experiments even I obtain that knowledge. Maybe books with some end-to-end examples can help. 
 A: Hi: It is too broad of a topic to explain here. But, the first thing you should do, in order to keep things clear,  is not use the term "normal linear  regression model.". Call it a linear model with a normally distributed error term. In this way,  every single model ( poisson, binomial, linear model with normally distributed error term, probit ) can be viewed as a generalized linear model. I don't know what a gravity model is but any decent econometrics book will have a chapter on a few generalized linear models. The problem is that the econometrics text will not call them generalized linear models. Still, if you want to look in econometrics texts, look at william greene's, judge and hill or madalla.  I suspect that each of those will have a chapter on what they call limited dependent variables which are a smaller specific set of glms and therefore make things more confusing. ( econometrics only considers a few glms ).  That's why I said to look at John Fox's text. He will actually call them generalized linear models and he'll cover most if not all of them.  The drawback with using his text is that he won't come at  them from a econometric standpoint. Still, I think it's better to look at generalized linear models first and then, once you have those straight, then look at limited dependent variable models for specific "econometric" type glm's. This is not an answer but I decided to write it here since there was more space. I hope it helps a little.
