Serial Correlation means (loosely speaking) that error in time period 2 is correlated with error in time period 1. In these videos, we look at what is serial correlation and when does it arise? What are different orders of serial correlation? What are the consequences of ignoring serial correlation? How do you detect first order serial correlation? Continue reading
While discussing the assumptions of classical linear regression model, one of the assumptions was of equal variance of the error term i.e. E(ui^2) = σ^2, in these videos, we will figure out, what will happen if this assumption is not met.
Hetroscedasticity is the problem in cross-section data. Imagine a dataset of income of households in some city, there will be both rich households and poor households. Do you think that expenditure patterns of both kinds of households are same? No. This is the problem of hetroscedasticity. We discuss the following in these videos: Continue reading
In these videos, we describe what are functional forms? Up until now, we have seen models, involving linear dependent variables and linear independent variables. Now, we will see cases, involving non-linear dependent and non-linear independent variables. Continue reading
This post starts the new chapter in our econometrics course. From now on, we move to regression of several variables, instead of just one variable. Once again we derive OLS estimates or normal equations in a multiple variable model ( precisely, 3 variable model).
Then, we pick up hypothesis testing in multiple linear regression model. How to test several coefficients jointly? How to test linear combination of coefficients?
Last video, explains, yet another very important concept : omitted variable bias. What happens, if you remove one very important variable from the regression model? How does that affect OLS estimates, are they still unbiased? Continue reading
Here we present the concept of Hypothesis testing. We start with the simple linear regression model and test for the following:
We begin by introducing hypothesis testing, then explaining one tailed and two tailed t tests and end this discussion by explaining test for goodness of fit Continue reading
Simple Linear Regression Model. These videos will help you understand all basic proofs and theorems related to it. We discuss following issues in these videos
We start by deriving normal equations in a simple linear regression model and get the OLS estimates of constant and slope parameter Continue reading
These videos are discussing the initial basics econometrics course. Simple linear regression model is given by Yi = β1 + β2Xi + ui where ui~N(0,σ2). Here, we set out different assumptions of classical linear regression model.
Two main (and excellent) references for this course are :
Basic Econometrics by D. Gujarati
Introductory Econometrics with Applications by Ramu Ramanathan
I hope you like these videos..
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