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Lecture 1 Introduction to Course: the Nature of Econometrics
1) The nature of econometrics-in your introductory economics courses you learned about various theories of economic behavior. For instance, you learned in introductory macro that consumption i
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Lecture 35- Multicollinearity 1
We are now going to look at the second classical regression assumption concerning multicollinearity. Recall that multicollinearity exists when one independent variable can be written as a linear function of the remainin
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Lecture 33 - Dummy variables
1) We are going to return to the study of dummy variables. Recall that we showed that one should always use one less variable than the number of categories one is looking at. Recall also that the interpretation of the dumm
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Lectures 31 & 32 - Functional form
1) The next topic we will discuss concerns the functional form of the regression equation. That is, do the variables enter in linear form, as squares, logs, etc. Once again, we should allow theory to determine this a
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Lecture 29 - Things to be careful about when doing regression analysis
1) Doing what theory suggests. We have seen that the inclusion of irrelevant variables will cause the variance of estimators to increase and hence lower the value of their t-statis
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Lecture 28 - Irrelevant and omitted variables
1) We are going to need a derivation to understand some of the work in this chapter. Suppose we have the following regression model and the derivation based upon it. Recall that the formula for the slope c
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Lecture 23 Hypothesis Tests
We will now begin to discuss hypothesis tests. As we will see, hypothesis tests and confidence intervals are closely inter-related. Recall from introductory macroeconomics that economic theory states that the demand for mone
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Lecture 22 - Statistical inference
1) We have learned how to calculate estimators, and we have discovered what their PDF is. Now we must return to the original purpose for finding estimators. After all, what we really want to know are the true values o
Lecture 21 Lags and Dummy Variables
Lagged variables-up until now we have given all of our variables the same time subscript, t. When we do this we are saying that a change in an independent variable at one point in time affects the dependent variable at
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Lecture 17 - Classical regression model
We have learned how to estimate regression equations, and how to interpret the results. However, life is not as simple as this, for it turns out that we can only use the method of least squares when a certain set
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Lecture 16 Learning to Use Regression Analysis
We are now going to look at the procedure to be followed in doing an econometric study. While the general procedure is easy to outline, you should recognize that in practice econometrics is as much an art
Lecture 15 - Directions for downloading data into STATA
The second method for entering data works for data that you can find stored on another computer, usually on the Internet. This is particularly helpful when using large data sets, for it takes time to
Lecture 14 Introduction to STATA
Our next step is to learn how to use the computer to carry out the computations for us. In what follows, commands you should enter appear in lower-case boldface.
Instructions for STATA
1) Formatting a disk-(this should be
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Lecture 12 Ordinary Least Squares and Multiple Regression
1) Multiple regression - a multiple regression equation is an equation with more than one independent variable. The general form of the multiple regression equation can be written as follows:
Yt
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Lecture 10 Estimating and Using a Regression Line
Regression analysis involves two steps: 1) First, formulate theory, or specify a model; for example, you have a theory that parents income determines the grade point average of a student; 2) second, you
Lecture 6 Estimators of Parameters (Statistics)
What we will now do is to discuss the estimators for each of these parameters. Estimators (statistics)The process of taking a random sample is an experiment. Any particular random sample leads to a specific
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Lecture 3: Random Variables
Note that in the last diagram I have included the symbol X. What is this symbol standing for? We can think of a statistics problem as an experiment or process with one or more possible outcomes. We are not certain which outco
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Lecture 38 - Serial correlation
1) Serial correlation occurs most frequently in time series data. Serial correlation implies that order matters (a positive error follows a positive error, or a negative error follows a positive, etc.), since it implies