|
Question:
Intuition to Econometrics
Solution:
|
|
|
Question:
For the simple linear regression model, derive the estimates of the parameters. Using normal equations derive the estimates of constant and slope term
Solution:
|
|
|
Question:
Derive the deviation form of beta 2 hat (deviation form of slope term)
Solution:
|
|
|
Question:
Write a simple linear regression model in deviation form
Solution:
|
|
|
Question:
Regression line passes through sample means of x and y
Solution:
|
|
|
Question:
Show that the mean value of the estimated y is equal to the mean value of the actual y
Solution:
|
|
|
Question:
Assuming a simple linear regression model, show that the residuals are uncorrelated with predicted y
Solution:
|
|
|
Question:
What are deterministic and Statistical relationships? Distinguish between them.
Solution:
|
|
|
Question:
Distinguish between regression, causation and correlation
Solution:
|
|
|
Question:
Ordinary least squares
Solution:
|
|
|
Question:
Why OLS? What is the main property of an OLS estimator?
Solution:
|
|
|
Question:
Explain the first assumption of CLRM, i.e. 'Linearity of a model'
Solution:
|
|
|
Question:
Explain the second assumption of CLRM, i.e. ' X values are assumed to be fixed in a repeated sampling'
Solution:
|
|
|
Question:
Assumption 1: Model is linear in parameters
Solution:
|
|
|
Question:
Assumption 2: x values are fixed in repeated sampling
Solution:
|
|
|
Question:
Assumption 3: Error term has a zero mean
Solution:
|
|
|
Question:
Assumption 4: 'Homoscedasticity'
Solution:
|
|
|
Question:
Assumption 5:'No autocorrelation'
Solution:
|
|
|
Question:
Assumption 6: 'No correlation between error term and x's'
Solution:
|
|
|
Question:
Assumption 7: 'the number of observations n must be greater than the number of parameters to be estimated, k'
|
|