Basic Introductory Econometrics Solved Questions

Step by step solutions to 60+ Econometrics Questions. Ideal for university students who are new to Econometrics.

Last updated 2022-01-10 | 4.3

- Practice Questions - Simple and Multiple Linear Regression
- Mathematical Proofs - Simple Linear Regression
- Practice Questions - Functional Forms and Dummy Variables

What you'll learn

Practice Questions - Simple and Multiple Linear Regression

Mathematical Proofs - Simple Linear Regression

Practice Questions - Functional Forms and Dummy Variables

Practice Questions - Multicollinearity

Practice Questions - Heteroscedasticity and Autocorrelation

Feel much more confident in solving exam style questions

* Requirements

* Basics of Introductory Econometrics (Linear Regression)
* Statistics Basics - Random Variables
* Expected Values
* Variance
* Hypothesis Testing
* Confidence Intervals
* Knowledge of Elementary Calculus

Description

'Econometrics: Solved Questions and Mathematical Proofs' is a course for anyone studying Introductory Econometrics at University Level.

What other students are saying about this course?
''Clear and well organised course'' - Johnson Nyella
''This is surely very helpful. Whenever I have some doubt regarding concepts, I go through this course and the questions help a lot'' - Ananya Nath

Most of the times, even if students understand the Econometrics concepts, they struggle with connecting the dots. Consequently, they end up getting confused and make silly mistakes in the exam. This course can help you in building a strong foundation of Econometrics so that you could avoid that confusing state of mind and ace your exam.

This course contains solutions to exam style questions for the following topics:
•Hypothesis Testing and Confidence Intervals
•Simple Linear Regression
•Multiple Linear Regression
•Functional forms
•Dummy Variables
•Multicollinearity
•Heteroscedasticity
•Autocorrelation

This course comes with:

A 30 day money-back guarantee.
Support in the Q&A section - ask me if you get stuck!

I really hope you enjoy this course!

Shubham

Who this course is for:

Students who are looking to test their basic econometrics concepts
Students who know the concepts but struggle with practice questions

Course content

12 sections • 85 lectures

Working with STATA 2 lectures

Reading and Using STATA Regression Output - Part 1 Preview 18:03

Reading and Using STATA Regression Output - Part 2 (ANOVA Table) Preview 07:46

Hypothesis Testing and Confidence Interval - Practice Questions 5 lectures

List of Questions Preview 00:55

Question 1 - p-value and Null Hypothesis Preview 00:57

Question 2 - Which test statistic to use for hypothesis testing? Preview 00:22

Question 3 - Power of Test Preview 15:05

Question 4 - Interpretation of Confidence Interval Preview 00:25

Simple Linear Regression - Practice Questions 9 lectures

List of Questions Preview 00:06

Question 1 - Estimation of intercept when slope coefficient is zero Preview 04:13

Question 2 - Location of sample regression line Preview 05:36

Question 3 - Regression doesn't imply causation Preview 00:57

Question 4 - Calculate the value of intercept and slope estimator Preview 03:56

Question 5 - Confidence Interval,Interpretation of slope coefficient & R-squared Preview 20:23

Question 6 - Calculate OLS estimators, standard error of regression & R-squared Preview 14:24

Question 7 - What if expected value of the population error term is not zero? Preview 00:46

Share your experience Preview 00:29

Simple Linear Regression - Mathematical Proofs and Assumptions 15 lectures

Derivation of Intercept's Estimator using Ordinary Least Squares Method Preview 15:16

Derivation of Slope's Estimator using Ordinary Least Squares Method Preview 14:59

(Optional Lecture) Another Method to Solve for Intercept and Slope's estimator Preview 10:14

Different formulas to calculate Slope's estimator Preview 08:29

Different formulas to calculate Slope's estimator (Continued) Preview 11:03

Useful Results of OLS Preview 10:39

Useful Results of OLS (Continued) Preview 09:47

Assumptions of Classical Linear Regression Model (CLRM) Preview 11:27

Assumptions of CLRM (Continued) Preview 13:53

What is Gauss Markov Theorem? Preview 12:18

Gauss Markov Theorem: Slope Estimator is Linear Preview 12:38

Gauss Markov Theorem: Properties of new non-stochastic variable Preview 06:26

Gauss Markov Theorem: Slope Estimator is Unbiased Preview 13:48

Gauss Markov Theorem: Slope Estimator is Efficient Preview 16:53

Gauss Markov Theorem: Slope Estimator is Efficient (Continued) Preview 27:19

Multiple Linear Regression and Different Functional Forms - Practice Questions 20 lectures

List of Questions Preview 00:04

Question 1 - Relationship between adjusted R2 and R2 Preview 02:19

Question 2 - Does a high value of R2 means you have a good model? Preview 02:22

Question 3 - Can adjusted R2 be negative? Preview 01:24

Question 4 - Value of R2 on adding an insignificant and unimportant variable Preview 01:32

Question 5 - R2 in regression through origin models Preview 00:21

Question 6 - ANOVA Table and Adjusted R squared Preview 06:14

Question 7 - ANOVA Table, Test of Overall Significance and Adjusted R-Squared Preview 21:33

Question 8 - Interpretation of coefficients and Test of Overall Significance Preview 13:51

Question 9 - Interpretation of log-log model and Test of Overall Significance Preview 15:03

Question 10 - Restricted, Unrestricted Model and F-test Preview 12:33

Question 11 - Joint Hypothesis Test and Interpretation of lin-log model Preview 15:25

Question 12 - Working with log-log functional form and t-test Preview 11:00

Question 13 - Slope and Elasticity of various functional forms Preview 06:08

Question 14 - Hypothesis testing using the relationship between t and F Preview 22:18

Question 15 - Correlation between fitted values and residuals Preview 16:31

Question 16 - Numerator and Denominator Degrees of Freedom in F-test Preview 01:45

Question 17 - Interpretation of slope coefficient in log-lin model Preview 00:30

Question 18 - Interpretation of slope coefficient in lin-log model Preview 00:28

Question 19 - Don't take 'logs' on the variables under this scenario Preview 00:22

Dummy Variables - Practice Questions 5 lectures

List of Questions Preview 00:04

Question 1 - Basic Interpretation Preview 00:25

Question 2 - Dummy variable trap Preview 01:41

Question 3 - Dummy variable trap Preview 00:26

Question 4 - Dummy variables for 4 quarters Preview 00:00

Multicollinearity - Practice Questions 5 lectures

List of Questions Preview 00:04

Question 1 - Perfect Collinearity (Exact Linear Relationship) among regressors Preview 08:43

Question 2 - Perfect Collinearity (Exact Linear Relationship) among regressors Preview 06:33

Question 3 - Perfect Collinearity (Exact Linear Relationship) among regressors Preview 00:00

Question 4 - High Multicollinearity Preview 00:12

Heteroscedasticity - Practice Questions 4 lectures

Question 1 - Consequences of Heteroscedasticity Preview 01:37

Question 2 - White's test for Heteroscedasticity Preview 14:51

Question 3 - Transform the regression equation to deal with Heteroscedasticity Preview 17:15

Question 4 - Transform the regression equation to deal with Heteroscedasticity Preview 13:05

Autocorrelation - Practice Questions 6 lectures

Question 1 - Test 1st Order Autocorrelation (Durbin-Watson d-test) Preview 07:05

Question 2 - Assumptions underlying Durbin Watson d-test Preview 01:05

Question 3 - Test 1st Order Autocorrelation (Durbin-Watson d-test) Preview 05:39

Question 4 - Presence of Positive Autocorrelation? Preview 00:28

Question 5 - Test 1st Order Autocorrelation (Durbin-Watson d-test) Preview 00:00

Question 6 - Testing for Autocorrelation - Breusch Godfrey Test Preview 13:04

Functional Form Misspecification and Proxy Variables - Practice Questions 2 lectures

List of Questions (Multiple Choice Questions) Preview 00:52

MCQ Solutions Preview 01:39

Miscellaneous Questions - (Not so straightforward ones) 11 lectures

Question 1 Preview 06:01

Question 2 Preview 04:56

Question 3 Preview 04:52

Question 4 Preview 04:11

Question 5 Preview 06:52

Question 6 - Transforming Dependent & Independent variable by adding a constant Preview 26:17

Question 7 - Changing the values of dummy variable and statistical significance Preview 29:33

Question 8: Covariance between sample error and independent variable Preview 23:11

Question 9 Preview 22:09

Question 10 Preview 15:16

Question 11 Preview 17:15

Bonus Section 1 lectures