Econometrics: Solved Questions and Mathematical Proofs

Name: Econometrics: Solved Questions and Mathematical Proofs
Rating: 4.5 (100 reviews)

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

Created byShubham Kalra

Last updated 9/2023

English

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

Course content

11 sections • 91 lectures • 12h 47m total length

List of Questions0:55
Question 1 - p-value and Null Hypothesis0:57
Question 2 - Which test statistic to use for hypothesis testing?0:22
Question 3 - Power of Test15:05
Question 4 - Interpretation of Confidence Interval0:25

List of Questions0:06
Question 1 - Estimation of intercept when slope coefficient is zero4:13
Question 2 - Location of sample regression line5:36
Question 3 - Regression doesn't imply causation0:57
Question 4 - Calculate the value of intercept and slope estimator3:56
Question 5 - Confidence Interval,Interpretation of slope coefficient & R-squared20:23
Question 6 - Calculate OLS estimators, standard error of regression & R-squared14:24
Question 7 - What if expected value of the population error term is not zero?9:22
Question 8 (Newly Added)8:10
Question 9 (Newly Added)26:11
Question 10 (Newly Added) - Working with R2 formulas7:09
Share your experience0:30

Derivation of Intercept's Estimator using Ordinary Least Squares Method15:16
Derivation of Slope's Estimator using Ordinary Least Squares Method14:59
(Optional Lecture) Another Method to Solve for Intercept and Slope's estimator10:14
Different formulas to calculate Slope's estimator8:29
Different formulas to calculate Slope's estimator (Continued)11:03
Useful Results of OLS10:39
Useful Results of OLS (Continued)9:47
Assumptions of Classical Linear Regression Model (CLRM)11:27
Assumptions of CLRM (Continued)13:53
What is Gauss Markov Theorem?12:18
Gauss Markov Theorem: Slope Estimator is Linear12:38
Gauss Markov Theorem: Properties of new non-stochastic variable6:26
Gauss Markov Theorem: Slope Estimator is Unbiased13:48
Gauss Markov Theorem: Slope Estimator is Efficient16:53
Gauss Markov Theorem: Slope Estimator is Efficient (Continued)27:19

List of Questions0:04
Question 1 - Relationship between adjusted R2 and R22:19
Question 2 - Does a high value of R2 means you have a good model?2:22
Question 3 - Can adjusted R2 be negative?1:24
Question 4 - Value of R2 on adding an insignificant and unimportant variable1:32
Question 5 - R2 in regression through origin models0:21
Question 6 - ANOVA Table and Adjusted R squared6:14
Question 7 - ANOVA Table, Test of Overall Significance and Adjusted R-Squared21:33
Question 8 - Interpretation of coefficients and Test of Overall Significance13:51
Question 9 - Interpretation of log-log model and Test of Overall Significance15:03
Question 10 - Restricted, Unrestricted Model and F-test12:33
Question 11 - Joint Hypothesis Test and Interpretation of lin-log model15:25
Question 12 - Working with log-log functional form and t-test11:00
Question 13 - Slope and Elasticity of various functional forms6:08
Question 14 - Hypothesis testing using the relationship between t and F22:18
Question 15 - Correlation between fitted values and residuals16:31
Question 16 - Numerator and Denominator Degrees of Freedom in F-test1:45
Question 17 - Interpretation of slope coefficient in log-lin model0:30
Question 18 - Interpretation of slope coefficient in lin-log model0:28
Question 19 - Don't take 'logs' on the variables under this scenario0:22
Question 20 (Newly Added) - Calculate value of t-test statistic5:45
Question 21 (Newly Added) - Calculate value of F-test statistic8:29

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
• p-value
• Power of Test
Simple Linear Regression
• Calculation of OLS estimators
• Interpretation of OLS estimators
• Standard Error of Regression (SER)
• R-squared
Multiple Linear Regression and Different Functional Forms
• Adjusted R2 and R2
• F-test and t-test
• Interpretation of OLS estimators in different functional forms
Dummy Variables
• Working with Dummy Variables and interpretaion
• Dummy Variable Trap
Multicollinearity
• Perfect multicollinearity
• Imperfect multicollinearity

AND MANY MORE TOPICS!!

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

Econometrics: Solved Questions and Mathematical Proofs

What you'll learn

Explore related topics

Course content

Hypothesis Testing and Confidence Interval - Practice Questions5 lectures • 18min

Simple Linear Regression - Practice Questions12 lectures • 1hr 41min

Simple Linear Regression - Mathematical Proofs and Assumptions15 lectures • 3hr 15min

Multiple Linear Regression and Different Functional Forms - Practice Questions22 lectures • 2hr 46min

Dummy Variables - Practice Questions8 lectures • 35min

Multicollinearity - Practice Questions5 lectures • 16min

Heteroscedasticity - Practice Questions4 lectures • 47min

Autocorrelation - Practice Questions6 lectures • 27min

Functional Form Misspecification and Proxy Variables - Practice Questions2 lectures • 3min

Miscellaneous Questions - (Not so straightforward ones)11 lectures • 2hr 41min

Requirements

Description

Who this course is for: