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DSE 6 : Advanced Econometrics [ECON 036] for Economics (H) Semester V

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Semester 5 Subjects

►Game Theory & Strategic Interactions

►Economic Growth & Business Cycles

►Introductory Development Economics

►DSE – Advanced Econometrics

►DSE – Law and Economics

►DSE – Environmental Economics

►DSE – Open Economics Macroeconomics

►DSE – Public Economics

The DSE 6 : Advanced Econometrics [ECON 036] Course for BA (Hons) Economics Semester V, Delhi University has been taught by Mr. Dheeraj Suri. The Video Lectures are based upon the books prescribed by the University of Delhi. The Duration of Video Lectures is approximately 55 Hours.

Access of Video Lectures is provided on any one of the following devices:

Windows Computer or Laptop, or

Android Phone or Tablet, or

Apple Iphone or Ipad, or

Apple Macbook,

till end of Semester V Exams.

Course Fee : Rs. 7000

Fee Structure :

	Single Device	Two Devices
Single Subject	Rs. 7000	Rs. 12,000
Two Subjects	Rs. 13,500	Rs. 23,000
Three Subjects	Rs. 20,000	Rs. 33,000
Four Subjects	Rs. 26,000	Rs. 42,000

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Click Here for Reading List PDF

You will get :

►Full Course Video Lectures

►Video Lectures Cover Theory Portions Exhaustively + Complete Solutions of Back Questions of Readings + Solutions of Previous Years Papers + Large Number of Numericals

►Complete Study Material (PDF Notes) which includes Concepts, Previous Year Questions, Numerical Questions, MCQ’s and Important Questions

►Live Online Doubts Sessions with Expert Faculty (at least twice a week) for resolution of Doubts

►Online Discussion Forum to Post Your Queries to Discuss with Faculty & other fellow Students

►Mock Tests at the Website for regular assessment and progress tracking

►Comprehensive Coverage of Syllabus and Exam Oriented Preperation

This online coaching platform aims to provide a supportive and engaging learning environment for students to achieve academic success and excel in their Economics Honours program.

Demo Lectures for Advanced Econometrics

Course Introduction

Linear Regression in Matrix Form

Panel Data

These Lectures are Only for Demo.

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Contact us at +91 9899192027 for details

Demo Quiz

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Logit Probit Models Test #1

1. Please Read the Questions and all the options Carefully, Before Selecting Your Choice.
2. You are not Allowed to edit your answers after submission.
3. The Paper has Twenty Questions
4. Time Allowed is 20 Minutes.
5. It is necessary to enter your valid Email id to attempt this test.

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1 / 20

In a logit model, what does the log-odds represent?

A) The probability of the outcome

B) The logarithm of the odds of the outcome occurring

C) The odds of the outcome not occurring

D) The linear predictor

2 / 20

What is the primary application of the probit model in econometrics?

A) Predicting continuous outcomes

B) Predicting binary outcomes

C) Predicting categorical outcomes

D) Predicting time series data

3 / 20

Which function is used to transform the linear predictor in the logit model?

A) Logarithm function

B) Cumulative normal distribution function

C) Logit (log-odds) function

D) Exponential function

4 / 20

In a probit model, what does $Φ (Xβ)$ represent?

A) The probability density function of the normal distribution

B) The cumulative distribution function of the normal distribution

C) The log-odds

D) The error term

5 / 20

When comparing the logit and probit models, which statement is correct regarding their predicted probabilities?

A) Predicted probabilities from both models range from 0 to 1.

B) Predicted probabilities from the logit model range from -1 to 1, while those from the probit model range from 0 to 1.

C) Predicted probabilities from the logit model range from 0 to ∞, while those from the probit model range from - ∞ to ∞

D) Predicted probabilities from both models range from - ∞ to ∞

6 / 20

What is a common application for both logit and probit models?

A) Predicting stock prices

B) Modeling the likelihood of default on loans

C) Estimating GDP growth

D) Forecasting weather patterns

7 / 20

Which of the following is NOT an assumption of the logit model?

A) The error terms are normally distributed

B) The relationship between the logit of the probability and the predictor variables is linear

C) The outcome variable is binary

D) The observations are independent

8 / 20

Which method is commonly used to estimate the parameters of a probit model?

A) Ordinary Least Squares (OLS)

B) Maximum Likelihood Estimation (MLE)

C) Method of Moments

D) Generalized Method of Moments (GMM)

9 / 20

Which test is commonly used to assess the goodness-of-fit for a logit model?

A) Durbin-Watson test

B) Chi-square goodness-of-fit test

C) Augmented Dickey-Fuller test

D) Breusch-Pagan test

10 / 20

What is the odds ratio in the context of a logit model?

A) The ratio of the probability of success to the probability of failure

B) The log of the probability of success

C) The inverse of the log-odds

D) The ratio of the predicted value to the actual value

11 / 20

Which of the following statements is true about the probit model?

A) It assumes the dependent variable is normally distributed

B) It uses a logistic function to model the probability

C) It uses the cumulative normal distribution function to model the probability

D) It can be estimated using Ordinary Least Squares (OLS)

12 / 20

What is a key difference between the logit and probit models?

A) The logit model uses a cumulative normal distribution, while the probit model uses a logistic distribution.

B) The logit model uses a logistic distribution, while the probit model uses a cumulative normal distribution.

C) The logit model is used for binary outcomes, while the probit model is used for continuous outcomes.

D) The logit model can be estimated using Maximum Likelihood Estimation, while the probit model cannot.

13 / 20

Which method is commonly used to estimate the parameters of a logit model?

A) Ordinary Least Squares (OLS)

B) Maximum Likelihood Estimation (MLE)

C) Method of Moments

D) Generalized Method of Moments (GMM)

14 / 20

In the probit model, what is the range of the predicted probabilities?

A) − ∞ to ∞

B) 0 to 1

C) -1 to 1

D) 0 to ∞

15 / 20

What is the purpose of the link function in a logit model?

A) To transform the dependent variable

B) To ensure the predictions lie within the range 0 to 1

C) To standardize the independent variables

D) To maximize the likelihood function

16 / 20

Which assumption is common to both logit and probit models?

A) Homoscedasticity of errors

B) Linearity of the relationship between predictors and the outcome variable

C) Independence of irrelevant alternatives

D) Normality of the predictors

17 / 20

Which function is used to transform the linear predictor in the probit model?

A) Logarithm function

B) Cumulative normal distribution function

C) Logit (log-odds) function

D) Exponential function

18 / 20

In the logit model, what is the range of the predicted probabilities?

A) − ∞ to ∞

B) 0 to 1

C) -1 to 1

D) 0 to ∞

19 / 20

Which of the following statements is true about the logit and probit models?

A) Both models are used for predicting categorical outcomes with more than two categories.

B) Both models assume a linear relationship between the dependent variable and independent variables.

C) Both models are used for binary outcome prediction.

D) Both models are estimated using Ordinary Least Squares (OLS).

20 / 20

What is the logit model primarily used for in econometrics?

A) Predicting continuous outcomes

B) Predicting binary outcomes

C) Predicting categorical outcomes

D) Predicting time series data

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Demo PDF Notes

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Exam Pattern

The Question Paper will be of 90 Marks

►One Compulsory question of 10 Marks will be from Unit II

►Five Questions of 20 Marks each will be from Unit III, IV & V out of which students will be required to attempt four

►Internal assessment would comprise two class tests of 12 Marks each

►The continuous assessment would comprise of 35 Marks tutorial assignment and individual class projects/group projects, where data is collected and analysed.

►Tutorial attendence will carry 5 Marks

Reading List

Click Here for Reading List of Advanced Econometrics

►Wooldridge, J. (2014). Introduction to econometrics: A modern approach, 5th ed. Cengage Learning.

►James Stock and Mark Watson, Introduction to Econometrics, 4th Edition, 2019, Pearson.

Course Content of our Video Lectures

Lectures are Strictly as per Latest Syllabus for UGCF 2024

Unit I : Stages in Empirical Econometric Research

Chapter 1 : The Nature of Econometrics

Number Video Lectures : 2

Duration of Video Lectures : 51 Minutes

Based Upon Chapter 1 Wooldridge J.

Topics Covered

►What is Econometrics,

►Steps in Empirical Economic Analysis,

►The Structure of Economic Data, Cross Sectional Data, Time Series Data, Pooled Cross Sections, Panel or Longitudinal Data,

►Causality & the Notion of Ceteris Paribus in Econometric Analysis,

Unit II : The Linear Regression Model

Chapter 2 : Simple Regression Model

Number Video Lectures : 2

Duration of Video Lectures : 51 Minutes

Based Upon Chapter 2 Wooldridge J.

Topics Covered

►Two Variable Linear Regression Model,

►Population Regression Function, Sampke Regression Function,

►Assumptions of CLRM, Method of Ordinary Least Squares,

►Goodness of Fit,

►Gauss Markov Theorem,

►Effect of Change of Scale on OLS Estimators and their Variances,

Chapter 3 : Multiple Regression Model

Number Video Lectures : 2

Duration of Video Lectures : 51 Minutes

Based Upon Chapter 3 & Chapter 4 Wooldridge J.

Topics Covered

►Regression Model with Two Independent Variables, Regression Model with K Independent Variables,

►Obtaining the OLS Estimation, Interpreting the OLS Regression Equation,

►Goodness of Fit,

►Sampling Distributions of the OLS Estimators,

►Confidence Intervals and Hypothesis Testing of OLS Estimators,

►Overall Test of Significance,

Chapter 4 : Functional Form

Number Video Lectures : 2

Duration of Video Lectures : 51 Minutes

Based Upon Chapter2, Chapter 3 & Chapter 4 Wooldridge J.

Topics Covered

►Log-Linear Regression Model,

►Log-lin Regression Model, Lin-log Regression Model,

►Regression Through Origin,

Chapter 5 : Model Specification Bias

Number Video Lectures : 2

Duration of Video Lectures : 51 Minutes

Based Upon Chapter 4 Wooldridge J.

Topics Covered

►Under-Fitting Bias,

►Over-fitting Bias,

Chapter 6 : The Linear Regression Model in Matrix Form

Number Video Lectures : 5

Duration of Video Lectures : 170 Minutes

Based Upon Appendix E Wooldridge J.

Topics Covered

►Matrix Representation of Linear Regression,

►OLS in Matrix Form,

►Gauss Markov Theorem in Matrix Form,

Unit III : Limited Dependent Variables

Chapter 7 : Limited Dependent Variable

Number Video Lectures : 5

Duration of Video Lectures : 135 Minutes

Based Upon Chapter 17 Wooldridge J. & Chapter 11 Stock & Watson

Topics Covered

►Linear Probability Models,

►Logit & Probit Models for Binary Response,

►Interpreting Logit & Probit Estimates,

►The Tobit Model, Interpreting Tobit Estimates,

Unit IV : Dynamic Econometric Models

Chapter 8 : Instrumental Variables

Number Video Lectures : 4

Duration of Video Lectures : 90 Minutes

Based Upon Chapter 15 Wooldridge J.

Topics Covered

►Introducing Instrumental Variable (IV),

►Statistical Inference with IV Estimator,

►Properties of IV with a Poor Instrumental Variable,

►IV Estimation of the Multiple Regression Model,

►Two Stage Least Squares, Multicollinearity and 2SLS,

►IV Solutions to Errors-in-Variables Problems,

►Testing for Endogeneity & Testing Overidentifying Restrictions,

Chapter 9 : Simultaneous Equations Models

Number Video Lectures : 4

Duration of Video Lectures : 90 Minutes

Based Upon Chapter 15 Wooldridge J.

Topics Covered

►Simultaneous Equations Introduction,

►Simultaneity Bias in OLS,

►Identifying and Estimating a Structural Equation,

Unit V : Introduction to Econometric Software

Chapter 10 : Panel Data

Number Video Lectures : 4

Duration of Video Lectures : 90 Minutes

Based Upon Chapter 13 Wooldridge J.

Topics Covered

►Pooling Independent Cross Section Across Time,

►The Chow Test for Structural Change,

►Policy Analysis with Pooled Cross Sections,

►Two Period Panel Data Analysis, Organizing Panel Data,

►Policy Analysis with Two Period Panel Data,

Chapter 11 : Dynamic Econometric Models

Number Video Lectures : 1

Duration of Video Lectures : 60 Minutes

Based Upon Chapter 14 Wooldridge J.

Topics Covered

►Fixed Effects Transformation,

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Economics (H) Semester V [ UGCF 2024 ]

Video Lectures, PDF Notes, Mock Tests & Live Doubt Sessions are offered for the following Subjects for BA (Hons) Economics Semester V, Delhi University

Welcome to Prime Academy

For getting access of recorded video lectures of

DSE 6 : Advanced Econometrics [ECON 036] for Economics (H) Semester V

you need to subscribe our course.

If you are not registered at our website then Register Here (After registration please inform us at +91 9899 192027 to get the access)

If you are already registered and have Got the access then Login to watch the Lectures

Semester 5 Subjects

Fee Structure :

You will get :

►Full Course Video Lectures

►Video Lectures Cover Theory Portions Exhaustively + Complete Solutions of Back Questions of Readings + Solutions of Previous Years Papers + Large Number of Numericals

►Complete Study Material (PDF Notes) which includes Concepts, Previous Year Questions, Numerical Questions, MCQ’s and Important Questions

►Live Online Doubts Sessions with Expert Faculty (at least twice a week) for resolution of Doubts

►Online Discussion Forum to Post Your Queries to Discuss with Faculty & other fellow Students

►Mock Tests at the Website for regular assessment and progress tracking

►Comprehensive Coverage of Syllabus and Exam Oriented Preperation

This online coaching platform aims to provide a supportive and engaging learning environment for students to achieve academic success and excel in their Economics Honours program.

Demo Lectures for Advanced Econometrics

Course Introduction

Linear Regression in Matrix Form

Panel Data

These Lectures are Only for Demo.

For Complete Course Video Lectures You Need to Subscribe Our Course

Contact us at +91 9899192027 for details

Demo Quiz

For Complete Mock Test Series of Advanced Econometrics, You need to subscribe our course.

Contact us at +91 9899192027

Demo PDF Notes

Click here for Demo PDF of Study Material

For Complete PDF Notes of Advanced Econometrics

Contact us at +91 9899192027

Exam Pattern

The Question Paper will be of 90 Marks

►One Compulsory question of 10 Marks will be from Unit II

►Five Questions of 20 Marks each will be from Unit III, IV & V out of which students will be required to attempt four

►Internal assessment would comprise two class tests of 12 Marks each

►The continuous assessment would comprise of 35 Marks tutorial assignment and individual class projects/group projects, where data is collected and analysed.

►Tutorial attendence will carry 5 Marks

Reading List

Click Here for Reading List of Advanced Econometrics

Recommended Readings

►Wooldridge, J. (2014). Introduction to econometrics: A modern approach, 5th ed. Cengage Learning.

►James Stock and Mark Watson, Introduction to Econometrics, 4th Edition, 2019, Pearson.

Course Content of our Video Lectures

Lectures are Strictly as per Latest Syllabus for UGCF 2024

Unit I : Stages in Empirical Econometric Research

Chapter 1 : The Nature of Econometrics

Number Video Lectures : 2

Duration of Video Lectures : 51 Minutes

Based Upon Chapter 1 Wooldridge J.

Topics Covered

►What is Econometrics,

►Steps in Empirical Economic Analysis,

►The Structure of Economic Data, Cross Sectional Data, Time Series Data, Pooled Cross Sections, Panel or Longitudinal Data,

►Causality & the Notion of Ceteris Paribus in Econometric Analysis,

Unit II : The Linear Regression Model

Chapter 2 : Simple Regression Model

Number Video Lectures : 2

Duration of Video Lectures : 51 Minutes

Based Upon Chapter 2 Wooldridge J.

Topics Covered

►Two Variable Linear Regression Model,

►Population Regression Function, Sampke Regression Function,

►Assumptions of CLRM, Method of Ordinary Least Squares,

►Goodness of Fit,

►Gauss Markov Theorem,

►Effect of Change of Scale on OLS Estimators and their Variances,

Chapter 3 : Multiple Regression Model

Number Video Lectures : 2

Duration of Video Lectures : 51 Minutes

Based Upon Chapter 3 & Chapter 4 Wooldridge J.

Topics Covered

►Regression Model with Two Independent Variables, Regression Model with K Independent Variables,

►Obtaining the OLS Estimation, Interpreting the OLS Regression Equation,

►Goodness of Fit,

►Sampling Distributions of the OLS Estimators,

►Confidence Intervals and Hypothesis Testing of OLS Estimators,

►Overall Test of Significance,

Chapter 4 : Functional Form

Number Video Lectures : 2