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ECS4863 Assignment 1 (100% COMPLETE ANSWERS) 2025 - DUE 16 May 2025

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Advanced Econometrics - ECS4863 Assignment 1 2025 - DUE 16 May 2025 ;100 % TRUSTED workings, Expert Solved, Explanations and Solutions. For assistance call or W.h.a.t.s.a.p.p us on ...(.+.2.5.4.7.7.9.5.4.0.1.3.2)........... ASSIGNMENT 01 DUE DATE: Friday, 16 May 2025, 11:00 PM Dear Student, INSTRUCTIONS Please do not use this document as a template on which to fill in your answers. Submit your answers on a separate document and make sure to check your numbering. Also remember to include your name and student number on the front page of your submission, and preferably on each page of your answer sheet. All the assignments for ECS4863 are strictly individual assignments! This means you are NOT allowed to work in groups or make use of any human assistance. Indications of plagiarism and/or cheating will be dealt with according to the University’s Disciplinary Processes. All the best with the assignment! This assignment covers: Lesson 1, Time series analysis and introductory lessons. Question 1: (15 marks) 1.1 Explain the concept of omitted variable bias and distinguish between positive and negative bias 1.2 Explain in your own words how you test serial correlation with strictly exogenous variables 1.3 Explain, in your own words, the concept of heteroscedasticity and implications for inferences in econometrics 1.4 Explain in your own words what is meant by the following: a) Covariance stationary process b) Sequential exogeneity Question 2: (5 marks) In this question you need to gather and analyze time series data for a country (other than South Africa!)

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ECS4863
ASSIGNMENT 1 2025

UNIQUE NO.
DUE DATE: 16 MAY 2025

, ECS4863

Assignment 1 2025




Unique Number:

Due Date: 16 May 2025

Advanced Econometrics

Question 1

1.1 Explain the concept of omitted variable bias and distinguish between positive
and negative bias. (4 Marks)

Omitted Variable Bias (OVB) occurs in regression analysis when a relevant variable
that influences both the dependent variable and one or more independent variables is
left out of the model. This omission violates the assumption that the error term is
uncorrelated with the regressors, leading to biased and inconsistent estimates of the
coefficients.

For OVB to occur:

1. The omitted variable must be a determinant of the dependent variable.
2. The omitted variable must be correlated with one or more included independent
variables.

Positive Bias arises when the omitted variable is positively correlated with both the
dependent variable and the included independent variable. This leads to an
overestimation of the effect of the included variable.

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