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ECS3706 Assignment 2 (ANSWERS) Semester 2 2024 - DISTINCTION GUARANTEED

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Well-structured ECS3706 Assignment 2 (ANSWERS) Semester 2 2024 - DISTINCTION GUARANTEED. (DETAILED ANSWERS - DISTINCTION GUARANTEED!)..1. Explain why econometricians must know and understand the classical linear regression assumptions. (4) 2. Explain the role of the Central Limit Theorem in statistical hypothesis testing (3) 3. You came across a recent (24 April 2024) News24 article, "The DRC had the world’s biggest military spend increase last year—and South Sudan was second.” You are worried about the increasing defence expenditure of African countries at war and its impact on economic growth. You collect data for one of the countries from 1980 to 2023, and you estimate the following model: Dependent Variable: Gross Domestic Product (GDP) Explanatory Variables Coefficient Standard Error Constant 0.0486 0.0242 Lagged GDP (GDPt-1) 0.6527 0.0813 Capital Investment (CI) -0.0025 0.0012 Labour (L) 0.0246 0.0074 Defence expenditure (DE) 0.5883 0.0648 Inflation (INF) 0.0272 0.0152 Recession Dummy (REC) REC = 1: Recession REC=0: No recession -0.0441 0.0254 R-Squared 0.894 Dw-d = 1.654 F-Stat = 27.84 a. What are the expected signs of the variables in this model? Explain. (6) b. Interpret the estimated coefficients of CI, INF and REC (6) c. Test the estimated coefficients for statistical significance at the 5% level of significance (12) d. Test for the joint significance of coefficients using the F-Test (4) e. Construct a 95% and 99% Confidence Interval for the coefficient of Defence Expenditure (DE). Which one is wider and why? (6) f. Is there serial correlation in this model? (4) g. Regardless of your answer in part d, discuss the remedies of serial correlation. (5)

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ECS3706
Assignment 2 Semester 2 2024
Unique Number: 399706
Due Date: September 2024

QUESTION 1

Why econometricians must know and understand the classical linear regression
assumptions:

Econometricians must understand the classical linear regression assumptions because they
form the foundation of Ordinary Least Squares (OLS) estimation, ensuring the accuracy, validity,
and reliability of the regression results. These assumptions help in:

• Ensuring unbiased estimators: When assumptions like zero correlation between error

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