Basic Econometrics Research Report Group Assignment

Assignment 

This is a group assignment where you are allowed to work in groups of 1-4 other students. All group members will receive the same marks for the assignment, conditional that all group members participate equally. If a group member does not participate in the assignment, no marks can be awarded, if a group member does not participate equally, only partial marks can be awarded.

You must submit an electronic copy of your assignment in Canvas in pdf, doc or docx format along with your R-code copied into assignment. Hard copies will not be accepted. Show your calculations (if any) as well as answering the questions in clear full sentences. The number of tables, graphs, calculations given in parentheses after each question are a guide.

Questions

1. Please model the drivers of infant mortality rate (per 1000 live births) using R:

a) Include a minimum of 5 (five) explanatory variables in the regression - do not use region please- equation and provide a scatter plot of your dependent and independent variables (5 scatter plots).

When modelling, explain in detail each of your choices with respect to:

• Economic or common sense behind the model - why do you pick this variable? (1 x 5 marks)
• Multicollinearity are the independent variables multicollinear? (0.5 x 5 marks)
• Functional form specification- potential nonlinear relationships, eg: log-linear or quadratic relationships. Explain why you use a level or logarithmic form of a variable. (0.5 x 5 marks)

in writing. You will be graded on model accuracy in this section.

b) Interpret the coefficients on 5 explanatory variables. Describe if the coefficients are elasticities or semi-elasticities, or simple level coefficients.

c) Interpret the statistical significance of these coefficients using the p-values OR the t-stat.

d) Test for heteroscedasticity in R using the White test and copy below the results. Interpret the results of the White test.1 Table & Explanation

e) Present the results from a) using HC robust errors! Did any of the standard errors change significantly? 1 Table & Explanations

f) Regional differences play a significant role in electricity consumption. Please add “region” to the regression in part (A) and present the results below.

• Analyse the coefficients of the regional dummy categories. How do the other coefficients change as a result? 
• Should you include the regional dummy variable in the regression? Present your test results and analysis.

g) Add fertility rates to your original regression in (F) if it does not include “fertility rate”, and present the regression results below. (If it does include just copy the regression from F).

• Explain why including fertility rates can cause endogeneity in the regression

h) Download the 2021 value for your chosen instrument from the World Bank, merge with our current dataset and run an IV regression. Show the regression results of the IV regression

• Explain the outcome of the IV regression and compare the IV and OLS coefficients (magnitude and statistical significance) on the endogenous variable. 

Assessment Summary and Mentor-Guided Approach

This group-based econometrics assignment required students to model the drivers of infant mortality rate (per 1000 live births) using R programming. Each group (1–4 students) was expected to collaboratively conduct statistical modelling, data interpretation, and econometric analysis, ensuring all members contributed equally.

The key components to be covered included:

• Developing a regression model with at least five explanatory variables (excluding “region”) and presenting scatter plots.
• Explaining the rationale for variable selection based on economic reasoning and common sense.
• Testing for multicollinearity among independent variables and justifying the functional form of each.
• Interpreting coefficients (elasticities, semi-elasticities, or level coefficients) and assessing statistical significance using p-values or t-statistics.
• Testing for heteroscedasticity through the White test and applying HC robust standard errors.
• Introducing regional dummy variables to analyse regional differences and their effect on model coefficients.
• Addressing endogeneity by adding fertility rate as an explanatory variable and explaining its implications.
• Performing an Instrumental Variable (IV) regression, merging 2021 data from the World Bank, and comparing IV and OLS outcomes.

The overall objective of this assignment was to strengthen students’ ability to apply econometric concepts in R, interpret model outputs critically, and make data-driven conclusions consistent with economic reasoning.

Step-by-Step Academic Mentor Guidance

The academic mentor guided the student systematically through each stage of the assignment to ensure conceptual clarity, analytical precision, and appropriate interpretation of statistical results.

Step 1: Understanding the Objective and Dataset

The mentor began by helping the student comprehend the goal identifying economic, social, and demographic factors influencing infant mortality rates. The mentor explained how to select a reliable dataset, check variable types, and prepare data for regression analysis in R.

Step 2: Variable Selection and Economic Justification

Students were guided to select five relevant explanatory variables such as GDP per capita, education rate, access to healthcare, immunization coverage, and sanitation levels. The mentor emphasized explaining each variable’s economic rationale, e.g., higher education and healthcare spending typically reduce infant mortality.

Step 3: Checking Multicollinearity and Choosing Functional Forms

Using R functions like cor() and VIF tests, the mentor demonstrated how to identify multicollinearity. The discussion included determining appropriate functional forms (log-linear or quadratic) to capture realistic variable relationships for example, using a logarithmic transformation for GDP to interpret results in terms of elasticity.

Step 4: Running the Regression and Interpreting Coefficients

The mentor guided students through executing the regression model in R using lm() and interpreting coefficients. The student learned to distinguish between elasticity-based and level-based interpretations, and how to assess statistical significance via p-values or t-statistics.

Step 5: Diagnostic Testing White Test and Robust Errors

To ensure model reliability, the mentor demonstrated how to conduct the White test for heteroscedasticity and interpret its results. Upon detecting any heteroscedasticity, the mentor explained how to apply HC robust standard errors and compare changes in standard errors.

Step 6: Including Regional Variables and Analysing Impact

The next step involved adding regional dummy variables to capture geographical differences in infant mortality. The mentor explained how dummy variables affect other coefficients and guided students in deciding whether to retain the regional variable based on statistical tests and economic logic.

Step 7: Addressing Endogeneity Adding Fertility Rate

Recognizing that fertility rate might be endogenous, the mentor introduced the concept of endogeneity bias. The student learned how including this variable could distort estimates, and why an Instrumental Variable approach is needed.

Step 8: Performing IV Regression and Comparative Analysis

The mentor guided students in obtaining an appropriate instrument from the World Bank dataset, merging it, and running the IV regression using R commands such as ivreg(). Finally, the mentor assisted in comparing the IV vs OLS results, explaining differences in magnitude and significance of coefficients, emphasizing the implications for causal inference.

Final Outcome and Learning Achievements

By the end of the assessment, the student successfully developed an econometric model that identified the key determinants of infant mortality while critically interpreting all results. The process enhanced the student’s ability to:

• Apply R programming for statistical modelling.
• Understand and test classical regression assumptions.
• Address multicollinearity, heteroscedasticity, and endogeneity.
• Interpret and present econometric findings clearly for academic and applied purposes.
• Strengthen analytical and teamwork skills in line with course learning objectives.

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