ECON 1607: Economic and Quantitative Analysis

Assignment Overview

SECTION A

[This section includes two mandatory questions. Please adhere to the instructions and word limits provided for each question and its sub-parts.]

Question 1

[7 marks]
[There are 8 potential buyers (A, B, C…, H) for a bottle of premium Yarra Valley Pinot Noir. Each buyer’s reservation price (or willingness to pay) is known to the seller, as shown below. The constant marginal cost per bottle of wine is $28 to the seller. Let us ignore fixed cost.]

Buyers and their reservation prices:
A – $40
B – $38
C – $36
D – $34
E – $32
F – $30
G – $28
H – $26

a) [What type of market structure is described in the scenario? Explain your reasoning in a sentence. (1 mark)]

b) [Using the information provided, draw the demand curve for the seller of premium Yarra Valley Pinot Noir. (3 marks)]

c) [Calculate the seller’s Total Revenue (TR), and Marginal Revenue (MR) at different quantities of wine bottles sold. Present your calculations in a table format. (2 marks)]

d) [If the seller must charge the same price to all buyers, what is the profit-maximizing price and quantity? (1 mark)]

Question 2

[8 marks]
[Coronavirus pandemic and resultant shutdown measures to contain it have plunged the economies around the world including Australia into contraction. Assuming Australian economy was in long run equilibrium in the aggregate demand and supply (AD-AS) model before the pandemic (consequent shutdown) started, answer the questions below:]

a) [Assuming the contraction in the Australian economy is primarily driven by demand-side factors, illustrate the short-run effects using the AD-AS model in a diagram. In 100 words or fewer, explain the potential two demand-side factors that could lead to this contraction. (2+1 = 3 marks)]

b) [Now, assuming that the contraction in Australian economy is mainly driven by supply side factors, show the short-run effects of this using the AD-AS model in a different diagram (not the one from part a). In 100 words or fewer, discuss two supply-side factors that could lead to this contraction. (2+1 = 3 marks)]

c) [Which of the above mentioned two recession scenarios can be better handled by policy makers (the government and the central bank)? and why? (Explain in 50 or less words). (2 marks)]

SECTION B

[You must use Microsoft Excel and the Data Analysis ToolPak for this section. Submit a well-organized Excel file to verify your analysis and answers.]

• This section will not be graded without the required Excel file.

• A penalty of 2 marks will be applied if the Excel file is unclear or poorly organized.

Question 1

[19 marks]
[In the file "Assessment 3 Data.xls" (available on Canvas), you’ll find information on per capita GDP and CO2 emissions for 186 countries in the worksheet “Data (Across Countries)”. Choose any 30 countries from this dataset and complete the following analysis:]

a. [Concentrate on the CO2 variable. First, create a histogram in Excel to show the distribution of CO2 emissions for your selected countries. You may set the class width as you prefer. Next, use the Excel Data Analysis Tool Pack to generate descriptive statistics for the CO2 variable. Include the Excel graph and table here. In no more than 150 words, describe the key characteristics of the histogram and the descriptive statistics. (2+1+2 = 5 marks)]

b. [Next, analyse the relationship between CO2 emissions per capita and GDP per capita for your selected countries by creating a scatter plot in Excel. Include the scatter plot here and provide a summary of your main observations about the relationship between these two variables in 50 words or less. (2+1 = 3 marks)]

c. [Using (the same) data for your selected 30 countries, carry out simple linear regression to estimate the relationship between CO2 emissions (Y) and GDP per capita (X) using the Data Analysis Toolpak in Excel. Based on the Excel regression output, first write down the estimated regression equation and interpret the slope and intercept coefficients. Last, use the value of R-square for your estimated regression model to comment on the fit of the model in 3-4 sentences. (1+2+2 = 5 marks)]

d. [Test whether there is a significant linear relationship between CO2 emissions and GDP per capita at the 5% significance level. Conduct a two-tailed hypothesis test of the slope coefficient using the t-test and p-value approach and explain your results. Use the t-statistic and p-value from the Excel regression output. (4+2 = 6 marks)]

Question 2

[6 marks]
[In the file "Assessment 3 Data.xls" (available on Canvas), you’ll find information on GDP per capita (constant 2015 US$) for 15 countries in the worksheet “Timeseries Data GDP”. Choose any country of your liking from this dataset and complete the following analysis:]

a. [Estimate autoregressive (AR) models of order 3, 2, and 1 for the GDP per capita series of your chosen country. Present the results in three separate tables (one for each model). (1+1+1 = 3 marks)]

b. [Based on the p-value approach at the 5% significance level (α = 0.05), identify the most appropriate AR model and use it to forecast GDP per capita for 2025. (2+1 = 3 marks)]

Summary of Assessment Requirements

The ECON 1607 – Economic and Quantitative Analysis Assessment is divided into two major sections: Section A (theoretical and conceptual understanding) and Section B (data analysis and application using Microsoft Excel).

Section A: Conceptual and Theoretical Analysis (15 marks)

This section includes two mandatory questions focused on microeconomic and macroeconomic concepts.

• Question 1:
  Students analyze a pricing and revenue optimization scenario involving 8 buyers with known reservation prices for a product (bottle of premium wine). Key tasks include:

  • Identifying the market structure.

  • Drawing a demand curve based on reservation prices.

  • Calculating Total Revenue (TR) and Marginal Revenue (MR).

  • Determining the profit-maximizing price and quantity.

• Question 2:
  This question examines macroeconomic equilibrium using the Aggregate Demand and Supply (AD-AS) model in the context of the COVID-19 pandemic. Students must:

  • Illustrate demand-side and supply-side contractions.

  • Explain key demand and supply factors affecting economic activity.

  • Discuss which type of recession policymakers can better address and why.

Section B: Quantitative and Analytical Application (25 marks)

Students are required to use Microsoft Excel and the Data Analysis ToolPak to perform statistical analysis and regression modeling. The section consists of two questions:

• Question 1:
  Based on cross-country data for GDP and CO₂ emissions (selecting any 30 countries):

  • Create a histogram and describe descriptive statistics for CO₂ emissions.

  • Develop a scatter plot to study the relationship between CO₂ emissions and GDP per capita.

  • Perform a simple linear regression analysis and interpret the results.

  • Conduct hypothesis testing for the regression coefficients using t-tests and p-values.

• Question 2:
  Using time series GDP data for one selected country:

  • Estimate autoregressive (AR) models of orders 3, 2, and 1.

  • Identify the best-fitting AR model using the p-value approach.

  • Use the chosen model to forecast GDP per capita for 2025.

The Excel file submission is mandatory, and poor organization of the Excel data can result in a penalty.

Academic Mentor’s Step-by-Step Guidance Approach

The academic mentor guided the student through each part of the assessment methodically, ensuring both conceptual clarity and technical accuracy.

Step 1: Understanding the Assessment Scope

The mentor began by reviewing the entire assessment brief with the student to ensure they understood the key objectives linking economic theory with quantitative analysis. The mentor emphasized:

• The importance of following instructions and word limits.

• How Section A tests theoretical understanding, while Section B tests practical analytical skills.

Step 2: Guiding Through Section A – Economic Theory

Question 1 (Microeconomics):

• Market Structure Identification:
  The mentor helped the student recognize that the scenario represents a monopolistic market where the seller faces a downward-sloping demand curve and can set prices strategically.

• Demand Curve Construction:
  The mentor guided the student to plot price against quantity, using reservation prices to form the demand curve visually.

• Total Revenue and Marginal Revenue Calculation:
  The mentor explained how to calculate TR (Price × Quantity) for each output level and derive MR as the change in TR from one quantity to the next.
  These were later presented in a tabular format for clarity.

• Profit Maximization:
  The mentor assisted the student in comparing MR and Marginal Cost (MC = $28) to identify the equilibrium point where MR = MC, determining optimal price and quantity.

Question 2 (Macroeconomics):

• AD-AS Diagram and Explanation:
  The mentor demonstrated how to illustrate both demand-side and supply-side contractions using separate AD-AS diagrams.

  • Demand-side factors: reduced consumer spending and lower investment.

  • Supply-side factors: production disruptions and rising input costs.

• Policy Implication Discussion:
  The student was guided to argue that demand-side recessions can be addressed more effectively through fiscal and monetary policies, which directly influence aggregate demand.

Step 3: Guiding Through Section B – Data Analysis and Excel Application

Question 1 (Cross-country Data Analysis):

• Data Selection and Preparation:
  The mentor assisted the student in selecting 30 countries from the dataset and properly cleaning and labeling the variables in Excel.

• Histogram and Descriptive Statistics:
  The mentor explained how to use Excel’s Data Analysis ToolPak to generate a histogram and descriptive statistics for CO₂ emissions.
  The student learned to interpret key statistical measures such as mean, median, standard deviation, and skewness to describe data distribution.

• Scatter Plot and Relationship Observation:
  The mentor guided the student to create a scatter plot between GDP and CO₂ emissions to visually examine correlation trends.

• Simple Linear Regression:
  Using the regression tool, the student was guided to interpret:

  • Intercept (baseline CO₂ emissions),

  • Slope (change in emissions per unit increase in GDP), and

  • R-square (goodness of fit).
    The mentor explained how to write the regression equation and interpret results meaningfully.

• Hypothesis Testing:
  The mentor demonstrated how to use t-statistics and p-values to test whether GDP significantly affects CO₂ emissions at the 5% significance level, reinforcing understanding of statistical inference.

Question 2 (Time Series Analysis):

• Building AR Models:
  The mentor instructed how to run autoregressive models (AR(3), AR(2), AR(1)) and interpret their coefficients, using past GDP values as predictors.

• Model Selection and Forecasting:
  The student was guided to identify the best model using p-value criteria and use it to forecast GDP per capita for 2025, applying predictive analytics in Excel.

Step 4: Reviewing and Refining the Work

Once all analyses were completed, the mentor:

• Ensured consistency in formatting, clarity of Excel charts, and correctness of formulas.

• Reviewed written explanations for brevity and academic tone.

• Verified all Excel outputs were labeled and supported by short analytical summaries.

Final Outcome and Learning Objectives Achieved

By the end of the assessment, the student successfully:

• Integrated economic theory and quantitative analysis to solve real-world problems.

• Applied statistical and econometric tools using Excel’s Data Analysis ToolPak.

• Interpreted and visualized data using histograms, scatter plots, and regression outputs.

• Understood relationships between economic growth and environmental impact (GDP–CO₂ link).

• Developed forecasting skills using autoregressive modeling for economic prediction.

• Enhanced critical thinking and analytical reasoning through market analysis and policy evaluation.

Conclusion

The academic mentor’s step-by-step guidance ensured the student developed a balanced understanding of theoretical economics and data analytics. The process not only met the assessment requirements but also achieved key learning objectives data interpretation, economic reasoning, and evidence-based decision-making crucial for real-world economic analysis.

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