Basic Econometrics Individual Assignment: Cross-Sectional Regression Analysis, Model Interpretation, and Gauss-Markov Assumptions, Singapore

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      University The Royal Melbourne Institute of Technology (RMIT)
      Subject Basic Econometrics

      Basic Econometrics Individual Assignment

      QUESTIONS:

       1) Use R to run the following cross-sectional regression. (Please note the natural logs and construct these in R as needed):

      𝐋𝐢𝐟𝐞𝐞𝐱𝐩 = 𝜷𝟎 + 𝜷𝟏𝐥𝐨𝐠(𝐆𝐃𝐏𝐩𝐜) + 𝜷𝟐𝐔𝐧𝐝𝐞𝐫𝐍𝐨𝐮𝐫𝐢𝐬𝐡𝐞𝐝 + 𝜷𝟑𝐃𝐫𝐢𝐧𝐤𝐢𝐧𝐠𝐖𝐚𝐭𝐞𝐫 + 𝜷𝟒𝐥𝐨𝐠(𝐓𝐁) + 𝜷𝟓𝒍𝒐𝒈(𝐈𝐦𝐦𝐮𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧) + 𝒖   

      a) Present your regression results in a table below (R output):

      2 marks 

      b) Interpret the constant (2.5 marks) and its p-value (1.5 marks).

      4 marks

      c) Interpret the coefficient on GDP per capita (2.5 marks) and its p-value (1.5 marks).

      4 marks  

       d) Interpret the coefficient on the % of people using at least basic drinking water services (2.5 marks) and its p-value (1.5 marks).

      4 marks 

       e) Interpret the coefficient on Incidence of tuberculosis (per 100,000 people) (2.5 marks) and its pvalue (1.5 marks).

      4 marks  

      f) Interpret the coefficient on Immunization, DPT (% of children ages 12-23 months) (2.5 marks) and calculate its t-stat. Interpret the calculated t-statistic (1.5 marks).

      4 marks  

       g) Interpret the R2 of the regression.

      2 marks 

      h) One of the explanatory variables is in a functional form that is not usually recommended. Which one is it, and how would you change it?

      2 marks 

       2) Specify if the Gauss-Markov assumptions are likely to hold for the regression in Question 1 or not and explain why (each assumption).

      5 marks

       3) Run the following regression with a quadratic drinking water term added to the original regression:

      𝐋𝐢𝐟𝐞    𝐄𝐱𝐩𝐞𝐜𝐭𝐚𝐧𝐜𝐲 = 𝜷𝟎 + 𝜷𝟏𝐥𝐨𝐠(𝐆𝐃𝐏𝐩𝐜) + 𝜷𝟐𝐔𝐧𝐝𝐞𝐫𝐍𝐨𝐮𝐫𝐢𝐬𝐡𝐞𝐝 + 𝜷𝟑𝐃𝐫𝐢𝐧𝐤𝐢𝐧𝐠𝐖𝐚𝐭𝐞𝐫            + 𝜷𝟒𝐃𝐫𝐢𝐧𝐤𝐢𝐧𝐠𝐖𝐚𝐭𝐞𝐫𝟐        + 𝜷𝟒𝐥𝐨𝐠(𝐓𝐁) + 𝜷𝟓                      𝐥𝐨𝐠(𝐈𝐦𝐦𝐮𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧) + 𝒖   

      2 marks

       a) Is the relationship U-shaped or inverted U shaped? Is this a significant relationship?

      2 marks 

      b) Calculate the turning point of the quadratic relationship, and please analyse the result.

      4 marks 

       

      4) Present a functioning R code reproducing the results below. This is a critical part of the assignment without which we’ll initiate a plagiarism check.

      1 mark

      Assignment Total: 40 marks 

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      FORMULA SHEET

      Critical values for the standard normal distribution (z)

      Confidence level

      (1-α)

      Level of

      Significance (α)

      Two–Sided

      Critical Value cα/2

      One-Sided,

      Upper-Tail

      Critical Value cα

      One-Sided,

      Lower-Tail

      Critical Value -cα

      90% 10% 1.645 1.28 -1.28
      95% 5% 1.96 1.645 -1.645
      99% 1% 2.58 2.33 -2.33

       

      Formula for a t-statistic

      𝑡      = 𝑒𝑠𝑡𝑖𝑚𝑎𝑡𝑒 − ℎ𝑦𝑝𝑜𝑡ℎ𝑒𝑠𝑖𝑠𝑒𝑑             𝑣𝑎𝑙𝑢𝑒 / 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑             𝑒𝑟𝑟𝑜𝑟

      Formula for a (1-α)% confidence interval

      𝐶𝐼+,- = ^𝛽‘ − 𝑐-// ∗ 𝑠𝑒c𝛽‘d, 𝛽‘ + 𝑐-// ∗ 𝑠𝑒c𝛽‘df

      Logarithmic/Quadratic/Interaction specifications

      For the model 𝑙𝑜𝑔(𝑦) = 𝛽‘0 + 𝛽‘+𝑥+ + 𝛽‘/𝑥/, the exact effect of a change in explanatory variable x2 is: %∆𝑦k = 100nexpc𝛽‘/∆𝑥/d − 1r

      For a quadratic specification of the form:

      𝑦 = 𝛽0 + 𝛽+𝑥 + 𝛽/𝑥/ + 𝑢

      The turning point (maximum/minimum) is given by:

      𝑥= s𝛽‘+/(2𝛽‘/)s

      The approximation of the marginal effect of x on y is given by:

      ∆∆𝑦𝑥k   ‘+ + 2𝛽‘/𝑥 ≈ 𝛽

      For a interaction specification of the form:

      𝑦 = 𝛽0 + 𝛽+𝑥+ + 𝛽/𝑥+ ∗ 𝑥/ + 𝑢

      The approximation of the marginal effect of x1 on y is given by:

      Δy/Δ𝑥/ ≈ 𝛽/ ∗ 𝛽/ 𝑥/

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