Reference no: EM133770429
Basic Econometrics
Use the dataset: WDI_24.RData
Use R to run a cross sectional regression onGDP per capitafor the listed countries as follows:
Ln(GDPpc) = β_0+β_1 ln(Conspc)+ β_2 Trade+β_3 Alco+β_4hightech+u
The variables are defined as follows:
GDPpc = GDP per capita, PPP (current international $)
Conspc= Households and NPISHs final consumption expenditure per capita (constant 2015 US$) [NE.CON.PRVT.PC.KD]
Trade=Trade (% of GDP) [NE.TRD.GNFS.ZS]
Alco = Total alcohol consumption per capita (liters of pure alcohol, 15+ years of age) [SH.ALC.PCAP.LI]
Popgr=Population growth (annual %) [SP.POP.GROW]
hightech= Medium and high-tech manufacturing value added (% manufacturing value added) [NV.MNF.TECH.ZS.UN]
You will have to take the natural log of GDPpc and Consumption per capita yourself using R!
Present your regression results in a table below (R output):
Interpret the constant (2.5 marks) and its p-value (1.5 marks).
Interpret the coefficient on household and NPISH consumptionand its p-value (1.5 marks each).
Interpret the coefficient on trade and its p-value (1.5 marks each). Hint: Use a large change for trade, such as a "10% point" change for example.
Interpret the coefficient on alcohol consumptionand carry out (meaning: calculate with the official formula) a t-test to determine the significance of the coefficient (1.5 marks each).
Interpret the R2 of the regression.
Run the following regression with the quadratic form of "Popgr".
Ln(GDPpc) = β_0+β_1 ln(Conspc)+ β_2 Trade+β_3 Alco+β_4 Popgr+β_5 ?Popgr?^2+ u
Explain if adding Popgr^2 is a good idea or not.
Interpret whether the relationship between Ln(GDPpc) and Popgr is U-shaped or inverted U-shaped in Q7.
Interpret the impact of population growth on the GDP per capita in Q7 when population growth is 3%.
Run the following regression:
Ln(GDPpc) = β_0+β_1 Alco+u
Comment on how the coefficient on "Alco" differs from that of Q1!
Why do you observe this difference and what does it mean for the (un)biasedness of the coefficient in Q8?
Describe each of the Gauss Markov Assumptions and specifically explain if they are likely to hold for the regression in Q1 or not
Present a functioning R code reproducing the results.
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