1. You are required to use R for this exercise. In particular, you must use the following libraries: tidyverse and WDI, both of which are introduced and discussed as part of the pre-recorded lectures.
   1. Put together a data frame using the WDI function, which is part of the WDI library. Call your data frame: df. For this data frame, the country should be set as the US. And the indicators should include:

• FP.CPI.TOTL.ZG (Inflation, consumer prices (annual %))
• NY.GDP.MKTP.KD.ZG (GDP growth (annual %))
• FM.LBL.BMNY.ZG (Broad money growth (annual %))
• NE.GDI.FTOT.KD.ZG (Gross fixed capital formation (annual % growth))
  1. Rename variable FP.CPI.TOTL.ZG as: inflation. Copy and paste the code below.

The starting year must be set at 1973, and the end year must be set at 2019.

Copy and paste below the line of code by which you put together the above data frame .

1. Rename variable NY.GDP.MKTP.KD.ZG as: gdp_growth. Copy and paste the code below.
2. Rename variable FM.LBL.BMNY.ZG as: money_growth. Copy and paste the code below.
3. Rename variable NE.GDI.FTOT.KD.ZG as: inv_growth. Copy and paste the code below.
4. Estimate the parameters of the population regression function below:

inflation = a + 6, x gap_growth + 6, x money_growth + 6, x inv_growth +s\fil