Reading in my data.
This set contains monthly Federal Reserve interest rates from 1954-2017. It also contains the quarterly inflation rates, and monthly unemployment rates. I downloaded this data from Kaggle. Raw data can be found here
fed_rates <- read_csv("../../_data/FedFundsRate.csv")
Fixing dates
Changing numeric values in column “Year” to names of the month. I want to simplify the data set by being able to see the names of the month instead of month number.
fed_rates <- fed_rates %>%
mutate(Month = case_when(
Month == 1 ~ "January",
Month == 2 ~ "February",
Month == 3 ~ "March",
Month == 4 ~ "April",
Month == 5 ~ "May",
Month == 6 ~ "June",
Month == 7 ~ "July",
Month == 8 ~ "August",
Month == 9 ~ "September",
Month == 10 ~ "October",
Month == 11 ~ "November",
Month == 12 ~ "December")
)
Remove variables
There are variables in the fed_rates data set that I am not concerned with. These columns are the Fed Funds Target Rate, Fed Funds Upper Target Rate, and Fed Funds Lower Target Rate. Taking my variables from 10 to 7 by removing those columns and reducing noise in the data.
fed_rates_new <- fed_rates %>%
select("Year" , "Month" , "Day" , "Effective Federal Funds Rate" , "Real GDP (Percent Change)", "Unemployment Rate" , "Inflation Rate")
fed_rates_new
# A tibble: 904 × 7
Year Month Day `Effective Federal Fun… `Real GDP (Percent C…
<dbl> <chr> <dbl> <dbl> <dbl>
1 1954 July 1 0.8 4.6
2 1954 August 1 1.22 NA
3 1954 September 1 1.06 NA
4 1954 October 1 0.85 8
5 1954 November 1 0.83 NA
6 1954 December 1 1.28 NA
7 1955 January 1 1.39 11.9
8 1955 February 1 1.29 NA
9 1955 March 1 1.35 NA
10 1955 April 1 1.43 6.7
# … with 894 more rows, and 2 more variables:
# Unemployment Rate <dbl>, Inflation Rate <dbl>Filtering by Quarter
to make viewing quarterly GDP growth rate data easier to track. Because we do not tend to see wild swings in monthly data in regards to inflation and interest rate change.
fed_rates_quarter <- fed_rates_new %>%
filter(`Month` %in% c("January" , "April" , "July" , "October"))
fed_rates_quarter
# A tibble: 295 × 7
Year Month Day `Effective Federal Funds Rate` `Real GDP (Perc…
<dbl> <chr> <dbl> <dbl> <dbl>
1 1954 July 1 0.8 4.6
2 1954 October 1 0.85 8
3 1955 January 1 1.39 11.9
4 1955 April 1 1.43 6.7
5 1955 July 1 1.68 5.5
6 1955 October 1 2.24 2.4
7 1956 January 1 2.45 -1.5
8 1956 April 1 2.62 3.4
9 1956 July 1 2.75 -0.3
10 1956 October 1 2.96 6.7
# … with 285 more rows, and 2 more variables:
# Unemployment Rate <dbl>, Inflation Rate <dbl>Real GDP (Percent Change) over time
We see a downward annual trend since 1954 with fairly substantial movement in GDP percent growth quarterly. The highest % change in GDP came in Q2 (April) of 1978 with a 16% increase QoQ.
summarise_GDP <- fed_rates_quarter
select(fed_rates_quarter,`Month`, `Real GDP (Percent Change)`)
# A tibble: 295 × 2
Month `Real GDP (Percent Change)`
<chr> <dbl>
1 July 4.6
2 October 8
3 January 11.9
4 April 6.7
5 July 5.5
6 October 2.4
7 January -1.5
8 April 3.4
9 July -0.3
10 October 6.7
# … with 285 more rows summarise(fed_rates_quarter , mean.Real_GDP = mean(`Real GDP (Percent Change)` , na.rm = TRUE) , max.Real_GDP = max(`Real GDP (Percent Change)` , na.rm = TRUE) , min.Real_GDP = min(`Real GDP (Percent Change)` , na.rm = TRUE) , sd.Real_GDP = sd(`Real GDP (Percent Change)` , na.rm = TRUE) , IQR.Real_GDP = IQR(`Real GDP (Percent Change)` , na.rm = TRUE))
# A tibble: 1 × 5
mean.Real_GDP max.Real_GDP min.Real_GDP sd.Real_GDP IQR.Real_GDP
<dbl> <dbl> <dbl> <dbl> <dbl>
1 3.14 16.5 -10 3.60 3.48ggplot(fed_rates_quarter, aes(`Year` , `Real GDP (Percent Change)`)) +
geom_point() +
geom_smooth()
Unemployment rate over time
ggplot(fed_rates_quarter, aes(`Year` , `Unemployment Rate`)) +
geom_point() +
geom_smooth()
Interest Rate over time
ggplot(fed_rates_quarter, aes(`Year` , `Effective Federal Funds Rate`)) +
geom_point() +
geom_smooth()
Inflation Rate over time
ggplot(fed_rates_quarter, aes(`Year` , `Inflation Rate`)) +
geom_point() +
geom_smooth()
