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AdvancedR

The goal of Macroeconomic functions is to extract macroeconomic time series data quickly and efficiently. If desired the extracted time series data can be expanded to include columns of growth rates. In addition, when presenting macroeconomic times series data the points in the data where a recession occurred is often informative. This package includes a function used to extract US recession dates and prepare them for presentation with other macroeconomic data.

Example

Currently this package will extract the following four time series, Real GDP (RGDP), Consumer Price Index (CPI), the unemployment rate (unemploymentrate), and the US population (population), using the ME_dtaextract() function For example to extract the macroeconomic time series for real gross domestic product:

X<-ME_dtaextract("rgdp")
kable(head(X))

date	rgdp	units
1947-01-01	1934.5	millions
1947-04-01	1932.3	millions
1947-07-01	1930.3	millions
1947-10-01	1960.7	millions
1948-01-01	1989.5	millions
1948-04-01	2021.9	millions

The data in this table is in levels; i.e. for each date in the table we're given the level of real GDP. The data in this table can easily be rendered in a graphical form.

X %>% ggplot(aes(x=date, y=rgdp))+
        geom_point()+
        geom_line(color="dodgerblue3")+
        geom_hline(yintercept=0)

In many circumstances the data given in levels is more interesting if it is transformed to growth rates. This is especially true of macroeconomic data. For example, transforming the CPI data into growth rates creates a data series representing rates of inflation. For example, converting real gross domestic product to growth rates in real GDP is done quickly using the code below.

X<-X %>% ME_growth()
kable(head(X))

date	rgdp	units	Growth	Measure	Annual_Growth	Measured
1947-04-01	1932.3	millions	-0.0011372	Quarterly	-0.0045490	Annual
1947-07-01	1930.3	millions	-0.0010350	Quarterly	-0.0041401	Annual
1947-10-01	1960.7	millions	0.0157488	Quarterly	0.0629954	Annual
1948-01-01	1989.5	millions	0.0146886	Quarterly	0.0587545	Annual
1948-04-01	2021.9	millions	0.0162855	Quarterly	0.0651420	Annual
1948-07-01	2033.2	millions	0.0055888	Quarterly	0.0223552	Annual

The length of the data series can be manipulated by way of the date variable; e.g., maybe only real GDP data since 2000 is of interest. We can reduce the scope of the data with another call to the ME_growth function, as shown here.

X1<-ME_growth(X,"2000-01-01")
kable(head(X1))

date	rgdp	units	Growth	Measure	Annual_Growth	Measured
2000-01-01	12359.1	millions	0.0029051	Quarterly	0.0116203	Annual
2000-04-01	12592.5	millions	0.0188849	Quarterly	0.0755395	Annual
2000-07-01	12607.7	millions	0.0012071	Quarterly	0.0048283	Annual
2000-10-01	12679.3	millions	0.0056791	Quarterly	0.0227163	Annual
2001-01-01	12643.3	millions	-0.0028393	Quarterly	-0.0113571	Annual
2001-04-01	12710.3	millions	0.0052992	Quarterly	0.0211970	Annual

Finally, most macroeconomic data, when presented in a classroom/report setting, include markers showing where in time recessions occurred. This data is collected by the national bureau of economic research (NBER) and deposited at the St. Louis FED. This data is extracted and prepared for graphing use the ME_Rec function as shown here.

rec<-ME_Rec(X1)
kable(head(rec))

date	recession	units	Cyc
2000-01-01	0	unit	0
2000-02-01	0	unit	0
2000-03-01	0	unit	0
2000-04-01	0	unit	0
2000-05-01	0	unit	0
2000-06-01	0	unit	0

With the data trimmed to 2000 we can use the recession data to place recession markers in the picture showing real GDP series.

X1%>%ggplot(aes(x=date, y=rgdp))+
        geom_point()+
        geom_line(color="red3")+
        geom_path(data=rec, aes(x=date, y=Cyc))+
        geom_hline(yintercept = 0)

The Federal Reserve Bank of St. Louis maintains thousands of economic time series all are available to the public. However, at this time ME_dtaextract function will only extract the four series stated above. The functions ME_growth and ME_Rec will work with any of the FEDS time series data.