Consider the US monthly macroeconomic data used in Example 8.4. Use the same training and testing subsamples, and apply a DL network below

```
mod mod %>%\
layer_dense(units=32,activation="relu",input_shape=c(k)) %>%\
layer_dropout(rate=0.4) %>%\
layer_dense(units=16,activation="relu") %>%\
layer_dropout(rate=0.25) %>%\
layer_dense(units=16,activation="relu") %>%\
layer_dropout(rate=0.25) %>%\
layer_dense(units=4,activation="relu") %>%\
layer_dense(units=1)\
```
What are the root mean squared error, mean absolute error, and median absolute error in the testing subsample? Compare the results with those in Example 8.4.

Example 8.4:

In this example, we consider US monthly unemployment rate from January 1948 to February 2020. The data are seasonally adjustment and can be downloaded directly from the FRED, which is the Economic Database of the Federal Reserve Banks at St. Louis. In fact, one can download the data over the Internet via the R package quantmod. Details are given in the attached R commands and output. Alternatively, the data are also in the file unrate4820.txt. Figure 8.9 shows the time plot of the US monthly unemployment rate data.

Transcribed Image Text:

UNRATE [1948-01-01/2020-02-01] Last 3.5 10 8 6 Jan 1948 Jan 1960 Jan 1970 Jan 1980 Jan 1990 Jan 2000 Jan 2010 Jan 2020 Figure 8.9 Monthly US unemployment rate from January 1948 to February 2020. The plot is obtained by the command chart Series of the R package quantmod.