# for Core packages
library(tidyverse)
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# for financial analysis
library(tidyquant)
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# for times series
library(timetk)

Goal: Apply Matt Dancho’s tutorial to state unemployment initial claims of New England states.

The following is the replication of Matt Dancho’s tutorial on this page

start_date <- "1989-01-01"

symbols_txt <- c("CTICLAIMS", # Connecticut
                 "MEICLAIMS", # Maine
                 "MAICLAIMS", # Massachusetts
                 "NHICLAIMS", # New Hampshire
                 "RIICLAIMS", # Rhode Island
                 "VTICLAIMS") # Vermont

claims_tbl <- tq_get(symbols_txt, get = "economic.data", from = start_date) %>%
    mutate(symbol = fct_recode(symbol,
                               "Connecticut"   = "CTICLAIMS",
                               "Maine"         = "MEICLAIMS",
                               "Massachusetts" = "MAICLAIMS",
                               "New Hampshire" = "NHICLAIMS",
                               "Rhode Island"  = "RIICLAIMS",
                               "Vermont"       = "VTICLAIMS")) %>%
    rename(claims = price)

Plotting time series

claims_tbl
## # A tibble: 11,058 × 3
##    symbol      date       claims
##    <fct>       <date>      <int>
##  1 Connecticut 1989-01-07   8345
##  2 Connecticut 1989-01-14   6503
##  3 Connecticut 1989-01-21   3821
##  4 Connecticut 1989-01-28   4663
##  5 Connecticut 1989-02-04   4162
##  6 Connecticut 1989-02-11   4337
##  7 Connecticut 1989-02-18   4079
##  8 Connecticut 1989-02-25   3556
##  9 Connecticut 1989-03-04   3826
## 10 Connecticut 1989-03-11   3515
## # ℹ 11,048 more rows
claims_tbl %>%
    plot_time_series(.date_var = date, .value = claims)
claims_tbl %>% count(claims)
## # A tibble: 5,332 × 2
##    claims     n
##     <int> <int>
##  1    152     1
##  2    154     1
##  3    184     2
##  4    189     1
##  5    200     1
##  6    201     1
##  7    203     1
##  8    205     1
##  9    206     1
## 10    211     2
## # ℹ 5,322 more rows
claims_tbl %>%
    group_by(symbol) %>%
    plot_time_series(.date_var = date,
        .value = claims,
        .facet_ncol = 2,
        .facet_scales = "free",
        .interactive = FALSE)

Box plots

claims_tbl %>% count(claims)
## # A tibble: 5,332 × 2
##    claims     n
##     <int> <int>
##  1    152     1
##  2    154     1
##  3    184     2
##  4    189     1
##  5    200     1
##  6    201     1
##  7    203     1
##  8    205     1
##  9    206     1
## 10    211     2
## # ℹ 5,322 more rows
claims_tbl %>%
    plot_time_series_boxplot(.date_var = date,
        .value = claims,
        .period = "1 year",
        .facet_ncol = 2)

Regression plots

Plotting Seasonality and Correlation

Correlation Plots

claims_tbl %>%
    group_by(symbol) %>%
    plot_acf_diagnostics(date, claims,
        .lags = "7 days")

Seasonality

claims_tbl %>%
    plot_seasonal_diagnostics(date, claims)

STL Diagnostics

claims_tbl %>%
    group_by(symbol) %>%
    plot_stl_diagnostics(date, claims, 
                         .feature_set = c("observed", "season", "trend", "remainder"))
## frequency = 13 observations per 1 quarter
## trend = 53 observations per 1 year
## frequency = 13 observations per 1 quarter
## trend = 53 observations per 1 year
## frequency = 13 observations per 1 quarter
## trend = 53 observations per 1 year
## frequency = 13 observations per 1 quarter
## trend = 53 observations per 1 year
## frequency = 13 observations per 1 quarter
## trend = 53 observations per 1 year
## frequency = 13 observations per 1 quarter
## trend = 53 observations per 1 year

Time Series Data Wrangling

Summarize by Time

claims_tbl %>%
    group_by(symbol) %>%
    plot_time_series(date, claims, .facet_ncol = 2, .interactive = FALSE)

Filter By Time

claims_tbl %>%
    group_by(symbol) %>%
    filter_by_time(.date_var = date,
                   .start_date = "2013",
                   .end_date = "2018") %>%
    plot_time_series(date, claims, .facet_ncol = 2)

Padding Data

claims_tbl %>%
    group_by(symbol) %>%
    pad_by_time(date, .by = "day", .pad_value = 0)
## # A tibble: 77,370 × 3
## # Groups:   symbol [6]
##    symbol      date       claims
##    <fct>       <date>      <int>
##  1 Connecticut 1989-01-07   8345
##  2 Connecticut 1989-01-08      0
##  3 Connecticut 1989-01-09      0
##  4 Connecticut 1989-01-10      0
##  5 Connecticut 1989-01-11      0
##  6 Connecticut 1989-01-12      0
##  7 Connecticut 1989-01-13      0
##  8 Connecticut 1989-01-14   6503
##  9 Connecticut 1989-01-15      0
## 10 Connecticut 1989-01-16      0
## # ℹ 77,360 more rows

Sliding (Rolling) Calculations

claims_tbl %>%
    head(10) %>%
    mutate(rolling_avg_2 =slidify_vec(claims, mean, .period = 2, .align = "Right", .partial = TRUE))
## # A tibble: 10 × 4
##    symbol      date       claims rolling_avg_2
##    <fct>       <date>      <int>         <dbl>
##  1 Connecticut 1989-01-07   8345         8345 
##  2 Connecticut 1989-01-14   6503         7424 
##  3 Connecticut 1989-01-21   3821         5162 
##  4 Connecticut 1989-01-28   4663         4242 
##  5 Connecticut 1989-02-04   4162         4412.
##  6 Connecticut 1989-02-11   4337         4250.
##  7 Connecticut 1989-02-18   4079         4208 
##  8 Connecticut 1989-02-25   3556         3818.
##  9 Connecticut 1989-03-04   3826         3691 
## 10 Connecticut 1989-03-11   3515         3670.
# Rolling regressions are easy to implement using `.unlist = FALSE`
lm_roll <- slidify(~ lm(..1 ~ ..2 + ..3), .period = 90, 
                   .unlist = FALSE, .align = "right")


claims_tbl %>%
  select(symbol, date, claims) %>%
  group_by(symbol) %>%
  mutate(numeric_date = as.numeric(date)) %>%
  # Apply rolling regression
  mutate(rolling_lm = lm_roll(claims, date, numeric_date)) %>%
  filter(!is.na(rolling_lm))
## # A tibble: 10,524 × 5
## # Groups:   symbol [6]
##    symbol      date       claims numeric_date rolling_lm
##    <fct>       <date>      <int>        <dbl> <list>    
##  1 Connecticut 1990-09-22   3927         7569 <lm>      
##  2 Connecticut 1990-09-29   4471         7576 <lm>      
##  3 Connecticut 1990-10-06   4430         7583 <lm>      
##  4 Connecticut 1990-10-13   4494         7590 <lm>      
##  5 Connecticut 1990-10-20   4894         7597 <lm>      
##  6 Connecticut 1990-10-27   4653         7604 <lm>      
##  7 Connecticut 1990-11-03   4719         7611 <lm>      
##  8 Connecticut 1990-11-10   5347         7618 <lm>      
##  9 Connecticut 1990-11-17   4824         7625 <lm>      
## 10 Connecticut 1990-11-24   5367         7632 <lm>      
## # ℹ 10,514 more rows