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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 %>%
    plot_time_series(.date_var = date, .value = claims)
claims_tbl %>% count(symbol)
## # A tibble: 6 × 2
##   symbol            n
##   <fct>         <int>
## 1 Connecticut    1870
## 2 Massachusetts  1870
## 3 Maine          1870
## 4 New Hampshire  1870
## 5 Rhode Island   1870
## 6 Vermont        1870
claims_tbl %>% 
    group_by(symbol) %>%
    plot_time_series(
        .date_var = date, 
        .value = claims, 
        .facet_ncol = 3, 
        .facet_scales = "free",
        .interactive = FALSE)

Box plots

claims_tbl %>%
    group_by(symbol) %>%
    plot_time_series_boxplot(.date_var = date, .value = claims, .period = "6 months", .facet_ncol = 2)

Regression plots

claims_tbl %>%
    group_by(symbol) %>%
    plot_time_series_regression(.date_var = date, 
                                .formula = log(claims) ~ as.numeric(date) + month(date, label = TRUE),
                                .show_summary = FALSE)

Plotting Seasonality and Correlation

Correlation Plots

Seasonality

claims_tbl %>% 
    plot_seasonal_diagnostics(date, claims)
claims_tbl %>%
    group_by(symbol) %>%
    plot_seasonal_diagnostics(.date_var = date, .value = claims, .interactive = FALSE)

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)

claims_tbl %>%
    group_by(symbol) %>%
    summarise_by_time(.date_var = date, claims = sum(claims), .by = "quarter") %>%
    plot_time_series(date, claims, .facet_ncol = 2, .interactive = FALSE)

claims_tbl %>%
    group_by(symbol) %>%
    summarise_by_time(.date_var = date, claims = sum(claims), .by = "month") %>%
    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 = "1990-01", 
                   .end_date = "1991") %>%
    plot_time_series(date, claims, .facet_ncol = 2)

Padding Data

claims_tbl %>%
    group_by(symbol) %>%
    pad_by_time(date, .by = "day", .pad_value = 0) %>%
    plot_time_series(date, claims, .facet_ncol = 2)

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.
lm_roll <- slidify(~ lm(..1 ~ ..2 + ..3), .period = 90, 
                   .unlist = FALSE, .align = "right")

claims_tbl %>%
    select(date, claims, symbol) %>%
    group_by(symbol) %>%
    mutate(numeric_date = as.numeric(date)) %>%
    mutate(rolling_lm = lm_roll(claims, date, numeric_date)) %>%
  filter(!is.na(rolling_lm))
## # A tibble: 10,686 × 5
## # Groups:   symbol [6]
##    date       claims symbol      numeric_date rolling_lm
##    <date>      <int> <fct>              <dbl> <list>    
##  1 1990-09-22   3927 Connecticut         7569 <lm>      
##  2 1990-09-29   4471 Connecticut         7576 <lm>      
##  3 1990-10-06   4430 Connecticut         7583 <lm>      
##  4 1990-10-13   4494 Connecticut         7590 <lm>      
##  5 1990-10-20   4894 Connecticut         7597 <lm>      
##  6 1990-10-27   4653 Connecticut         7604 <lm>      
##  7 1990-11-03   4719 Connecticut         7611 <lm>      
##  8 1990-11-10   5347 Connecticut         7618 <lm>      
##  9 1990-11-17   4824 Connecticut         7625 <lm>      
## 10 1990-11-24   5367 Connecticut         7632 <lm>      
## # ℹ 10,676 more rows