nonparametric_tests: Returns the result of given event study nonparametric tests.
In estudy2: An Implementation of Parametric and Nonparametric Event Study

Description Usage Arguments Details Value References See Also Examples

Performs main nonparametric tests for each date in the event window and returns a data frame of their statistics and significance.

1	nonparametric_tests(list_of_returns, event_start, event_end, all = TRUE, tests)

`list_of_returns`	a list of objects of S3 class `returns`, each element of which is treated as a security.
`event_start`	an object of `Date` class giving the first date of the event period.
`event_end`	an object of `Date` class giving the last date of the event period.
`all`	a logical vector of length one indicating whether all tests should be performed. The default value is `TRUE`.
`tests`	a list of tests' functions among `sign_test`, `generalized_sign_test`, `corrado_sign_test`, `rank_test`, `modified_rank_test`, and `wilcoxon_test`.

nonparametric_tests performs given tests among sign_test, generalized_sign_test, corrado_sign_test, rank_test, modified_rank_test, wilcoxon_test, and merge result to a single data frame. If all = TRUE (the default value), the function ignores the value of tests.

A data frame of the following columns:

date: a calendar date
weekday: a day of the week
percentage: a share of non-missing observations for a given day
Various tests' statistics and significance

Corrado C.J., Zivney T.L. The Specification and Power of the Sign Test in Event Study Hypothesis Tests Using Daily Stock Returns. Journal of Financial and Quantitative Analysis, 27(3):465-478, 1992.
McConnell J.J., Muscarella C.J. Capital expenditure plans and firm value Journal of Financial Economics, 14:399-422, 1985.
Boehmer E., Musumeci J., Poulsen A.B. Event-study methodology under conditions of event-induced variance. Journal of Financial Economics, 30(2):253-272, 1991.
Cowan A.R. Nonparametric Event Study Tests. Review of Quantitative Finance and Accounting, 2:343-358, 1992.
Corrado C.J. A Nonparametric Test for Abnormal Security-Price Performance in Event Studies. Journal of Financial Economics 23:385-395, 1989.
Campbell C.J., Wasley C.E. Measuring Security Price Performance Using Daily NASDAQ Returns. Journal of Financial Economics 33:73-92, 1993.
Savickas R. Event-Induced Volatility and Tests for Abnormal Performance. The Journal of Financial Research, 26(2):156-178, 2003.
Kolari J.W., Pynnonen S. Event Study Testing with Cross-sectional Correlation of Abnormal Returns. The Review of Financial Studies, 23(11):3996-4025, 2010.
Wilcoxon F. Individual Comparisons by Ranking Methods. Biometrics Bulletin 1(6):80-83, 1945.
Lehmann E.L, Nonparametrics: Statistical Methods Based on Ranks. San Francisco: Holden-Day, 1975.
Hollander M., Wolfe D.A. Nonparametric Statistical Methods. New York: John Wiley & Sons, 1973.

sign_test, generalized_sign_test, corrado_sign_test, rank_test, modified_rank_test, and wilcoxon_test.

## Not run: 
library("magrittr")
rates_indx <- get_prices_from_tickers("^GSPC",
                                      start = as.Date("2019-04-01"),
                                      end = as.Date("2020-04-01"),
                                      quote = "Close",
                                      retclass = "zoo") %>%
    get_rates_from_prices(quote = "Close",
                          multi_day = TRUE,
                          compounding = "continuous")
tickers <- c("AMZN", "ZM", "UBER", "NFLX", "SHOP", "FB", "UPWK")
nparam <- get_prices_from_tickers(tickers,
                                  start = as.Date("2019-04-01"),
                                  end = as.Date("2020-04-01"),
                                  quote = "Close",
                                  retclass = "zoo") %>%
    get_rates_from_prices(quote = "Close",
                          multi_day = TRUE,
                          compounding = "continuous") %>%
    apply_market_model(regressor = rates_indx,
                       same_regressor_for_all = TRUE,
                       market_model = "sim",
                       estimation_method = "ols",
                       estimation_start = as.Date("2019-04-01"),
                       estimation_end = as.Date("2020-03-13")) %>%
    nonparametric_tests(event_start = as.Date("2020-03-16"),
                        event_end = as.Date("2020-03-20"))

## End(Not run)
## The result of the code above is equivalent to:
data(securities_returns)
nparam <- nonparametric_tests(list_of_returns = securities_returns,
                              event_start = as.Date("2020-03-16"),
                              event_end = as.Date("2020-03-20"))