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8- [ ![ CRAN status] ( https://www.r-pkg.org/badges/version/exuber )] ( https://CRAN.R-project.org/package=exuber )
8+ [ ![ CRAN
9+ status] ( https://www.r-pkg.org/badges/version/exuber )] ( https://CRAN.R-project.org/package=exuber )
910[ ![ Project Status: Active – The project has reached a stable, usable
1011state and is being actively
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1618<!-- badges: end -->
1719
1820Testing for and dating periods of explosive dynamics (exuberance) in
@@ -28,8 +30,8 @@ processes.
2830
2931Testing for explosive dynamics is comprised of two distinct parts :
3032
31- - Estimation
32- - Critical Values
33+ - Estimation
34+ - Critical Values
3335
3436** Some Context:** Conventional testing techniques compute critical
3537values,and p-values from a standard distribution, where the user does
@@ -42,7 +44,7 @@ distributions in order to calculate the critical values.
4244
4345The cornerstone function of the package is:
4446
45- - ` radf() ` : Recursive Augmented Dickey-Fuller Test.
47+ - ` radf() ` : Recursive Augmented Dickey-Fuller Test.
4648
4749This function offers a vectorized estimation (i.e. single and/or
4850multiple time-series) for individual and panel estimation. The
5355
5456There are several options for generating critical values:
5557
56- - ` radf_mc_cv() ` : Monte Carlo
57- - ` radf_wb_cv() ` : Wild Bootstrap
58- - ` radf_sb_cv() ` : Sieve Bootstrap (Panel)
58+ - ` radf_mc_cv() ` : Monte Carlo
59+ - ` radf_wb_cv() ` : Wild Bootstrap
60+ - ` radf_sb_cv() ` : Sieve Bootstrap (Panel)
5961
6062On default ` exuber ` will use Monte Carlo simulated critical values if no
6163other option is provided. The package offers these critical values in
@@ -68,10 +70,10 @@ For the analysis you should include both the output from estimation
6870(` object ` ) and critical values (` cv ` ). The below methods break the
6971process into small simple steps:
7072
71- - ` summary() ` summarizes the model.
72- - ` diagnostics() ` shows which series reject the null hypothesis .
73- - ` datestamp() ` computes the origination, termination and duration of
74- episodes (if any).
73+ - ` summary() ` summarizes the model.
74+ - ` diagnostics() ` shows which series reject the null hypothesis .
75+ - ` datestamp() ` computes the origination, termination and duration of
76+ episodes (if any).
7577
7678These combined provide a comprehensive analysis on the exuberant
7779behavior of the model.
@@ -104,42 +106,42 @@ rsim_data <- radf(sim_data)
104106summary(rsim_data )
105107# > Using `radf_crit` for `cv`.
106108# >
107- # > -- Summary (minw = 19, lag = 0) ------------------- Monte Carlo (nrep = 2000) --
109+ # > ── Summary (minw = 19, lag = 0) ─────────────────── Monte Carlo (nrep = 2000) ──
108110# >
109111# > psy1 :
110- # > # A tibble: 3 x 5
112+ # > # A tibble: 3 × 5
111113# > stat tstat `90` `95` `99`
112114# > <fct> <dbl> <dbl> <dbl> <dbl>
113115# > 1 adf -2.46 -0.413 -0.0812 0.652
114116# > 2 sadf 1.95 0.988 1.29 1.92
115117# > 3 gsadf 5.19 1.71 1.97 2.57
116118# >
117119# > psy2 :
118- # > # A tibble: 3 x 5
120+ # > # A tibble: 3 × 5
119121# > stat tstat `90` `95` `99`
120122# > <fct> <dbl> <dbl> <dbl> <dbl>
121123# > 1 adf -2.86 -0.413 -0.0812 0.652
122124# > 2 sadf 7.88 0.988 1.29 1.92
123125# > 3 gsadf 7.88 1.71 1.97 2.57
124126# >
125127# > evans :
126- # > # A tibble: 3 x 5
128+ # > # A tibble: 3 × 5
127129# > stat tstat `90` `95` `99`
128130# > <fct> <dbl> <dbl> <dbl> <dbl>
129131# > 1 adf -5.83 -0.413 -0.0812 0.652
130132# > 2 sadf 5.28 0.988 1.29 1.92
131133# > 3 gsadf 5.99 1.71 1.97 2.57
132134# >
133135# > div :
134- # > # A tibble: 3 x 5
136+ # > # A tibble: 3 × 5
135137# > stat tstat `90` `95` `99`
136138# > <fct> <dbl> <dbl> <dbl> <dbl>
137139# > 1 adf -1.95 -0.413 -0.0812 0.652
138140# > 2 sadf 1.11 0.988 1.29 1.92
139141# > 3 gsadf 1.34 1.71 1.97 2.57
140142# >
141143# > blan :
142- # > # A tibble: 3 x 5
144+ # > # A tibble: 3 × 5
143145# > stat tstat `90` `95` `99`
144146# > <fct> <dbl> <dbl> <dbl> <dbl>
145147# > 1 adf -5.15 -0.413 -0.0812 0.652
@@ -149,7 +151,7 @@ summary(rsim_data)
149151diagnostics(rsim_data )
150152# > Using `radf_crit` for `cv`.
151153# >
152- # > -- Diagnostics (option = gsadf) --------------------------------- Monte Carlo --
154+ # > ── Diagnostics (option = gsadf) ───────────────────────────────── Monte Carlo ──
153155# >
154156# > psy1: Rejects H0 at the 1% significance level
155157# > psy2: Rejects H0 at the 1% significance level
@@ -160,7 +162,7 @@ diagnostics(rsim_data)
160162datestamp(rsim_data )
161163# > Using `radf_crit` for `cv`.
162164# >
163- # > -- Datestamp (min_duration = 0) --------------------------------- Monte Carlo --
165+ # > ── Datestamp (min_duration = 0) ───────────────────────────────── Monte Carlo ──
164166# >
165167# > psy1 :
166168# > Start Peak End Duration Signal Ongoing
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