Testing for and dating periods of explosive dynamics (exuberance) intime series using the univariate and panel recursive unit root testsproposed byPhillips etal. (2015) andPavlidis etal. (2016). The recursive least-squares algorithm utilizes thematrix inversion lemma to avoid matrix inversion which results insignificant speed improvements. Simulation of a variety ofperiodically-collapsing bubble processes.
Testing for explosive dynamics is comprised of two distinct parts:
Some Context: Conventional testing techniquescompute critical values,and p-values from a standard distribution, wherethe user does not need to specify critical values explicitly. However,the recent literature in explosive dynamics require the use ofnon-standard distributions, which require the use of techniques thatsample empirical distributions in order to calculate the criticalvalues.
The cornerstone function of the package is:
radf(): Recursive Augmented Dickey-Fuller Test.This function offers a vectorized estimation (i.e. single and/ormultiple time-series) for individual and panel estimation. Theestimation can parse data from multiple classes and handle dates asindex.
There are several options for generating critical values:
radf_mc_cv(): Monte Carloradf_wb_cv(): Wild Bootstrapradf_sb_cv(): Sieve Bootstrap (Panel)On defaultexuber will use Monte Carlo simulatedcritical values if no other option is provided. The package offers thesecritical values in the form ofdata (up to 600observations), that are obtained with themc_cv()function.
For the analysis you should include both the output from estimation(object) and critical values (cv). The belowmethods break the process into small simple steps:
summary() summarizes the model.diagnostics() shows which series reject the nullhypothesis .datestamp() computes the origination, termination andduration of episodes (if any).These combined provide a comprehensive analysis on the exuberantbehavior of the model.
# Install release version from CRANinstall.packages("exuber")You can install the development version of exuber from GitHub.
# install.packages("devtools")devtools::install_github("kvasilopoulos/exuber")If you encounter a clear bug, please file a reproducible example onGitHub.
library(exuber)rsim_data<-radf(sim_data)summary(rsim_data)#> Using `radf_crit` for `cv`.#>#> ── Summary (minw = 19, lag = 0) ─────────────────── Monte Carlo (nrep = 2000) ──#>#> psy1 :#> # A tibble: 3 × 5#> stat tstat `90` `95` `99`#> <fct> <dbl> <dbl> <dbl> <dbl>#> 1 adf -2.46 -0.413 -0.0812 0.652#> 2 sadf 1.95 0.988 1.29 1.92#> 3 gsadf 5.19 1.71 1.97 2.57#>#> psy2 :#> # A tibble: 3 × 5#> stat tstat `90` `95` `99`#> <fct> <dbl> <dbl> <dbl> <dbl>#> 1 adf -2.86 -0.413 -0.0812 0.652#> 2 sadf 7.88 0.988 1.29 1.92#> 3 gsadf 7.88 1.71 1.97 2.57#>#> evans :#> # A tibble: 3 × 5#> stat tstat `90` `95` `99`#> <fct> <dbl> <dbl> <dbl> <dbl>#> 1 adf -5.83 -0.413 -0.0812 0.652#> 2 sadf 5.28 0.988 1.29 1.92#> 3 gsadf 5.99 1.71 1.97 2.57#>#> div :#> # A tibble: 3 × 5#> stat tstat `90` `95` `99`#> <fct> <dbl> <dbl> <dbl> <dbl>#> 1 adf -1.95 -0.413 -0.0812 0.652#> 2 sadf 1.11 0.988 1.29 1.92#> 3 gsadf 1.34 1.71 1.97 2.57#>#> blan :#> # A tibble: 3 × 5#> stat tstat `90` `95` `99`#> <fct> <dbl> <dbl> <dbl> <dbl>#> 1 adf -5.15 -0.413 -0.0812 0.652#> 2 sadf 3.93 0.988 1.29 1.92#> 3 gsadf 11.0 1.71 1.97 2.57diagnostics(rsim_data)#> Using `radf_crit` for `cv`.#>#> ── Diagnostics (option = gsadf) ───────────────────────────────── Monte Carlo ──#>#> psy1: Rejects H0 at the 1% significance level#> psy2: Rejects H0 at the 1% significance level#> evans: Rejects H0 at the 1% significance level#> div: Cannot reject H0#> blan: Rejects H0 at the 1% significance leveldatestamp(rsim_data)#> Using `radf_crit` for `cv`.#>#> ── Datestamp (min_duration = 0) ───────────────────────────────── Monte Carlo ──#>#> psy1 :#> Start Peak End Duration Signal Ongoing#> 1 44 48 56 12 positive FALSE#>#> psy2 :#> Start Peak End Duration Signal Ongoing#> 1 22 40 41 19 positive FALSE#> 2 62 70 71 9 positive FALSE#>#> evans :#> Start Peak End Duration Signal Ongoing#> 1 20 20 21 1 positive FALSE#> 2 44 44 45 1 positive FALSE#> 3 66 67 68 2 positive FALSE#>#> blan :#> Start Peak End Duration Signal Ongoing#> 1 34 36 37 3 positive FALSE#> 2 84 86 87 3 positive FALSEautoplot(rsim_data)#> Using `radf_crit` for `cv`.
Please note that the ‘exuber’ project is released with aContributorCode of Conduct. By contributing to this project, you agree to abideby its terms.