Package index • exuber

Package

exuber-package exuber: exuber: Econometric Analysis of Explosive Time Series
exuber_functions(): Look Up exuber's Test/Procedure Functions by Family

Recursive Augmented Dickey Fuller

Estimation & Critical Values – the cornerstone of the package

radf(): Recursive Augmented Dickey-Fuller Test
radf_mc_cv() radf_mc_distr(): Monte Carlo Critical Values
radf_wb_cv() radf_wb_distr(): Wild Bootstrap Critical Values
radf_wb_ps_cv() radf_wb_ps_distr(): Wild Bootstrap Critical Values (Phillips & Shi 2020)
radf_sb_cv() radf_sb_distr(): Panel Sieve Bootstrap Critical Values
radf_crit: Stored Monte Carlo Critical Values

Analysis

The core workflow, in order: summarize the fit, check which series reject the null, date the explosive episodes, then quantify how fast each one is growing

summary(<radf_obj>): Summarizing radf models
diagnostics(): Diagnostics on hypothesis testing
datestamp(): Date-stamping periods of mildly explosive behavior
rootstamp() autoplot(<rootstamp_est>) autoplot(<rootstamp_episodes>): Confidence Interval and Doubling Time for an Explosive Root

Tidying

tidy(<radf_obj>) augment(<radf_obj>): Tidy a radf_obj object
tidy(<radf_cv>) augment(<radf_cv>): Tidy a radf_cv object
tidy(<radf_distr>): Tidy a radf_distr object
tidy(<ds_radf>): Tidy a ds_radf object

Plotting

autoplot(<radf_obj>) autoplot2(<radf_obj>) shade(): Plotting radf models
autoplot(<radf_distr>): Plotting a radf_distr object
autoplot(<ds_radf>): Plotting a ds_radf object
scale_exuber_manual() theme_exuber(): Exuber scale and theme functions

Heteroskedasticity-robust (time-transformed)

Bootstrap-free alternative to radf_wb_cv() under time-varying volatility

radf_tt(): Time-Transformed Test for Explosive Bubbles under Non-stationary Volatility
radf_tt_cv(): Monte Carlo critical values for the time-transformed test (STADF/GSTADF)

Volatility-robust (other routes)

Further tests robust to time-varying innovation variance; see docs/enhancements/volatility-robustness.md

radf_sbz(): WLS/Kernel-Volatility Bubble Statistic (SBZ)
radf_sbz_cv(): Wild Bootstrap Critical Values for the SBZ Statistic
radf_sbz_union() autoplot(<radf_sbz_union>): SBZ Weighted Least Squares Bubble Test with Union-of-Rejections
radf_kp(): Kernel-Purged Heteroskedasticity-Robust PSY Test
radf_sign(): Sign-Based Bubble Test (sPWY / sPSY)
radf_sign_cv(): Monte Carlo Critical Values for the Sign-Based Test
radf_sign_dm(): Recursively Demeaned Sign-Based Bubble Test (s-bar-PWY / s-bar-PSY)
radf_sign_dm_cv(): Monte Carlo Critical Values for the Recursively Demeaned Sign-Based Test
ssu_test() autoplot(<ssu_test_obj>): Stochastic Unit Root Bubble Test (Kurozumi & Nishi 2025)

Dating and root inference

Standalone dating procedures with no critical value needed, and recovery dating; see docs/enhancements/dating-and-root-inference.md. rootstamp() itself now lives under Analysis above – it’s the last step of the core workflow, not an alternative to datestamp()

dating_pdc() autoplot(<dating_pdc_obj>): Sequential Sample-Splitting Bubble Dating (PDC/KS)
radf_recovery() autoplot(<radf_recovery_obj>): Reverse-Regression Dating of Crisis Origination and Market Recovery
radf_recovery_cv(): Monte Carlo Critical Values for Reverse-Regression Recovery Dating
dating_hls() autoplot(<dating_hls_obj>): SSR/BIC Bubble Dating (Harvey, Leybourne & Sollis 2017)
dating_hlw() autoplot(<dating_hlw_obj>): Multi-Bubble SSR/BIC Dating (Harvey, Leybourne & Whitehouse 2020)
dating_knp() autoplot(<dating_knp_obj>): Bias-Corrected Single-Bubble Dating (Kejriwal, Nguyen & Perron 2025)

Real-time monitoring

Sequential/real-time bubble detection; see docs/enhancements/monitoring.md. Each monitor is listed with its static, full-sample counterpart where one exists.

monitor() autoplot(<monitor_obj>): Real-Time Monitoring for Explosive Bubbles
monitor_cusum() autoplot(<monitor_cusum_obj>): CUSUM Real-Time Monitoring for Explosive Bubbles
monitor_lbi() autoplot(<monitor_lbi_obj>): Sequential LBI Monitoring for an Unknown Bubble Start Date (Breitung & Diegel 2025)
lbi_test() autoplot(<lbi_test_obj>): Locally Best Invariant Test for a Bubble (Breitung & Diegel 2025)
monitor_quantile() autoplot(<monitor_quantile_obj>): QPWY Recursive Quantile Monitoring (Wu, Shi & Wu 2025)
quantile_test() autoplot(<quantile_test_obj>): Quantile Unit Root Test for Bubble Detection (Global Test)

Multivariate / panel bubble tests

Panel and cross-series tests; see docs/enhancements/multivariate.md

radf_common(): Common-Bubble Detection via PCA + PSY
radf_common_cv(): Critical Values for the Common-Bubble (PCA + PSY) Test
cobubble_test() autoplot(<cobubble_test>): Test for Co-explosive Behaviour Between Two Series
contagion_reg() autoplot(<contagion_reg_obj>): Bubble Contagion Regression (Greenaway-McGrevy & Phillips 2016)

Simulation

sim_psy1(): Simulation of a single-bubble process
sim_psy2(): Simulation of a two-bubble process
sim_ps1(): Simulation of a single-bubble process with multiple forms of collapse regime
sim_blan(): Simulation of a Blanchard (1979) / Rotermann-Wilfling (2018) bubble process
sim_evans(): Simulation of an Evans (1991) bubble process
sim_div(): Simulation of dividends

Simulation (additional DGPs)

Data-generating processes for axes exuber’s original sim_*() functions don’t cover – time-varying volatility, non-Gaussian innovations, long memory, and distinct multi-series/branching mechanisms; see docs/enhancements/simulation-dgps.md

sim_innov(): Simulate innovations with heavy-tailed/skewed marginal distributions
sim_vol_garch(): Simulate GARCH(1,1)/TGARCH(1,1) innovations
sim_vol_cir(): Simulate CIR-type stochastic-volatility innovations
sim_vol_sv(): Simulate AR(1) lognormal stochastic-volatility innovations
sim_fi(): Simulate fractionally-integrated (long-memory) innovations
sim_common(): Simulation of a latent common-factor bubble across multiple series
sim_coexplosive(): Simulation of a bivariate co-explosive pair
sim_tree(): Simulation of a stochastic branching-tree bubble
sim_mar(): Simulation of a mixed causal-noncausal AR(1,1) bubble
sim_msbubble(): Simulation of a Markov-switching present-value bubble
sim_falsebubble(): Simulation of a deterministic technology-adoption "false bubble" null

Helpers

psy_minw() psy_ds(): Helper functions in accordance to PSY(2015)
index() `index<-`(): Retrieve/Replace the index
series_names() `series_names<-`(): Retrieve/Replace series names
ps_tb(): Helper function to find tb from the Phillips and Shi (2020)

Extra tidying methods

tidy_join() augment_join(): Tidy into a joint model
tidy_join(<radf_obj>) augment_join(<radf_obj>): Tidy into a joint model