Bias-Corrected Single-Bubble Dating (Kejriwal, Nguyen & Perron 2025)

dating_knp dates a single bubble episode (origination, collapse) by minimising a residual-omission-corrected sum of squared residuals over a three-regime model (unit root, explosive, unit root resuming from a shifted level after an instantaneous collapse). Plain OLS over this model is provably inconsistent – the origination-date estimate converges to the true collapse date, not the origination date – which omit = TRUE (the default) fixes by dropping the single squared residual at the candidate collapse date from the objective before minimising.

Usage

dating_knp(data, trim = 0.05, omit = TRUE)

# S3 method for class 'dating_knp_obj'
autoplot(object, ...)

Arguments

data

A univariate or multivariate numeric time series object, a numeric vector or matrix, or a data.frame. A column may have leading and/or trailing NA values (an uneven/unbalanced panel where series enter or exit the sample at different times) – those periods are filled with NA in badf/bsadf and excluded from that series' adf/sadf/ gsadf. Interior NA values (a gap in the middle of a series) are not supported. When any series is padded this way, the panel statistic (bsadf_panel/gsadf_panel) is not available and is returned as NA, with a warning.

trim

Minimum fraction of the (differenced) sample required in each regime (default 0.05).

omit

Use Kejriwal, Nguyen & Perron's consistency-restoring correction (default TRUE). FALSE gives the plain, provably inconsistent OLS estimator (their Theorem 1) – kept mainly to demonstrate the correction's effect, not for practical dating.

object

An object of class dating_knp_obj, the output of dating_knp.

Value

An object of class dating_knp_obj: a list with origination, collapse (dates) and delta (the fitted explosive AR coefficient).

Note

This is a residual-sum-of-squares model-selection dating procedure, not a hypothesis test – it needs no critical values at all.

Returns its own class (not radf_obj), so it does not plug into summary()/\link{datestamp}/tidy/autoplot – prints its own dating table (model, origination, collapse, recovery) – see vignette("naming-and-analysis", package = "exuber") for the full picture of which functions do and don't fit that pipeline.

Status

References

Kejriwal, M., Nguyen, L., & Perron, P. (2025). An improved procedure for retrospectively dating the emergence and collapse of bubbles. Journal of Time Series Analysis, 46(5), 867-883.

See also

dating_hls, dating_pdc for related SSR-based dating approaches.

Examples

# \donttest{
res <- dating_knp(sim_data$psy1, trim = 0.05)
print(res)
#> 
#> ── dating_knp (n = 100, trim = 0.05, omit = TRUE) ──────────────────────────────
#> 
#>    series  origination  collapse  delta
#>   series1           41        55  0.964
#> 
autoplot(res)


# Compare the bias-corrected estimate against the plain (inconsistent) OLS
# one, layering an extra reference line onto the internal autoplot() output
res_plain <- dating_knp(sim_data$psy1, trim = 0.05, omit = FALSE)
autoplot(res) +
  ggplot2::geom_vline(xintercept = as.numeric(res_plain$origination), linetype = 3)

# }