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Wild Bootstrap Inference for IVX Models

Source: R/ivx-boot.R
ivx_boot.Rd

Computes bootstrap p-values for the IVX Wald and t statistics using the residual wild bootstrap (RWB) or the fixed regressor wild bootstrap (FRWB) of Demetrescu et al. (2023). The null hypothesis of no predictability is imposed on the bootstrap samples. RWB rebuilds the regressor from an AR fit and its residuals (multiplied by the same wild multiplier as the predictive-regression residuals, so the innovation correlation is preserved); FRWB keeps the regressors and instruments fixed and only resamples the response.

Usage

ivx_boot(
  object,
  B = 999,
  type = c("rwb", "frwb"),
  ar_max = 5,
  dist = c("rademacher", "normal"),
  seed = NULL,
  cores = 1L
)

# S3 method for class 'ivx_boot'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

Arguments

object

an object of class "ivx" (not "ivx_ar"), fitted without weights.

B

number of bootstrap replications.

type

bootstrap scheme: "rwb" (residual wild bootstrap, recommended for strongly persistent regressors) or "frwb" (fixed regressor wild bootstrap).

ar_max

maximum lag order of the (vector) autoregression fitted to the regressors by the RWB scheme; the order is selected by BIC (Remark 24).

dist

distribution of the wild multipliers.

seed

optional integer seed. With cores > 1 the L'Ecuyer-CMRG streams of the parallel package are used, so results are reproducible for a given seed and cores but differ from the serial run.

cores

number of CPU cores. Uses forking on Unix and a PSOCK cluster on Windows (the package must be installed for the workers to load it).

x

an object of class "ivx_boot".

digits

minimal number of significant digits.

...

further arguments passed to printCoefmat().

Value

an object of class "ivx_boot": a list with the observed statistics, the bootstrap distributions (boot), and bootstrap p-values (p.value). p.value$tstat has one column per alternative: two-sided, beta < 0 and beta > 0.

References

Demetrescu, M., Georgiev, I., Rodrigues, P. M. M., & Taylor, A. M. R. (2023). Extensions to IVX methods of inference for return predictability. Journal of Econometrics, 237(2), 105271.

Examples

mod <- ivx(Ret ~ DP + TBL, data = kms)
ivx_boot(mod, B = 199, seed = 1)
#> 
#> Call:
#> ivx(formula = Ret ~ DP + TBL, data = kms, horizon = 1)
#> 
#> Residual wild bootstrap, B = 199
#> 
#> Coefficients (bootstrap p-values):
#>      Estimate t value Wald Ind Pr(> chi) Pr(t < 0) Pr(t > 0)
#> DP   0.006145   1.349    1.819    0.3668    0.6935     0.307
#> TBL -0.080717  -1.399    1.957    0.2060    0.0804     0.920
#> 
#> Joint Wald statistic: 3.644, bootstrap p-value 0.3266
#>