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Changelog

Source: NEWS.md

ivx 2.0.0

  • New vignette “Choosing a test: a decision workflow” (vignette("workflow")) that walks from the plain ivx() fit through the diagnostics (ac_test(), delta(), ARCH check) to the extension each one calls for.

  • print() of ac_test() on a numeric vector no longer errors (it indexed a Breusch-Godfrey column that only the ivx method computes).

  • ac_test() and ac_test_*() now stop with an informative error on ivx_ar, ivx_ra, ivx_qr, ivx_iv and arm fits, which do not store the OLS residuals the tests use; previously they failed with an obscure error.

  • New nobs() method for ivx objects (and the extensions inheriting the class).

  • New vignette “Rolling IVX tests for bubble detection” showing how to build the rolling-window IVX test of Pavlidis, Paya & Peel (2017) from ivx() (#2).

  • ivx() and ivx_fit() gain beta, cz and bandwidth arguments that expose the IVX instrument tuning (previously hard-coded to the Kostakis et al. (2015) values) and the Newey-West bandwidth.

  • ivx() gains robust = TRUE for Eicker-White (heteroskedasticity-robust) IVX standard errors (Demetrescu, Georgiev, Rodrigues & Taylor, 2023).

  • ivx() and ivx_fit() gain lag_y = TRUE: the lag-augmented IVX regression of Demetrescu (2014), which adds the lagged dependent variable (instrumented by itself) to raise local power under strong persistence and endogeneity. The joint Wald statistic tests the predictors only.

  • summary() coefficient tables now report Std. Error and t value next to the individual Wald statistics; the fitted object stores se and tstat. Breaking: the table gains two columns, so code indexing coef(summary(x)) by position must use column names ("Wald Ind", "Pr(> chi)") instead.

  • New ivx_boot() implementing the residual wild bootstrap and fixed regressor wild bootstrap of Demetrescu et al. (2023), returning bootstrap p-values for the joint and individual Wald statistics and one-sided t-tests. Supports cores > 1 via the package; the regressor recursion of the residual wild bootstrap runs in C++.

  • New ivx_ra() / ivx_ra_fit(): the residual-augmented (bias-reduced) IVX estimator of Demetrescu & Rodrigues (2022) with its heteroskedasticity-robust standard errors; returns an ivx object so summary(), vcov() etc. apply. ivx_ra() gains horizon: for horizon > 1 it is the transformed-regression long-horizon test of Demetrescu, Rodrigues & Taylor (2023), which handles the overlap of the long-horizon regression without HAC estimation.

  • New ivx_qr() / ivx_qr_fit(): the IVX-QR quantile predictability test of Lee (2016, Proposition 3.2) via (in Suggests); returns the estimated QR endogeneity rho_tau for the paper’s tuning rule.

  • New ivx_qr_boot(): moving block bootstrap percentile intervals and p-values for IVX-QR (Fan & Lee, 2019), robust to conditional heteroskedasticity and to the sparsity estimate.

  • New ivx_episodic(): subsample (rolling, forward and backward recursive) IVX tests for pockets of predictability with sup/inf functionals and wild bootstrap p-values (Demetrescu et al. 2022, 2023 Section 3.2).

  • Documented that the long-horizon statistic (horizon > 1) is the modified IVX-Wald of Kostakis, Magdalinos & Stamatogiannis (2023), eqs (15)/(23), after auditing the implementation against the paper.

  • New ivx_sys() / ivx_sys_fit(): systems of predictive regressions with a matrix response (cbind(y1, y2) ~ x), short and long horizon, with the Kronecker-form IVX-Wald covariance of Kostakis et al. (2023); reports joint, per-equation and individual Wald statistics.

  • New vignettes for each methodology (ivx, ivx-sys, ivx-ar, ivx-ra, ivx-qr, robust-inference, ivx-episodic) with the underlying statistics, replication results and caveats; pkgdown site reorganised (Bootstrap 5, MathJax rendering, grouped reference and articles).

  • New ivx_iv() / ivx_iv_fit(): the 2SLS predictability tests of Breitung & Demetrescu (2015) with fractional-difference, long-difference and sine instruments and their recommended combination (IVcomb), Eicker-White standard errors.

  • New arm() / arm_fit(): the multipredictor augmented regression method of Amihud, Hurvich & Wang (2009) - reduced-bias OLS with Nicholls-Pope corrected VAR(1) residuals as control variables and the paper’s covariance estimator; a non-IVX benchmark for stationary persistent predictors.

  • New hlt_test(): the hybrid switching t-test of Harvey, Leybourne & Taylor

      • standard or quasi-GLS-demeaned t-ratio with the paper’s conservative critical values under strong persistence, normal critical values under weak persistence (ADF/MBIC switch).

  • New el_test(): the unified empirical likelihood test of Liu, Yang, Cai & Peng (2019) for the predictive regression augmented with the lagged difference of the predictor; chi-square profile EL ratios whatever the persistence of the predictor, no tuning parameters.

  • New cy_test(): the Bonferroni Q-test of Campbell & Yogo (2006) (the feasible Cavanagh, Elliott & Stock 1995 approach): DF-GLS confidence interval for the largest root inverted from simulated local-to-unity quantiles, Table 2 levels, Q-estimates with the AR(p) correction of Appendix A.

  • New elliott_cf(): the control-function predictive regression of Elliott

    1. with user-supplied orthogonalising covariates and their lags, Wald test with Eicker-White standard errors and the remaining innovation correlation as a diagnostic.

  • Fixed: weighted fits ignored the horizon argument; zero weights made ivx() fail (the dropped observations now get NA residuals and fitted values, with the same coefficients as fitting on the kept rows).

  • Fixed: ac_test_bg() dispatched to the Box-Pierce method; ac_test_lb() and ac_test_bp() reported wrong p-values for non-consecutive lag vectors; the Breusch-Godfrey result now has the same ac_test_ class and pval attribute as the other tests. case.names() no longer returns an empty vector.

  • Deprecated: drop1(), add1() and step() on ivx objects. They compare residual sums of squares of an IV estimator, which is not a valid selection criterion; use the IVX Wald tests in summary().

  • All formula interfaces share one model-frame routine, so - 1 in a formula warns consistently and a matrix response is rejected consistently.

  • extract() (texreg) now reports IVX standard errors instead of Wald statistics in the se slot.

  • ivx_ar() and ivx_ar_fit() accept the same beta, cz, bandwidth and robust arguments as ivx().

ivx 1.1.1

CRAN release: 2025-09-20

  • Patch version to fix minor issues.

ivx 1.1.0

CRAN release: 2020-11-24

  • Added ivx_ar that implements Yang, B., Long, W., Peng, L., & Cai, Z. (2020) new instrumental variable based Wald statistic which accounts for serial correlation and heteroscedasticity in the error terms of the linear predictive regression model.
  • Added the Yang et al. (2020) dataset named ylpc.
  • Renamed the monthly and quarterly dataset into kms and kms_quarterly
  • Removed dependency on tibble and magrittr.
  • Added texreg functionality that converts regression output to LaTeX or HTML tables. Specifically added extract methods for ivx and ivx_ar, which coefficients and GOF measures from a statistical object.

ivx 1.0.0

CRAN release: 2019-05-04

  • Added a NEWS.md file to track changes to the package.