dcce 0.4.0

New estimators

• Interactive Fixed Effects (IFE) estimator of Bai (2009), via model = "ife" in dcce(). Iterates between pooled OLS and principal-component extraction of factors/loadings until convergence. The number of factors can be specified or selected automatically via the BIC3 criterion of Bai & Ng (2002). Unlike CCE, IFE estimates factors and loadings directly rather than proxying them with cross-sectional averages.

• Pooled CCE (CCEP) estimator, via model = "ccep" in dcce(). Constrains slopes to be identical across units (precision-weighted pooled OLS with cross-sectional averages), as proposed by Pesaran (2006) alongside the existing CCE-MG. More efficient than CCE-MG when slopes are truly homogeneous.

New diagnostics

• Dumitrescu-Hurlin (2012) panel Granger causality test via the new exported function granger_test(). Reports the W-bar statistic (cross-sectional average of unit Wald statistics), the Z-bar (large-T standardised), and the Z-bar tilde (small-sample adjusted) statistics.

• IPS and LLC panel unit root tests via panel_ur_test(). Implements Im, Pesaran & Shin (2003) IPS t-bar and Levin, Lin & Chu (2002) LLC common-root tests. These do not correct for CSD (use cips_test() for that); they are included as standard benchmarks.

• Pedroni and Kao panel cointegration tests via panel_coint_test(). Reports the Pedroni (1999, 2004) group-mean t and group-mean rho statistics, or the Kao (1999) ADF statistic.

New features

• Impulse response functions via the new exported function irf(). Computes IRFs from fitted dynamic panel models (DCCE, CS-ARDL, PMG) using the MG ARDL lag polynomial. Optional cross-section bootstrap for confidence bands. Includes print.dcce_irf() and plot.dcce_irf() methods.

• Half-panel jackknife bias correction (Chudik & Pesaran 2015) via bias_correction = "half_panel_jackknife" in dcce(). Splits each unit’s time series in half, fits on each half, and corrects the full-sample MG estimate: b_hpj = 2b_full - 0.5(b_half1 + b_half2). Targets the Nickell bias in dynamic CCE.

Infrastructure

• GitHub Actions CI added (.github/workflows/R-CMD-check.yaml): runs R CMD check --as-cran on Ubuntu, Windows, and macOS on every push/PR to main.
• pkgdown site configuration added (_pkgdown.yml) with grouped reference index.
• cran-comments.md added for CRAN submission readiness.

References

• Bai, J. (2009). Panel data models with interactive fixed effects. Econometrica, 77(4), 1229-1279.
• Dumitrescu, E.-I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29(4), 1450-1460.
• Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53-74.
• Kao, C. (1999). Spurious regression and residual-based tests for cointegration in panel data. Journal of Econometrics, 90(1), 1-44.
• Levin, A., Lin, C.-F., & Chu, C.-S. J. (2002). Unit root tests in panel data. Journal of Econometrics, 108(1), 1-24.
• Pedroni, P. (2004). Panel cointegration. Econometric Theory, 20(3), 597-625.

dcce 0.3.2

Bug fixes

• structural_break_test(type = "unknown") now uses the correct asymptotic distribution for the sup-Wald statistic. The previous implementation applied a Bonferroni correction over the candidate breakdate grid, which was wildly over-conservative and routinely inflated borderline p-values all the way to 1. The sup-Wald statistic has a non-standard asymptotic distribution (supremum of a squared Brownian bridge) whose critical values were tabulated by Andrews (1993, Econometrica 61(4), Table I). dcce now ships those critical values as internal data and reports p-values by interpolating on the log-scale between the tabulated 1%, 5%, and 10% levels. Values above the 1% critical value are reported as p <= 0.01; below the 10% critical value they are reported as p > 0.10.
• print.dcce_break() for unknown-date tests now displays the full set of Andrews critical values next to the sup-Wald statistic, and formats the p-value as a human-readable bracket (“> 0.10”, “<= 0.01”, or the interpolated value) instead of a bare number. The known-date (Chow) path is unchanged and still uses the standard chi-square p-value.
• The dcce_break object now carries a critical_values element (a named vector with cv10, cv05, cv01) and a trim element so downstream code and custom print methods can access them.
• Silenced Armadillo runtime warnings. src/unit_ols.cpp now defines ARMA_WARN_LEVEL 0 in addition to ARMA_DONT_PRINT_ERRORS, fully suppressing the solve(): system is singular messages that used to leak through to the console during structural_break_test() and near-rank-deficient CCE fits. The R-level .unit_ols() fallback still handles rank deficiency via pinv() and continues to log its own cli_warn diagnostics where appropriate.

Internal

• New tests in test-structural-break.R covering the Andrews critical-value lookup, p-value interpolation, and a regression test reproducing the v0.3.1 Bonferroni blow-up (sup-Wald = 7.20, q = 3, pi0 = 0.15) to lock in the fix.

dcce 0.3.1

Bug fixes / ergonomics

• New dcce_bootstrap() alias. broom exports a function called bootstrap() (for resampling data frames) that has a completely different signature from dcce::bootstrap(). If broom is loaded after dcce, the broom version masks ours on the search path and calls of the form bootstrap(fit, type = "crosssection", reps = 199) fail with an “unused arguments” error. This release adds dcce_bootstrap(), an exported alias with identical semantics to dcce::bootstrap() but a conflict-free name. bootstrap() itself is unchanged and still available; users who previously wrote dcce::bootstrap(fit, ...) can keep doing so.
• ?bootstrap now documents the conflict and three workarounds.
• README updated with the conflict note under “Verify your installation”.

dcce 0.3.0

New features

• dcce_rolling() for rolling-window estimation. Fits a sequence of dcce() models on overlapping time windows and returns a dcce_rolling object containing the full list of fits and a tidy tibble of coefficients indexed by window end-date. Includes print.dcce_rolling() and a plot.dcce_rolling() method that draws one coefficient-path panel per regressor with a 95% confidence ribbon. Useful for detecting parameter drift and regime shifts in long panels.

• absorb argument for high-dimensional fixed effects. A new argument to dcce() that accepts a one-sided formula (or character vector) naming grouping factors to project out of and before the main unit loop runs. A single factor uses the within transformation; multiple factors use the alternating projections of Guimaraes & Portugal (2010) / Correia (2016). The unit fixed effects of CCE estimators are still kept via unit intercepts — absorb is for additional categorical effects on top of the cross-section (e.g. industry, region, sub-period dummies).

• Spatial CCE via the new spatial_weights argument. When supplied with an row-normalised weight matrix, dcce() replaces the global cross-sectional averages of classical CCE with local, unit-specific weighted averages . The matrix is automatically row-normalised, its diagonal is zeroed, and rows/columns are aligned to the panel’s unit identifiers if they are named. This enables spatial CCE estimation that respects the topology of cross-sectional dependence (geographical contiguity, trade links, input-output connections).

• structural_break_test() for panel structural breaks. New exported function implementing a Wald-type Chow test at a known break date, a sup-Wald test over a trimmed candidate window for an unknown break date, and a sequential Bai & Perron (1998) procedure for multiple breaks. Returns a dcce_break object with the test statistic, p-value, estimated break date(s), the full Wald profile across candidates, and fitted dcce_fit objects for the pre- and post-break regimes. Works with any base estimator supported by dcce(). References: Andrews (1993); Bai & Perron (1998); Ditzen, Karavias & Westerlund (2024). This closes the gap with Stata’s xtbreak package.

Internal

• Added R/rolling.R, R/absorb.R, R/spatial_cce.R, and R/structural_break.R.
• Added ARMA_DONT_PRINT_ERRORS to src/unit_ols.cpp to silence Armadillo diagnostics on near-singular XtX matrices. The R-level .unit_ols() fallback still handles rank deficiency via pinv().
• 60+ new test assertions across four new test files.

References

• Andrews, D. W. K. (1993). Tests for parameter instability and structural change with unknown change point. Econometrica, 61(4), 821-856.
• Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47-78.
• Correia, S. (2016). Linear models with high-dimensional fixed effects: An efficient and feasible estimator. Working paper.
• Ditzen, J., Karavias, Y., & Westerlund, J. (2024). Multiple structural breaks in interactive effects panel data models. Journal of Applied Econometrics.
• Guimaraes, P., & Portugal, P. (2010). A simple feasible procedure to fit models with high-dimensional fixed effects. Stata Journal, 10(4), 628-649.

dcce 0.2.0

New features

• RcppArmadillo C++ inner OLS loop for the unit-level estimator. The pure-R .unit_ols() is retained as an automatic fallback, and the new fast = TRUE argument to dcce() controls which path is used. Pure-R and C++ paths are numerically identical to well under the 1e-6 tolerance on all bundled tests. Compilation requires the standard R toolchain (Rtools on Windows, Xcode command-line tools on macOS).

• Parallel unit-level estimation via the new n_cores argument to dcce(). When n_cores > 1L on Unix/macOS, the unit loop is dispatched to parallel::mclapply(); on Windows the argument is silently ignored.

• Augmented Mean Group (AMG) estimator (Eberhardt & Teal 2010; Bond & Eberhardt 2013), available as model = "amg" in dcce(). The estimator extracts a Common Dynamic Process (CDP) from a pooled first-difference regression with time dummies, cumulates it within each unit, and adds the level-form CDP as a nuisance regressor in the unit-level OLS. The CDP is stored on the fit object as fit$cdp.

• Westerlund (2007) panel cointegration tests exposed via the new exported function cointegration_test(). Supports all four statistics from Westerlund (2007): Gt, Ga, Pt, Pa. Asymptotic p-values use the Westerlund (2007) Table 3 critical values; a cross-sectional bootstrap path (n_bootstrap > 0) is also provided for dependence-robust inference. Returns a dcce_cointegration object with a dedicated print method.

• dcce_workflow() diagnostic pipeline that runs the recommended pre-estimation sequence and returns a structured report plus a printable recommended dcce() call. Steps include the panel summary, CIPS unit root tests on each variable, a pooled CD test, (conditionally) the Westerlund cointegration test, the rank condition classifier, and IC-based CSA lag selection. Returns a dcce_workflow object.

• marginaleffects compatibility. S3 methods on the internal marginaleffects generics (get_coef, get_vcov, get_predict, find_predictors, find_response) are registered dynamically in .onLoad() when the marginaleffects package is available, enabling avg_slopes(), avg_predictions(), and hypotheses() on dcce_fit objects. marginaleffects is in Suggests only, so the integration is a zero-cost soft dependency.

• predict.dcce_fit() now supports newdata. When a new data frame is supplied, predictions are computed using the Mean Group coefficients on the structural regressors.

Internal

• Added src/unit_ols.cpp with RcppArmadillo implementations of unit_ols_cpp() and batch_ols_cpp().
• Added src/Makevars and src/Makevars.win linking to R’s LAPACK_LIBS, BLAS_LIBS, and FLIBS.
• Added Rcpp (>= 1.0.0) to Imports; added Rcpp and RcppArmadillo to LinkingTo.
• Added marginaleffects and parallel to Suggests.
• Added .run_unit_loop() dispatcher in R/est_mg.R.
• Added .onLoad() hook in R/zzz.R for dynamic method registration.

References

• Bond, S., & Eberhardt, M. (2013). Accounting for unobserved heterogeneity in panel time series models. Economics Letters.
• Eberhardt, M., & Teal, F. (2010). Productivity Analysis in Global Manufacturing Production. Economics Series Working Paper 515, Oxford.
• Westerlund, J. (2007). Testing for Error Correction in Panel Data. Oxford Bulletin of Economics and Statistics, 69(6), 709-748.

dcce 0.1.0

• Initial release.
• Mean Group (MG), Common Correlated Effects (CCE), and Dynamic CCE (DCCE) estimators.
• Regularized CCE (rCCE) with Ahn-Horenstein ER/GR criteria.
• Long-run estimators: PMG, CS-DL, CS-ARDL.
• Cross-sectional dependence tests: CD, CDw, PEA, CD*.
• Exponent of cross-sectional dependence (BKP 2016, 2019).
• Information criteria for CSA selection.
• Rank condition classifier.
• Cross-section and wild bootstrap inference.