Package 'dcce'

Description

Computes bootstrap standard errors and confidence intervals for dcce_fit objects using either cross-section or wild bootstrap.

Usage

bootstrap(
  object,
  type = c("crosssection", "wild"),
  reps = 500L,
  percentile = TRUE,
  cfresiduals = FALSE,
  seed = NULL
)

Arguments

Value

An object of class dcce_boot with elements:

se_boot

Bootstrap standard errors.

ci_lower

Percentile CI lower bound (if percentile=TRUE).

ci_upper

Percentile CI upper bound.

b_boot

B x K matrix of bootstrap coefficient draws.

reps

Number of repetitions.

type

Bootstrap type.

Note

Naming conflict with broom::bootstrap. The broom package also exports a function called bootstrap() (for resampling data frames), with a completely different signature. If you load broom after dcce, broom::bootstrap will mask dcce::bootstrap on the search path and calls to bootstrap(fit, type = ..., reps = ...) will fail with an "unused arguments" error. To avoid the conflict you can either (a) use the namespace prefix dcce::bootstrap(fit, ...), (b) load broom before dcce so dcce ends up higher in the search path, or (c) use the conflict-free alias dcce_bootstrap(fit, ...) which is exported by dcce and has the same semantics.

Examples

set.seed(42)
df <- data.frame(
  id = rep(1:10, each = 30),
  t  = rep(1:30, 10),
  y  = rnorm(300),
  x  = rnorm(300)
)
fit <- dcce(df, "id", "t", y ~ x, model = "mg", cross_section_vars = NULL)
boot_res <- bootstrap(fit, reps = 50)
print(boot_res)

Description

Implements the cross-sectionally augmented IPS (CIPS) panel unit root test of Pesaran (2007), which allows for cross-sectional dependence through a single unobserved common factor. For each unit, a cross-sectionally augmented Dickey-Fuller (CADF) regression is run:

Usage

cips_test(x, ...)

## S3 method for class 'matrix'
cips_test(x, ..., lags = 0L, trend = FALSE)

## S3 method for class 'dcce_fit'
cips_test(x, ..., lags = 0L, trend = FALSE)

## Default S3 method:
cips_test(
  x,
  ...,
  data = NULL,
  unit_index = NULL,
  time_index = NULL,
  lags = 0L,
  trend = FALSE
)

Arguments

Details

$\Delta y_{it} = a_i + b_i y_{i,t-1} + c_i \bar{y}_{t-1} + d_i \Delta \bar{y}_t + \sum_{j=1}^{p} \rho_{ij} \Delta y_{i,t-j} + \sum_{j=0}^{p} \delta_{ij} \Delta \bar{y}_{t-j} + u_{it}.$

The CIPS statistic is the cross-sectional average of the unit-level t-statistics for $b_i = 0$ (the CADF statistic). Critical values come from Pesaran (2007, Table II(b), constant case) and Pesaran (2007, Table II(c), constant + trend case). The null hypothesis is that all series contain a unit root.

Value

An object of class dcce_cips with elements:

statistic

The CIPS statistic (truncated cross-sectional average).

p_value

Approximate p-value from Pesaran (2007) critical values.

unit_stats

Per-unit truncated CADF t-statistics.

N

Number of units.

T

Time dimension.

lags

Number of augmentation lags.

trend

Whether a trend was included.

References

Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22(2), 265-312.

Examples

set.seed(1)
N <- 20; T <- 30
f <- cumsum(rnorm(T))
X <- matrix(NA, N, T)
for (i in seq_len(N)) X[i, ] <- cumsum(rnorm(T)) + 0.5 * f
cips_test(X)

Description

Extract coefficients from a dcce_fit object

Usage

## S3 method for class 'dcce_fit'
coef(object, type = c("mg", "unit"), ...)

Arguments

Value

A named numeric vector (for "mg") or a tibble (for "unit").

Description

Implements the four error-correction-based panel cointegration tests of Westerlund (2007): two group-mean statistics (Ga, Gt) and two panel statistics (Pa, Pt). The null hypothesis is no cointegration between the dependent variable and the regressors.

Usage

cointegration_test(
  data,
  unit_index,
  time_index,
  formula,
  lags = 1L,
  leads = 1L,
  test = c("ga", "gt", "pa", "pt"),
  n_bootstrap = 0L,
  seed = NULL,
  show_progress = FALSE
)

Arguments

Details

For each unit $i$, an error-correction regression is fitted

$\Delta y_{it} = \delta_i d_t + \alpha_i y_{i,t-1} + \lambda_i' x_{i,t-1} + \sum_{j=1}^{p_i} \alpha_{ij} \Delta y_{i,t-j} + \sum_{j=-q_i}^{p_i} \gamma_{ij}' \Delta x_{i,t-j} + e_{it},$

and the t-statistic for $\alpha_i = 0$ is used to construct the four test statistics:

• Gt: group-mean of the $\hat\alpha_i / SE(\hat\alpha_i)$.

• Ga: group-mean of $T \hat\alpha_i / \hat\alpha_i(1)$ (unnormalised form).

• Pt: pooled t-statistic.

• Pa: pooled alpha-statistic.

A negative, large-magnitude statistic rejects the null of no cointegration. Asymptotic p-values are obtained by linear interpolation on the critical values in Westerlund (2007, Table 3). A bootstrap path (n_bootstrap > 0) is also provided, resampling cross-sectional units with replacement to account for cross-sectional dependence.

Value

An object of class dcce_cointegration with elements statistics (a tibble with columns test, statistic, p_value, method), lags, leads, N, T_bar, and call.

References

Westerlund, J. (2007). Testing for Error Correction in Panel Data. Oxford Bulletin of Economics and Statistics, 69(6), 709-748.

Examples

data(pwt8)
result <- cointegration_test(
  data       = pwt8,
  unit_index = "country",
  time_index = "year",
  formula    = log_rgdpo ~ log_hc + log_ck,
  test       = c("ga", "gt"),
  lags       = 1L
)
print(result)

Description

Confidence intervals for a dcce_fit object

Usage

## S3 method for class 'dcce_fit'
confint(
  object,
  parm = NULL,
  level = 0.95,
  type = c("mg", "lr", "adjustment"),
  ...
)

Arguments

Value

A matrix of confidence intervals with rows corresponding to parameters.

Description

Estimates the exponent of cross-sectional dependence (alpha) using the methods of Bailey, Kapetanios & Pesaran (2016, 2019).

Usage

csd_exp(
  x,
  data = NULL,
  unit_index = NULL,
  time_index = NULL,
  use_residuals = FALSE,
  n_pca = 1L,
  test_size = 0.1,
  tuning = 0.5,
  n_bootstrap = 200L
)

Arguments

Value

An object of class dcce_csd with elements alpha (estimated exponent), se (standard error), ci (confidence interval), method, N, and T_val.

Examples

set.seed(42)
# Matrix of cross-sectionally dependent data
N <- 20; T_val <- 50
f <- rnorm(T_val)
x <- matrix(NA, N, T_val)
for (i in 1:N) x[i,] <- rnorm(1) * f + rnorm(T_val, sd = 0.5)
result <- csd_exp(x, use_residuals = FALSE)
print(result)

Description

Creates a differenced version of a variable for use inside dcce() formulas.

Usage

D(x, k = 1L)

Arguments

Value

A numeric vector of the same length as x with leading NAs.

Examples

x <- c(10, 20, 30, 40, 50)
D(x, 1)   # NA 10 10 10 10
D(x, 2)   # NA NA 20 20 20

Description

Estimates heterogeneous coefficient panel data models with cross-sectional dependence using Mean Group (MG), Common Correlated Effects (CCE), Dynamic CCE (DCCE), and related estimators.

Usage

dcce(
  data,
  unit_index,
  time_index,
  formula,
  model = c("dcce", "cce", "ccep", "mg", "amg", "rcce", "ife", "pmg", "csdl", "csardl"),
  cross_section_vars = ~.,
  cross_section_lags = 0L,
  pooled_vars = NULL,
  include_constant = TRUE,
  unit_trend = FALSE,
  bias_correction = c("none", "jackknife", "recursive", "half_panel_jackknife"),
  long_run_vars = NULL,
  long_run_model = NULL,
  csdl_xlags = 3L,
  absorb = NULL,
  spatial_weights = NULL,
  fast = TRUE,
  n_cores = 1L,
  run_cd_test = FALSE,
  full_sample = FALSE,
  verbose = FALSE,
  ...
)

Arguments

Value

An object of class dcce_fit (and a model-specific subclass).

Examples

# Simple Mean Group estimation
df <- data.frame(
  id = rep(1:10, each = 20),
  t  = rep(1:20, 10),
  y  = rnorm(200),
  x  = rnorm(200)
)
fit <- dcce(df, unit_index = "id", time_index = "t",
            formula = y ~ x, model = "mg", cross_section_vars = NULL)
coef(fit)

Description

Convenience alias for bootstrap with an unambiguous name. broom::bootstrap is a data-frame resampling helper that shares a name with dcce::bootstrap but has a completely different signature. If you load broom after dcce the broom function masks ours on the search path and calls to bootstrap(fit, type = ..., reps = ...) will fail with an "unused arguments" error. dcce_bootstrap() is identical to dcce::bootstrap() but cannot be masked by any other package.

Usage

dcce_bootstrap(
  object,
  type = c("crosssection", "wild"),
  reps = 500L,
  percentile = TRUE,
  cfresiduals = FALSE,
  seed = NULL
)

Arguments

Value

See bootstrap.

See Also

bootstrap

Examples

set.seed(42)
df <- data.frame(
  id = rep(1:10, each = 30),
  t  = rep(1:30, 10),
  y  = rnorm(300),
  x  = rnorm(300)
)
fit <- dcce(df, "id", "t", y ~ x, model = "mg", cross_section_vars = NULL)
dcce_bootstrap(fit, reps = 50)

Description

Fits a sequence of dcce() models on overlapping time windows of the panel, producing a time path of coefficient estimates. Useful for detecting coefficient drift, parameter instability, or regime shifts in long panels.

Usage

dcce_rolling(
  data,
  unit_index,
  time_index,
  formula,
  model = "cce",
  window = 20L,
  step = 1L,
  min_units = 5L,
  verbose = FALSE,
  ...
)

Arguments

Details

At each window the function subsets the panel to observations whose time index falls in $[t_k, t_k + \text{window} - 1]$, runs dcce() with the user-supplied arguments, and collects the Mean Group coefficient vector and its standard errors. The result is returned as a dcce_rolling object containing the full list of fits and a tidy tibble of coefficients indexed by window end-date.

Value

An object of class dcce_rolling containing:

fits

List of dcce_fit objects, one per window (or NULL for windows that failed).

coefficients

A tibble with one row per (window end-date, term) combination, columns window_end, window_start, term, estimate, std.error, conf.low, conf.high.

window

The window length.

step

The step size.

n_windows

Number of successful windows.

call

The original call.

Examples

data(pwt8)
roll <- dcce_rolling(
  data       = pwt8,
  unit_index = "country",
  time_index = "year",
  formula    = d_log_rgdpo ~ log_hc + log_ck + log_ngd,
  model      = "cce",
  cross_section_vars = ~ .,
  window     = 20,
  step       = 2
)
print(roll)

Description

A synthetic panel dataset generated from the dynamic common correlated effects DGP of Chudik and Pesaran (2015), equation (1). Generated with N = 30 cross-sectional units and T = 50 time periods. The true mean group parameters are: autoregressive coefficient 0.50, slope on x 1.00. Factor structure uses a single AR(1) common factor with persistence 0.60.

Usage

dcce_sim

Format

A data frame with 1500 rows and 4 columns:

unit

Cross-sectional unit identifier (integer, 1–30)

time

Time period (integer, 1–50)

y

Simulated dependent variable

x

Simulated regressor (cross-sectionally dependent via common factor)

Details

The true parameter values are stored in the companion object dcce_sim_truth and can be used in tests to verify estimator consistency.

References

Chudik, A. and Pesaran, M. H. (2015). Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors. Journal of Econometrics, 188(2), 393–420.

See Also

dcce_sim_truth for the true parameter values.

Description

A named list containing the true mean group parameter values used to generate dcce_sim. Use in tests to verify that dcce() recovers parameters close to their true values.

Usage

dcce_sim_truth

Format

A named list with elements:

beta1_mg

True mean group autoregressive coefficient (~0.50)

beta2_mg

True mean group slope on x (~1.00)

N

Number of cross-sectional units (30)

T

Number of time periods (50)

seed

Random seed used for generation (20240101)

Description

Runs the recommended pre-estimation diagnostic sequence on a panel regression specification and returns a structured report with a suggested dcce() call. The workflow performs six steps:

Usage

dcce_workflow(
  data,
  unit_index,
  time_index,
  formula,
  max_cr_lags = NULL,
  significance = 0.05,
  verbose = TRUE,
  n_bootstrap = 0L
)

Arguments

Details

1. Panel summary (N, T, balance).

2. Pesaran (2007) CIPS panel unit root test on each variable.

3. Pesaran CD test on raw residuals (pooled OLS) to check for cross-sectional dependence.

4. Westerlund (2007) cointegration test, if at least one variable is non-stationary.

5. Rank condition classifier (De Vos et al. 2024) for a baseline static CCE fit.

6. Information criterion selection of the optimal CSA lag order.

Based on the results, the function chooses a recommended estimator from c("mg", "cce", "dcce", "pmg", "csardl") and returns a printable suggested dcce() call.

Value

An object of class dcce_workflow with elements panel_summary, unit_root, csd_premodel, cointegration, rank_condition, optimal_cr_lags, recommendation, and call.

Examples

data(pwt8)
wf <- dcce_workflow(
  data       = pwt8,
  unit_index = "country",
  time_index = "year",
  formula    = log_rgdpo ~ log_hc + log_ck + log_ngd,
  verbose    = FALSE
)
print(wf)

Description

Extract fitted values from a dcce_fit object

Usage

## S3 method for class 'dcce_fit'
fitted(object, ...)

Arguments

Value

A numeric vector of fitted values (y-hat), or NULL if not available.

Description

Returns a single-row tibble of model summary statistics, compatible with the broom package.

Usage

## S3 method for class 'dcce_fit'
glance(x, ...)

Arguments

Value

A single-row tibble.

Description

Tests whether $x$ Granger-causes $y$ in a heterogeneous panel, following Dumitrescu & Hurlin (2012). For each unit $i$ a bivariate VAR-type regression is fitted:

$y_{it} = \alpha_i + \sum_{k=1}^{K} \gamma_{ik} y_{i,t-k} + \sum_{k=1}^{K} \beta_{ik} x_{i,t-k} + u_{it},$

and the individual Wald statistic for $H_0^{(i)}: \beta_{i1} = \cdots = \beta_{iK} = 0$ is computed.

Usage

granger_test(data, unit_index, time_index, y, x, lags = 1L)

Arguments

Details

The panel statistics are:

W-bar

The cross-sectional average of the N unit-level Wald statistics.

Z-bar

Standardised version: $\tilde{Z} = \sqrt{N/(2K)} (W\text{-bar} - K) \xrightarrow{d} \mathcal{N}(0,1)$.

Z-bar tilde

Small-sample adjusted version using $E[W_i]$ and $Var(W_i)$ from the $F(K, T-3K-1)$ distribution.

Value

An object of class dcce_granger with elements:

W_bar

Mean Wald statistic across units.

Z_bar

Standardised statistic (large-T).

Z_bar_tilde

Small-sample adjusted statistic.

p_value_Z

p-value for Z-bar.

p_value_Zt

p-value for Z-bar tilde.

unit_wald

Named vector of per-unit Wald statistics.

N

Number of units used.

T_bar

Average time-series length.

lags

Lag order.

References

Dumitrescu, E.-I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29(4), 1450–1460.

Examples

data(pwt8)
gc <- granger_test(
  data = pwt8, unit_index = "country", time_index = "year",
  y = "d_log_rgdpo", x = "log_ck", lags = 1
)
print(gc)

Description

Tests the null that the pooled and MG estimators are both consistent (i.e. slopes are homogeneous and the pooled estimator is efficient) against the alternative that slopes are heterogeneous and only MG is consistent. The test statistic is

$H = (\hat\beta_{MG} - \hat\beta_{pool})' [V_{MG} - V_{pool}]^{-1} (\hat\beta_{MG} - \hat\beta_{pool}) \sim \chi^2_k.$

Usage

hausman_test(object)

Arguments

Value

An object of class dcce_hausman with the test statistic, degrees of freedom, and p-value.

Description

Computes impulse response functions (IRFs) from a fitted dynamic panel model (DCCE, CS-ARDL, or PMG). The IRF traces the response of the dependent variable to a one-unit shock in a specific regressor over a given number of horizons, using the Mean Group ARDL coefficient estimates and the autoregressive lag polynomial.

Usage

irf(object, impulse, horizon = 10L, boot_reps = 200L, seed = NULL)

Arguments

Details

Confidence bands are computed via the cross-section bootstrap: units are resampled with replacement, the MG coefficients are recomputed, and the IRF is re-traced. The 2.5 and 97.5 percent quantiles of the bootstrap distribution form the 95 percent band.

Value

An object of class dcce_irf containing the impulse response path ($irf), optional bootstrap lower/upper bands, and metadata (impulse variable name and horizon length).

Examples

data(dcce_sim)
fit <- dcce(
  data = dcce_sim, unit_index = "unit", time_index = "time",
  formula = y ~ L(y, 1) + x,
  model = "dcce", cross_section_vars = ~ ., cross_section_lags = 3
)
ir <- irf(fit, impulse = "x", horizon = 10, boot_reps = 0)
print(ir)

Description

Creates a lagged version of a variable for use inside dcce() formulas. This function is evaluated during formula processing by dcce() and should not be called directly on raw vectors outside of a dcce formula context.

Usage

L(x, k = 1L)

Arguments

Value

A numeric vector of the same length as x with leading NAs.

Examples

x <- c(10, 20, 30, 40, 50)
L(x, 1)   # NA 10 20 30 40
L(x, 2)   # NA NA 10 20 30

Description

Creates multiple lagged versions of a variable from lag k0 to lag k1. Useful for distributed lag specifications.

Usage

Lrange(x, k0, k1)

Arguments

Value

A matrix with columns L[k0] through L[k1].

Examples

x <- c(10, 20, 30, 40, 50)
Lrange(x, 0, 2)   # 3 columns: lag 0, lag 1, lag 2

Description

Implements simplified versions of the Pedroni (1999, 2004) and Kao (1999) residual-based panel cointegration tests.

Usage

panel_coint_test(
  data,
  unit_index,
  time_index,
  formula,
  test = c("pedroni", "kao"),
  lags = 1L
)

Arguments

Details

Pedroni: Fits unit-level OLS regressions of $y$ on $x_1, \ldots, x_K$, extracts the residuals, runs an ADF regression on each unit's residual series, and averages the t-statistics. Reports the group-mean t-statistic (group_t) and the group-mean rho-statistic (group_rho).

Kao: Similar but pools the residuals rather than averaging unit-level statistics. Reports a single ADF t-statistic on the demeaned pooled residuals.

Both tests have null hypothesis no cointegration. A large negative statistic rejects the null.

Value

An object of class dcce_cointegration_extra with elements test, statistics (a tibble), N, T_bar, lags.

References

Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics, 61(S1), 653–670.

Pedroni, P. (2004). Panel cointegration: asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econometric Theory, 20(3), 597–625.

Kao, C. (1999). Spurious regression and residual-based tests for cointegration in panel data. Journal of Econometrics, 90(1), 1–44.

Examples

data(pwt8)
panel_coint_test(
  data = pwt8, unit_index = "country", time_index = "year",
  formula = log_rgdpo ~ log_hc + log_ck,
  test = "pedroni", lags = 1
)

Description

Implements the Im, Pesaran & Shin (2003) IPS t-bar test and the Levin, Lin & Chu (2002) LLC common-root test for panel unit roots. Neither test corrects for cross-sectional dependence — use cips_test for CSD-robust testing.

Usage

panel_ur_test(x, ...)

## S3 method for class 'matrix'
panel_ur_test(x, ..., test = c("ips", "llc"), lags = 0L, trend = FALSE)

## Default S3 method:
panel_ur_test(
  x,
  ...,
  data = NULL,
  unit_index = NULL,
  time_index = NULL,
  test = c("ips", "llc"),
  lags = 0L,
  trend = FALSE
)

Arguments

Details

IPS: fits unit-level ADF regressions, averages the t-statistics, and standardises using tabulated moments from Im et al. (2003, Table 2).

LLC: fits unit-level ADF regressions, extracts the t-statistic from a pooled regression of adjusted $\Delta y$ on adjusted $y_{t-1}$, standardised to N(0,1).

Value

An object of class dcce_unit_root with elements test, statistic, p_value, N, T, lags, trend.

References

Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53–74.

Levin, A., Lin, C.-F., & Chu, C.-S. J. (2002). Unit root tests in panel data: asymptotic and finite-sample properties. Journal of Econometrics, 108(1), 1–24.

Examples

set.seed(1)
X <- matrix(cumsum(rnorm(20 * 30)), 20, 30)
panel_ur_test(X, test = "ips")
panel_ur_test(X, test = "llc")

Description

Computes cross-sectional dependence test statistics for panel data residuals. Implements the Pesaran (2015) CD test, CDw (Juodis & Reese 2022), PEA (Fan et al. 2015), and CD* (Pesaran & Xie 2021).

Usage

pcd_test(x, ...)

## S3 method for class 'dcce_fit'
pcd_test(
  x,
  ...,
  test = c("pesaran", "cdw", "cdwplus", "pea", "cdstar"),
  n_reps = 500L,
  n_pca = 1L
)

## S3 method for class 'data.frame'
pcd_test(
  x,
  ...,
  unit_index = NULL,
  time_index = NULL,
  test = c("pesaran", "cdw", "pea", "cdstar"),
  n_reps = 500L,
  n_pca = 1L
)

## S3 method for class 'matrix'
pcd_test(
  x,
  ...,
  test = c("pesaran", "cdw", "pea", "cdstar"),
  n_reps = 500L,
  n_pca = 1L
)

## Default S3 method:
pcd_test(
  x,
  ...,
  data = NULL,
  unit_index = NULL,
  time_index = NULL,
  test = c("pesaran", "cdw", "cdwplus", "pea", "cdstar"),
  n_reps = 500L,
  n_pca = 1L
)

Arguments

Value

An object of class dcce_cd containing:

statistics

A data.frame with columns test, statistic, p_value.

N

Number of cross-sectional units.

T_bar

Average time dimension.

rho_ij

Matrix of pairwise correlations (if retained).

Examples

set.seed(42)
df <- data.frame(
  id = rep(1:10, each = 20),
  t  = rep(1:20, 10),
  e  = rnorm(200)
)
cd <- pcd_test(df$e, data = df, unit_index = "id", time_index = "t",
               test = "pesaran")
print(cd)

Description

Produces coefficient distribution plots (one histogram per regressor) showing the unit-level estimates and the MG mean.

Usage

## S3 method for class 'dcce_fit'
plot(x, which = c("coef", "resid"), ...)

Arguments

Value

Invisibly returns x.

Description

Plot a dcce_irf object

Usage

## S3 method for class 'dcce_irf'
plot(x, ...)

Arguments

Value

Invisibly returns x.

Description

Produces one line per regressor showing the rolling-window coefficient against the window end-date, with a 95% confidence ribbon built from the unit-loop standard errors.

Usage

## S3 method for class 'dcce_rolling'
plot(x, terms = NULL, ...)

Arguments

Value

Invisibly returns x.

Description

Predict from a dcce_fit object

Usage

## S3 method for class 'dcce_fit'
predict(object, newdata = NULL, type = c("response", "xb"), ...)

Arguments

Value

A numeric vector of predictions.

Description

Print method for dcce_boot objects

Usage

## S3 method for class 'dcce_boot'
print(x, ...)

Arguments

Value

Invisibly returns x.

Description

Print a dcce_break object

Usage

## S3 method for class 'dcce_break'
print(x, ...)

Arguments

Value

Invisibly returns x.

Description

Print method for dcce_cd objects

Usage

## S3 method for class 'dcce_cd'
print(x, ...)

Arguments

Value

Invisibly returns x.

Description

Print a dcce_cips object

Usage

## S3 method for class 'dcce_cips'
print(x, ...)

Arguments

Value

Invisibly returns x.

Description

Print method for dcce_cointegration

Usage

## S3 method for class 'dcce_cointegration'
print(x, ...)

Arguments

Value

Invisibly returns x.

Description

Print a dcce_cointegration_extra object

Usage

## S3 method for class 'dcce_cointegration_extra'
print(x, ...)

Arguments

Value

Invisibly returns x.

Description

Print method for dcce_csd objects

Usage

## S3 method for class 'dcce_csd'
print(x, ...)

Arguments

Value

Invisibly returns x.

Description

Print a dcce_fit object

Usage

## S3 method for class 'dcce_fit'
print(x, ...)

Arguments

Value

Invisibly returns x.

Description

Print a dcce_granger object

Usage

## S3 method for class 'dcce_granger'
print(x, ...)

Arguments

Value

Invisibly returns x.

Description

Print a dcce_hausman object

Usage

## S3 method for class 'dcce_hausman'
print(x, ...)

Arguments

Value

Invisibly returns x.

Description

Print a dcce_irf object

Usage

## S3 method for class 'dcce_irf'
print(x, ...)

Arguments

Value

Invisibly returns x.

Description

Print a dcce_rolling object

Usage

## S3 method for class 'dcce_rolling'
print(x, ...)

Arguments

Value

Invisibly returns x.

Description

Print a dcce_swamy object

Usage

## S3 method for class 'dcce_swamy'
print(x, ...)

Arguments

Value

Invisibly returns x.

Description

Print a dcce_unit_root object

Usage

## S3 method for class 'dcce_unit_root'
print(x, ...)

Arguments

Value

Invisibly returns x.

Description

Print a dcce_workflow object

Usage

## S3 method for class 'dcce_workflow'
print(x, ...)

Arguments

Value

Invisibly returns x.

Description

Panel dataset from Jan Ditzen's xtdcce2 Stata package, originally derived from the Penn World Tables 8. Used in Ditzen (2018, Stata Journal 18:3, 585–617) to illustrate MG, CCE, and DCCE estimation. Contains 93 countries observed from 1960 to 2007 (balanced).

Usage

pwt8

Format

A data frame with 4464 rows and 8 columns:

id

Country numeric identifier

year

Year (1960–2007)

log_rgdpo

Log real GDP (output-side)

log_hc

Log human capital index

log_ck

Log physical capital stock

log_ngd

Log of population growth plus depreciation

country

Country identifier (character)

d_log_rgdpo

First difference of log_rgdpo (GDP growth)

Source

Downloaded from https://github.com/JanDitzen/xtdcce2.

References

Ditzen, J. (2018). Estimating dynamic common-correlated effects in Stata. The Stata Journal, 18(3), 585–617.

Description

Implements the De Vos et al. (2024) rank condition classifier for CCE estimators. Tests whether the rank condition holds (RC=1) which ensures CCE consistency.

Usage

rank_condition(object, criterion = c("er", "gr"))

Arguments

Details

Note: Only valid for static panels. Emits a warning if called on dynamic or PMG models.

Value

An object of class dcce_rank with elements m (number of common factors), g (rank of average factor loadings), RC (1 if rank condition holds, 0 otherwise).

Description

Extract residuals from a dcce_fit object

Usage

## S3 method for class 'dcce_fit'
residuals(object, ...)

Arguments

Value

A numeric vector.

Description

Tests for and estimates structural breaks in heterogeneous panel data models with cross-sectional dependence. Implements a Wald-type Chow test at a known break date, a sup-Wald test for an unknown break date (with trimmed candidate set), and a sequential procedure for multiple breaks following Bai & Perron (1998) adapted to panel CCE settings.

Usage

structural_break_test(
  data,
  unit_index,
  time_index,
  formula,
  model = "cce",
  type = c("unknown", "known"),
  break_date = NULL,
  trim = 0.15,
  n_breaks = 1L,
  test_terms = NULL,
  verbose = FALSE,
  ...
)

Arguments

Details

For each candidate break date $\tau$ the function computes

$W_N(\tau) = (\hat\beta_1(\tau) - \hat\beta_2(\tau))' [V_1(\tau) + V_2(\tau)]^{-1} (\hat\beta_1(\tau) - \hat\beta_2(\tau))$

where $\hat\beta_1$ and $\hat\beta_2$ are the Mean Group coefficients from running the base estimator on the pre-break and post-break sub-samples respectively, and $V_1$, $V_2$ are the corresponding MG variances. Under the null of no break the statistic is distributed $\chi^2_k$ where $k$ is the number of tested coefficients.

The unknown-break-date sup-Wald test reports $\sup_{\tau \in [\tau_1, \tau_2]} W_N(\tau)$, where $[\tau_1, \tau_2]$ is the interior of the sample after applying the trimming parameter. Approximate p-values use the Andrews (1993) large-sample distribution.

Value

An object of class dcce_break with elements:

type

"known" or "unknown".

statistic

The Wald (or sup-Wald) test statistic.

p_value

Approximate p-value.

df

Degrees of freedom of the Wald statistic.

break_date

Known or estimated break date.

candidates

For type = "unknown": a tibble with one row per candidate date (date, wald).

break_dates

Vector of break dates when n_breaks > 1L.

fit_pre

dcce_fit object from the pre-break sub-sample.

fit_post

dcce_fit object from the post-break sub-sample.

call

The original call.

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.

Ditzen, J., Karavias, Y., & Westerlund, J. (2024). Multiple structural breaks in interactive effects panel data models. Journal of Applied Econometrics.

Examples

data(pwt8)
brk <- structural_break_test(
  data       = pwt8,
  unit_index = "country",
  time_index = "year",
  formula    = d_log_rgdpo ~ log_hc + log_ck + log_ngd,
  model      = "mg",
  cross_section_vars = NULL,
  type       = "unknown",
  trim       = 0.20
)
print(brk)

Description

Summary for a dcce_fit object

Usage

## S3 method for class 'dcce_fit'
summary(object, ...)

Arguments

Value

Invisibly returns object.

Description

Tests the null hypothesis that all slope coefficients are identical across cross-sectional units, against the alternative of heterogeneous slopes. Implements the Swamy (1970) chi-square test and the Pesaran & Yamagata (2008) standardised dispersion statistic.

Usage

swamy_test(object)

Arguments

Details

The Swamy statistic is

$\tilde{S} = \sum_{i=1}^N (\hat\beta_i - \hat\beta^*)' \frac{X_i' M_\tau X_i}{\hat\sigma_i^2} (\hat\beta_i - \hat\beta^*),$

where $\hat\beta_i$ is the unit-level OLS estimate, $\hat\beta^*$ is the weighted pooled estimate, and $M_\tau = I - \tau(\tau'\tau)^{-1}\tau'$ projects off the intercept. Under $H_0$ (homogeneous slopes), $\tilde{S}$ is asymptotically $\chi^2_{k(N-1)}$.

Pesaran & Yamagata (2008) propose a standardised version that is asymptotically standard normal:

$\tilde\Delta = \sqrt{N} \, \frac{N^{-1}\tilde{S} - k}{\sqrt{2k}}.$

Value

An object of class dcce_swamy with elements S_stat, delta_stat, df, p_swamy, p_delta, N, k.

References

Swamy, P. A. V. B. (1970). Efficient inference in a random coefficient regression model. Econometrica, 38(2), 311-323.

Pesaran, M. H., & Yamagata, T. (2008). Testing slope homogeneity in large panels. Journal of Econometrics, 142(1), 50-93.

Description

Returns a tibble of MG coefficients with standard errors, test statistics, p-values, and confidence intervals, compatible with the broom package. For long-run estimators (CS-ARDL, CS-DL, PMG) the tibble additionally includes rows for long-run coefficients and the adjustment speed.

Usage

## S3 method for class 'dcce_fit'
tidy(x, include_lr = TRUE, ...)

Arguments

Value

A tibble with columns term, estimate, std.error, statistic, p.value, conf.low, conf.high, type.

Description

Update a dcce_fit object

Usage

## S3 method for class 'dcce_fit'
update(object, formula. = NULL, ..., evaluate = TRUE)

Arguments

Value

An updated dcce_fit object (if evaluate = TRUE) or an unevaluated call.

Description

Extract variance-covariance matrix from a dcce_fit object

Usage

## S3 method for class 'dcce_fit'
vcov(object, ...)

Arguments

Value

A variance-covariance matrix.