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Overview

The csdm package implements econometric methods for panel data with cross-sectional dependence (CSD). In many applications, observations across units (e.g., countries, firms, regions) are not independent—macroeconomic shocks, trade relationships, or spillovers create correlation across cross-sectional units. The csdm package provides robust estimators that account for this dependence structure, plus diagnostic tests to detect and characterize it.

Methodology: Four Estimators

Model Specification

The csdm() interface estimates heterogeneous panel data models with optional cross-sectional augmentation and dynamic structure. A baseline heterogeneous panel model is:

$$ y_{it} = \alpha_i + \beta_i' x_{it} + u_{it}, \qquad i = 1, \ldots, N; t = 1, \ldots, T $$

where:

• $y_{it}$ is the outcome variable for unit (i) at time (t)
• $\alpha_i$ is a unit-specific intercept
• $\beta_i$ is a ((k \times 1)) vector of unit-specific slopes
• $x_{it}$ is a ((k \times 1)) vector of explanatory variables
• $u_{it}$ is the error term, which may exhibit cross-sectional dependence

The inner product $\beta_i' x_{it}$ is scalar-valued. Heterogeneous slopes allow each unit to respond differently to the regressors. In many applications, cross-sectional dependence arises because the error term contains unobserved common factors. The estimators implemented in csdm() differ in how they handle this dependence and whether they allow for dynamic adjustment.

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1. Mean Group (MG) Estimator

The Mean Group estimator fits separate regressions for each unit and averages the resulting coefficients:

$$ \hat{\beta}_{MG} = \frac{1}{N}\sum_{i=1}^N \hat{\beta}_i $$

Key idea: Estimation is performed unit by unit, with no pooling of slope coefficients across cross-sectional units.

Interpretation:

• $\hat{\beta}_{MG}$ is the cross-sectional average of the unit-specific estimates
• all slope coefficients are allowed to differ across units

Properties:

• accommodates slope heterogeneity
• requires sufficient time-series information within each unit
• does not explicitly model cross-sectional dependence

Use case: A natural benchmark when the main concern is heterogeneous slopes and no explicit factor structure is imposed.

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2. Common Correlated Effects (CCE) Estimator

The CCE estimator augments each unit regression with cross-sectional averages to proxy unobserved common factors:

$$ y_{it} = \alpha_i + \beta_i' x_{it} + \gamma_i' \bar{z}_t + v_{it} $$

where (\bar{z}_t) collects the cross-sectional averages specified through csdm_csa(), for example

$$ \bar{z}_t = (\bar{y}_t, \bar{x}_t), \qquad \bar{x}_t = \frac{1}{N}\sum_{i=1}^N x_{it}, \qquad \bar{y}_t = \frac{1}{N}\sum_{i=1}^N y_{it}. $$

Key idea: Cross-sectional averages serve as proxies for latent common factors that induce dependence across units.

Interpretation:

• $\beta_i$ measures the unit-specific effect conditional on the included cross-sectional averages.
• $\gamma_i$ captures unit-specific exposure to the common components with (\bar{z}_t) as a proxy.

Properties:

• allows heterogeneous slopes
• augments the regression with cross-sectional averages supplied through csa
• suitable when cross-sectional dependence is driven by latent common shocks

Use case: When dependence across units is believed to reflect common unobserved factors.

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3. Dynamic CCE (DCCE) Estimator

The DCCE estimator extends CCE to dynamic settings by including lagged dependent variables, optional distributed lags of regressors, and lagged cross-sectional averages:

$$ y_{it} = \alpha_i + \sum_{p=1}^{P} \phi_{ip} y_{i,t-p} + \sum_{q=0}^{Q} \beta_{iq}' x_{i,t-q} + \sum_{s=0}^{S} \delta_{is}' \bar{z}_{t-s} + e_{it} $$

where the dynamic structure is controlled through csdm_lr() and the cross-sectional averages and their lags are controlled through csdm_csa().

Key idea: Dynamics are introduced directly in the unit equation, while lagged cross-sectional averages help absorb common factor dependence over time.

Interpretation:

• $\phi_{ip}$ captures unit-specific persistence
• $\beta_{iq}$ captures contemporaneous and lagged effects of regressors
• $\delta_{is}$ captures the effect of contemporaneous and lagged common components

Properties:

• allows heterogeneous dynamic adjustment across units
• combines lagged dependent variables, optional distributed lags, and cross-sectional augmentation
• requires enough time periods to support the chosen lag structure

Use case: When the outcome is persistent over time and cross-sectional dependence remains important.

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4. Cross-Sectionally Augmented ARDL (CS-ARDL)

In the current csdm() implementation, model = "cs_ardl" is obtained by first estimating a cross-sectionally augmented ARDL-style regression in levels, using the same dynamic specification as model = "dcce", and then transforming the estimated unit-specific coefficients into adjustment and long-run parameters.

The underlying unit-level regression is

$$ y_{it} = \alpha_i + \sum_{p=1}^{P} \phi_{ip} y_{i,t-p} + \sum_{q=0}^{Q} \beta_{iq}' x_{i,t-q} + \sum_{s=0}^{S} \omega_{is}' \bar{z}_{t-s} + e_{it} $$

From this dynamic specification, the implied error-correction form is

$$ \Delta y_{it} = \alpha_i + \varphi_i \left( y_{i,t-1} - \theta_i' x_{i,t-1} \right) + \sum_{j=1}^{P-1} \lambda_{ij} \Delta y_{i,t-j} + \sum_{j=0}^{Q-1} \psi_{ij}' \Delta x_{i,t-j} + \sum_{s=0}^{S} \tilde{\omega}_{is}' \bar{z}_{t-s} + e_{it} $$

where the dynamic structure is controlled through csdm_lr() and the cross-sectional averages are supplied through csdm_csa().

Key idea: cs_ardl reports the implied short-run and long-run quantities from a cross-sectionally augmented ARDL fit.

Interpretation:

• $\theta_i$ is the unit-specific long-run relationship
• $\varphi_i$ is the implied speed of adjustment back toward equilibrium
• $\psi_{ij}$ captures short-run effects of changes in regressors
• $\tilde{\omega}_{is}$ captures the role of common cross-sectional components

Properties:

• supports heterogeneous short-run and long-run dynamics
• combines ARDL-style dynamics with cross-sectional augmentation
• recovers adjustment and long-run coefficients from estimated lag polynomials rather than fitting a separate ECM directly

Use case: When the objective is to study long-run relationships together with heterogeneous short-run adjustment in panels affected by common factors.

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Cross-Sectional Averages and Dynamic Structure

Two helper specifications control the main extensions in csdm():

• csdm_csa() defines which variables enter as cross-sectional averages and how many lags of those averages are included
• csdm_lr() defines the dynamic or long-run structure, such as lagged dependent variables and distributed lags

This design keeps the estimation interface consistent across the four estimators while allowing the model specification to vary by application.

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Summary

Estimator	Heterogeneous Slopes	Cross-Sectional Averages	Dynamics	Long-Run Structure
MG	Yes	No	No	No
CCE	Yes	Yes	No	No
DCCE	Yes	Yes	Yes	No
CS-ARDL	Yes	Yes	Yes	Yes

Package installation

To install the csdm package from CRAN, run:

install.packages("csdm")

To install the latest development version from GitHub, run:

install.packages("remotes")
remotes::install_github("Macosso/csdm")

Model Estimation: Four Examples

All models are fitted with csdm(), which automatically detects the input structure and applies the appropriate methodology. The key arguments are id and time to specify the cross-sectional and time-period identifiers, and model to choose the estimator. For CCE and DCCE, additional arguments (csa and lr) specify treatment of cross-sectional averages and dynamics.

Example 1: Mean Group (MG) Estimation

# MG: Separate regression per country, then average coefficients
fit_mg <- csdm(
  log_rgdpo ~ log_hc + log_ck + log_ngd,
  data = df,
  id = "id", 
  time = "year",
  model = "mg"
)

print(fit_mg)
summary(fit_mg)

Example 2: Common Correlated Effects (CCE)

# CCE: Add cross-sectional means to control for common shocks
fit_cce <- csdm(
  log_rgdpo ~ log_hc + log_ck + log_ngd,
  data = df,
  id = "id", 
  time = "year",
  model = "cce",
  csa = csdm_csa(vars = c("log_rgdpo", "log_hc", "log_ck", "log_ngd"))
)

print(fit_cce)
summary(fit_cce)

Example 3: Dynamic CCE (DCCE)

# DCCE: Include dynamics and cross-sectional means
# Use lagged dependent variable to capture dynamic adjustment
fit_dcce <- csdm(
  log_rgdpo ~ log_hc + log_ck + log_ngd,
  data = df,
  id = "id", 
  time = "year",
  model = "dcce",
  csa = csdm_csa(
    vars = c("log_rgdpo", "log_hc", "log_ck", "log_ngd"), 
    lags = 3
  ),
  lr = csdm_lr(type = "ardl", ylags = 1, xdlags = 0)
)

print(fit_dcce)
summary(fit_dcce)

Example 4: Cross-Sectionally Augmented ARDL (CS-ARDL)

# CS-ARDL: Separate short-run and long-run dynamics
# Includes lagged dependent and lagged regressors
fit_csardl <- csdm(
  log_rgdpo ~ log_hc + log_ck + log_ngd,
  data = df,
  id = "id", 
  time = "year",
  model = "cs_ardl",
  csa = csdm_csa(
    vars = c("log_rgdpo", "log_hc", "log_ck", "log_ngd"), 
    lags = 3
  ),
  lr = csdm_lr(type = "ardl", ylags = 1, xdlags = 1)
)

print(fit_csardl)
summary(fit_csardl)

Cross-Sectional Dependence Testing

After fitting a model, we can test whether residuals exhibit cross-sectional dependence using the Pesaran CD test and related variants. CSD tests detect whether residuals $u_{it}$ are correlated across units—a key assumption violation that can bias standard errors.

Four CD Test Types

All CD tests have null hypothesis: residuals are cross-sectionally independent.

1. Pesaran CD Test

The Pesaran CD statistic is:

$$CD = \sqrt{\frac{2}{N(N-1)}} \sum_{i=1}^{N-1} \sum_{j=i+1}^{N} \hat{\rho}_{ij} \sqrt{T}$$

where $\hat{\rho}_{ij}$ is the cross-sectional correlation between residuals of units $i$ and $j$. The test statistic is approximately standard normal under the null.

Interpretation: Large $|CD|$ rejects independence; both positive and negative correlations are flagged. This is the most general CD test and works even when $N$ is fixed and $T \to \infty$.

2. Pesaran CD Weighted (CDw)

The CDw statistic uses unit-level random sign flips to form a wild-type version of the CD test:

$$CD_w = \sqrt{\frac{2}{N(N-1)}} \sum_{i=1}^{N-1} \sum_{j=i+1}^{N} w_i w_j , \hat{\rho}_{ij} \sqrt{T},$$

where $(w_1,\ldots,w_N)$ are independent random weights with $w_i \in {-1,1}$ applied at the unit level. This statistic can be used in randomization-based or simulation-based inference procedures.

3. Pesaran CD Weighted Plus (CDw+)

CDw+ applies an alternative unit-level random sign-flip scheme:

$$CD_w^+ = \sqrt{\frac{2}{N(N-1)}} \sum_{i=1}^{N-1} \sum_{j=i+1}^{N} w_i^{(+)} w_j^{(+)} , \hat{\rho}_{ij} \sqrt{T},$$

where $(w_1^{(+)},\ldots,w_N^{(+)})$ are again independent random weights with $w_i^{(+)} \in {-1,1}$ (typically a separate draw from that used for $CD_w$).

4. Pesaran CD*, Fan-Liao-Yao (FLY)

The CD* statistic is a semiparametric refinement for large $N$ and $T$:

$$CD^* = \frac{1}{\sqrt{N(N-1)}} \sum_{i=1}^{N-1} \sum_{j=i+1}^{N} (\hat{\rho}_{ij}^2 - \tau_T)$$

where $\tau_T$ is a variance adjustment. FLY-type tests are designed for large panel dimensions and provide robustness against certain forms of weak cross-sectional dependence.

Running CD Tests with Seed Selection

The cd_test() function accepts the fitted model and computes all test variants. Tests use a random seed to initialize pseudo-random computations (for cdw and cdw+); setting a seed ensures reproducibility of numerical results across runs.

# Test MG residuals for CSD
cd_mg <- cd_test(fit_mg, type = "CD")
print(cd_mg)

# Test CCE residuals for CSD
set.seed(1234)
cd_cce <- cd_test(fit_cce, type = "all")
print(cd_cce)

Interpreting Results:

• CD statistic p-value < 0.05: Reject null of CSD independence; residuals are correlated across units.
• CDw, CDw+, CD variants*: Provide robustness checks; if all reject the null, CSD is strongly evidenced.
• Magnitude: Large $|CD|$ statistics (e.g., $|CD| &gt; 3$) indicate substantial and economically meaningful dependence.

In practice, models that do not account for cross-sectional dependence (like MG without augmentation) typically show significant CD test rejections, justifying the use of CSD-robust methods like CCE and DCCE.

References

Chudik, A., & Pesaran, M. H. (2013). Large panel data models with cross-sectional dependence: A survey [Globalization Institute Working Papers]. Federal Reserve Bank of Dallas, (153).

Chudik, A., & 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.

Ditzen, J. (2018). Estimating dynamic common-correlated effects in STATA. The STATA Journal, 18(3), 585–617. https://doi.org/10.1177/1536867X1801800306

Fan, J., Liao, Y., & Yao, J. (2015). Power enhancement in high-dimensional cross-section tests. Econometrica, 83(4), 1497–1541.

Juodis, A., & Reese, S. (2021). The incidental parameters problem in testing for remaining cross-sectional correlation. Journal of Business and Economic Statistics, 40(3), 1191–1203.

Pesaran, M. H. (2006). Estimation and inference in large heterogeneous panels with multifactor error structure. Econometrica, 74(4), 967–1012.

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

Pesaran, M. H. (2015). Testing weak cross-sectional dependence in large panels. Econometric Reviews, 34(6-10), 1089–1117.

Pesaran, M. H. (2021). General diagnostic tests for cross-sectional dependence in panels. Empirical Economics, 60(1), 13–50.

Pesaran, M. H., & Smith, R. (1995). Estimating long-run relationships from dynamic heterogeneous panels. Journal of Econometrics, 68(1), 79–113.

Pesaran, M. H., & Xie, Y. (2021). A bias-corrected CD test for error cross-sectional dependence in panel models. Econometric Reviews, 41(6), 649–677.