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ardlverse is a comprehensive R package for Autoregressive Distributed Lag (ARDL) modeling and cointegration analysis. It provides unified tools for:
# Install from CRAN (once available)
install.packages("ardlverse")
# Or install development version from GitHub
# install.packages("devtools")
devtools::install_github("muhammedalkhalaf/ardlverse")library(ardlverse)
# Generate example data
data <- generate_panel_data(n_groups = 10, n_time = 50)
# Estimate PMG model
pmg_model <- panel_ardl(
gdp ~ inflation + investment,
data = data,
id = "country",
time = "year",
p = 1, q = 1,
estimator = "pmg"
)
summary(pmg_model)
# Hausman test: PMG vs MG
hausman_test(pmg_model)# Generate time series data
ts_data <- generate_ts_data(n = 100)
# Bootstrap bounds test
boot_test <- boot_ardl(
gdp ~ inflation + investment,
data = ts_data,
p = 2, q = 2,
case = 3,
nboot = 2000
)
summary(boot_test)
plot(boot_test)# Generate oil price data
oil <- generate_oil_data(n = 200)
# Estimate QNARDL
qnardl_model <- qnardl(
gasoline ~ oil_price + exchange_rate,
data = oil,
tau = c(0.1, 0.25, 0.5, 0.75, 0.9),
p = 2, q = 2
)
summary(qnardl_model)
plot(qnardl_model, var = "oil_price")
# Test for asymmetry
asymmetry_test(qnardl_model, var = "oil_price")
# Dynamic multipliers
dynamic_multipliers(qnardl_model, var = "oil_price", tau = 0.5)# Estimate Fourier ARDL with automatic frequency selection
f_model <- fourier_ardl(
gdp ~ investment + trade,
data = ts_data,
p = 2, q = 2,
selection = "aic"
)
summary(f_model)
plot(f_model)
fourier_bounds_test(f_model)# Run comprehensive diagnostics
diag <- ardl_diagnostics(f_model)
summary(diag)
plot(diag)# Augmented ARDL with deferred tests
aardl_model <- aardl(
gdp ~ inflation + investment,
data = ts_data,
type = "linear", # or "nardl", "fourier", "fbnardl"
p = 2, q = 2
)
summary(aardl_model)# Decompose into 4 regimes: large/small positive/negative changes
mt_model <- mtnardl(
consumption ~ oil_price,
data = oil,
thresholds = c(-0.05, 0, 0.05),
p = 2, q = 2
)
summary(mt_model)
plot(mt_model, type = "multipliers")# Time-varying bounds test
roll_model <- rardl(
gdp ~ investment + trade,
data = ts_data,
method = "rolling",
window = 60
)
summary(roll_model)
plot(roll_model, type = "all")# Panel data with asymmetric effects
pnardl_model <- pnardl(
y ~ x1 + x2,
data = panel_data,
id = "country",
time = "year",
estimator = "pmg"
)
summary(pnardl_model)| Function | Description |
|---|---|
panel_ardl() |
Panel ARDL with PMG, MG, DFE estimators |
boot_ardl() |
Bootstrap ARDL bounds test |
qnardl() |
Quantile Nonlinear ARDL |
fourier_ardl() |
Fourier ARDL for structural breaks |
| Function | Description |
|---|---|
aardl() |
Augmented ARDL with deferred t and F tests (8 sub-models) |
mtnardl() |
Multiple-Threshold NARDL for complex asymmetries |
rardl() |
Rolling & Recursive ARDL for time-varying relationships |
pnardl() |
Panel Nonlinear ARDL (PMG/MG/DFE with asymmetry) |
| Function | Description |
|---|---|
ardl_diagnostics() |
Comprehensive model diagnostics |
hausman_test() |
Hausman test for PMG vs MG |
asymmetry_test() |
Test for long-run asymmetry |
dynamic_multipliers() |
Cumulative dynamic multipliers |
pss_critical_values() |
PSS (2001) critical value tables |
The PMG estimator allows for heterogeneous short-run dynamics while constraining long-run coefficients to be equal across groups:
\[\Delta y_{it} = \phi_i (y_{i,t-1} - \theta' x_{it}) + \sum_{j=1}^{p-1} \lambda_{ij} \Delta y_{i,t-j} + \sum_{j=0}^{q-1} \delta'_{ij} \Delta x_{i,t-j} + \mu_i + \varepsilon_{it}\]
Combines quantile regression with asymmetric decomposition:
\[x^+_t = \sum_{j=1}^{t} \max(\Delta x_j, 0), \quad x^-_t = \sum_{j=1}^{t} \min(\Delta x_j, 0)\]
Captures smooth structural breaks using Fourier approximation:
\[f_t = \sum_{k=1}^{K} [a_k \sin(2\pi k t/T) + b_k \cos(2\pi k t/T)]\]
Pesaran, M. H., Shin, Y., & Smith, R. P. (1999). Pooled mean group estimation of dynamic heterogeneous panels. Journal of the American Statistical Association, 94(446), 621-634.
Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289-326.
Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In Festschrift in Honor of Peter Schmidt (pp. 281-314). Springer.
Cho, J. S., Kim, T. H., & Shin, Y. (2015). Quantile cointegration in the autoregressive distributed-lag modeling framework. Journal of Econometrics, 188(1), 281-300.
Banerjee, P., Arcabic, V., & Lee, H. (2017). Fourier ADL cointegration test to approximate smooth breaks with new evidence from crude oil market. Economic Modelling, 67, 114-124.
McNown, R., Sam, C. Y., & Goh, S. K. (2018). Bootstrapping the autoregressive distributed lag test for cointegration. Applied Economics, 50(13), 1509-1521.
Sam, C. Y., McNown, R., & Goh, S. K. (2019). An augmented autoregressive distributed lag bounds test for cointegration. Economic Modelling, 80, 130-141.
Muhammad Alkhalaf - ORCID: 0009-0002-2677-9246 - Email: contact@rufyqelngeh.com - Website: rufyqelngeh.com
GPL-3
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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