asycaus 1.0.0

Asymmetric Granger-causality suite for Python (Hatemi-J, Bahmani-Oskooee, Fang et al., Nazlioglu et al.)

asycaus — Asymmetric Granger-Causality Suite for Python

Repository: https://github.com/merwanroudane/asycaus

A complete Python implementation of asymmetric Granger-causality tests for bivariate time series. The package is a faithful mirror of the companion Stata asycaus library by the same author, with publication-quality tables (powered by rich) and plots (matplotlib).

Author: Dr Merwan Roudane  ·  [e-mail]  ·  GitHub

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Methods implemented

Function	Test	Reference
asycaus.static	Static asymmetric causality with leverage bootstrap	Hatemi-J (2012); Hacker & Hatemi-J (2006, 2012)
asycaus.dynamic	Rolling / recursive time-varying asymmetric causality	Hatemi-J (2021)
asycaus.fourier	Fourier-augmented asymmetric Toda-Yamamoto	Nazlioglu, Gormus & Soytas (2016); Pata (2020)
asycaus.spectral	Frequency-domain BC test on Pos / Neg components	Bahmani-Oskooee, Chang & Ranjbar (2016); Breitung & Candelon (2006)
asycaus.quantile	Quantile asymmetric causality (+ optional Fourier detrending)	Fang, Wang, Shieh & Chung (2026)
asycaus.efficient	SUR-based joint Pos / Neg / Joint / Pos=Neg test	Hatemi-J (2024)
asycaus.all_tests	Full battery with unified summary table	this package
asycaus.pos_neg_components	Utility: build cumulative shock components	Granger & Yoon (2002)

The default lag-selection criterion across the library is the Hatemi-J information criterion (HJC; Hatemi-J 2003), with AIC, AICC, SBC, HQC also available.

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Installation

# Once published to PyPI:
pip install asycaus

# Directly from GitHub:
pip install git+https://github.com/merwanroudane/asycaus.git

# Or from a local clone:
git clone https://github.com/merwanroudane/asycaus.git
pip install -e asycaus

Dependencies (installed automatically): numpy, scipy, pandas, matplotlib, rich.

Python ≥ 3.9 is required.

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Quick start

import numpy as np
import asycaus

# An asymmetric DGP: positive shocks in x cause y, negative do not.
rng = np.random.default_rng(30540)
ex  = rng.standard_normal(300)
ey  = rng.standard_normal(300)
x   = np.cumsum(ex)
y   = np.zeros(300)
for t in range(1, 300):
    y[t] = 0.5 * y[t - 1] + 0.7 * max(ex[t - 1], 0) + ey[t]

# --- 1. Static Hatemi-J (2012) ----------------------------------------
asycaus.static(y, x, shock="both", boot=300)

# --- 2. Dynamic Hatemi-J (2021) rolling window ------------------------
asycaus.dynamic(y, x, shock="pos", mode="rolling", boot=150)

# --- 3. Fourier (Nazlioglu et al. 2016) -------------------------------
asycaus.fourier(y, x, kmax=3, form="single")

# --- 4. Spectral (Bahmani-Oskooee et al. 2016) ------------------------
asycaus.spectral(y, x, nfreq=50)

# --- 5. Quantile (Fang et al. 2026), with Fourier detrending ----------
asycaus.quantile(y, x, quantiles=(0.1, 0.25, 0.5, 0.75, 0.9),
                 fourier=True, kmax=2)

# --- 6. Efficient SUR (Hatemi-J 2024) ---------------------------------
asycaus.efficient(y, x, max_lag=4)

# --- Everything in one call -------------------------------------------
asycaus.all_tests(y, x, max_lag=4, boot=300, skip_dynamic=True)

Every test prints a coloured, boxed table (via rich) and renders a publication-quality plot (set plot=False to suppress; show=False to suppress the table; both return a Result dataclass).

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Detailed function reference

asycaus.static(y, x, *, shock='both', max_lag=8, ic='hjc', intorder=1, boot=1000, seed=12345, lnform=False, show=True, plot=True)

Hatemi-J (2012) static asymmetric causality with leverage-adjusted bootstrap.

• y, x: 1-D arrays of equal length (length T). H₀: x does not Granger-cause y.
• shock: 'pos' | 'neg' | 'both'. The cumulative components to test.
• max_lag: integer, max VAR lag for the IC search.
• ic: 'aic' | 'aicc' | 'sbc' | 'hqc' | 'hjc' (default).
• intorder: integer ≥ 0, Toda-Yamamoto augmentation lags (max integration order).
• boot: bootstrap replications.
• seed: RNG seed (None for non-reproducible).
• lnform: take np.log() of inputs before the decomposition.
• show / plot: print the table / render the bar plot.

Returns StaticResult:

• .table — pandas.DataFrame indexed by shock, columns Wald, lag, dof, asy_p, cv10, cv5, cv1, decision_5pct.
• .depvar, .causvar, .ic, .boot, .sample_size, .intorder.

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asycaus.dynamic(y, x, *, shock='pos', mode='rolling', window=None, max_lag=4, ic='hjc', intorder=1, boot=200, seed=12345, lnform=False, show=True, plot=True, progress=True)

Hatemi-J (2021) time-varying asymmetric causality.

• mode: 'rolling' (fixed window, slides by 1) or 'recursive' (anchored at observation 1, expanding).
• window: window size S; defaults to the Phillips-Shi-Yu (2015) lower bound S = ceil(T (0.01 + 1.8/√T)).
• progress: print subsample k/N every 10 windows.

Returns DynamicResult:

• .table — DataFrame columns sub_start, sub_end, lag, Wald, cv10, cv5, cv1, ratio_5pct.
• .mode, .window, .smin, .nsub, .shock, .ic, .boot, .intorder.

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asycaus.fourier(y, x, *, shock='both', kmax=5, form='single', max_lag=8, ic='hjc', intorder=1, lnform=False, show=True, plot=True)

Fourier-augmented asymmetric Toda-Yamamoto (Nazlioglu et al. 2016; Pata 2020).

• kmax: maximum Fourier frequency. The function searches k = 1..kmax and selects the most informative k*.
• form: 'single' (sin/cos at frequency k) or 'cumulative' (basis k = 1..k_max).

Returns FourierResult with .table indexed by shock, columns Wald, lag, k_opt, asy_p, sample, decision_5pct.

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asycaus.spectral(y, x, *, shock='both', nfreq=50, max_lag=8, ic='hjc', lnform=False, show=True, plot=True)

Asymmetric frequency-domain causality (Bahmani-Oskooee et al. 2016 — BC 2006 applied to Pos/Neg components).

• nfreq: number of grid points in ω ∈ (0, π].

Returns SpectralResult with:

• .table — full grid (shock_id, shock, omega, Wald, cv10, cv5, cv1, lag)
• .summary — per-shock count of rejections at 1%, 5%, 10%

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asycaus.quantile(y, x, *, shock='both', quantiles=(0.1,0.25,0.5,0.75,0.9), max_lag=4, ic='hjc', intorder=1, fourier=False, kmax=3, lnform=False, show=True, plot=True)

Quantile asymmetric causality (Fang, Wang, Shieh & Chung 2026).

• quantiles: iterable of quantile probabilities in (0, 1).
• fourier=True: project out a cumulative Fourier basis (sin/cos with k = 1..kmax) before estimating the quantile causality.

Returns QuantileResult with .table columns shock_id, shock, tau, Wald, lag, asy_p, decision_5pct.

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asycaus.efficient(y, x, *, max_lag=8, ic='hjc', intorder=1, lnform=False, show=True, plot=True)

Hatemi-J (2024) efficient asymmetric causality via SUR.

Reports four hypotheses jointly:

1. No causality through Pos shocks
2. No causality through Neg shocks
3. Joint no causality
4. Pos = Neg causal coefficients — the formal asymmetry test

Returns EfficientResult with:

• .table indexed by hypothesis: Wald, df, asy_p, decision_5pct
• .raw dict with W_pos, p_pos, W_neg, p_neg, W_joint, p_joint, W_diff, p_diff, dof

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asycaus.all_tests(y, x, *, max_lag=4, ic='hjc', intorder=1, boot=500, seed=12345, kmax=5, nfreq=50, quantiles=(0.1,0.25,0.5,0.75,0.9), window=None, lnform=False, skip_dynamic=False, skip_spectral=False, skip_quantile=False, show=True, plot=False)

Run the full battery and print a unified summary.

Returns AllResult containing every individual result plus:

• .summary — DataFrame with rows of the form (Test, Shock, Statistic, p-value, Decision).
• .plot() — assembles a 6-panel dashboard.

skip_dynamic, skip_spectral, skip_quantile let you turn off the slowest tests.

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asycaus.pos_neg_components(Y, positive=True)

Utility: cumulative sum of positive (or negative) first differences of Y (T × K). Returns a (T-1) × K matrix.

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Output styling

Tables use rich for colour output if available, falling back to a clean ASCII boxed format otherwise. Plots use matplotlib with a consistent publication-quality theme (navy / orange / green / red).

Each Result dataclass exposes .print() and .plot() so you can re-render without re-running the test:

r = asycaus.static(y, x, show=False, plot=False)   # silent
r.print()                                          # boxed table
fig = r.plot(save="static.png")                    # save the figure

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References

• Bahmani-Oskooee, M., Chang, T., & Ranjbar, O. (2016). Asymmetric causality using frequency-domain and time-frequency-domain (wavelet) approaches. Economic Modelling, 56, 66–78.
• Breitung, J., & Candelon, B. (2006). Testing for short- and long-run causality: a frequency-domain approach. Journal of Econometrics, 132, 363–378.
• Enders, W., & Lee, J. (2012). The flexible Fourier form and Dickey-Fuller type unit root tests. Economics Letters, 117(1), 196–199.
• Fang, H., Wang, C.-H., Shieh, J. C. P., & Chung, C.-P. (2026). The asymmetric Granger causality between banking-sector and stock-market development and economic growth in quantiles considering Fourier. Applied Economics, 58(20), 3822–3838.
• Granger, C. W. J., & Yoon, G. (2002). Hidden cointegration. UCSD Discussion Paper 2002-02.
• Hacker, R. S., & Hatemi-J, A. (2006). Tests for causality between integrated variables using asymptotic and bootstrap distributions: theory and application. Applied Economics, 38(13), 1489–1500.
• Hacker, R. S., & Hatemi-J, A. (2012). A bootstrap test for causality with endogenous lag length choice: theory and application in finance. Journal of Economic Studies, 39(2), 144–160.
• Hatemi-J, A. (2003). A new method to choose optimal lag order in stable and unstable VAR models. Applied Economics Letters, 10(3), 135–137.
• Hatemi-J, A. (2012). Asymmetric causality tests with an application. Empirical Economics, 43, 447–456.
• Hatemi-J, A. (2021). Dynamic Asymmetric Causality Tests with an Application. arXiv 2106.07612.
• Hatemi-J, A. (2024). Efficient Asymmetric Causality Tests. arXiv 2408.03137.
• Koenker, R. (2005). Quantile Regression. Cambridge University Press.
• Nazlioglu, S., Gormus, N. A., & Soytas, U. (2016). Oil prices and real estate investment trusts (REITs): gradual-shift causality and volatility transmission analysis. Energy Economics, 60, 168–175.
• Pata, U. K. (2020). How is COVID-19 affecting environmental pollution in US cities? Evidence from asymmetric Fourier causality test. Air Quality, Atmosphere & Health, 13, 1149–1155.
• Phillips, P. C. B., Shi, S., & Yu, J. (2015). Testing for multiple bubbles: limit theory of real-time detectors. International Economic Review, 56(4), 1043–1078.
• Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66, 225–250.

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Citation

Roudane, M. (2026). asycaus: Asymmetric Granger-causality suite for Python.
Version 1.0.0.  https://github.com/merwanroudane/asycaus

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License

MIT — see LICENSE.

Companion package (Stata)

The Stata twin of this library, also by the same author, is available on SSC:

ssc install asycaus
help asycaus