tvrcoint 1.0.0

Time-Varying Cointegration Tests - Bootstrap LR tests for time-varying cointegration based on Bierens & Martins (2010) and Martins (2015)

TVRCoint - Time-Varying Cointegration Tests

A comprehensive Python library for testing time-invariant cointegration against time-varying cointegration alternatives using bootstrap methods.

Overview

TVRCoint implements the methodology from:

1. Bierens, H.J. & Martins, L.F. (2010) - "Time-Varying Cointegration" - Econometric Theory, 26(5), 1453-1490
2. Martins, L.F. (2015) - "Bootstrap Tests for Time Varying Cointegration" - Econometric Reviews

Installation

pip install tvrcoint

Or install from source:

git clone https://github.com/merwanroudane/tvrcoint.git
cd tvrcoint
pip install -e .

Dependencies

numpy>=1.20.0
scipy>=1.7.0
pandas>=1.3.0

---

Complete API Reference with Examples

1. Chebyshev Time Polynomials

from tvrcoint.chebyshev import (
    chebyshev_poly,
    chebyshev_polynomials,
    chebyshev_matrix,
    verify_orthonormality,
    fourier_coefficients,
    reconstruct_beta
)
import numpy as np

# ============================================
# 1.1 chebyshev_poly(i, t, T)
# Compute single Chebyshev polynomial value
# ============================================
T = 100
# P_0(t) = 1 for all t
p0 = chebyshev_poly(0, 50, T)  # Returns 1.0

# P_1(t) = sqrt(2) * cos(pi*(t-0.5)/T)
p1 = chebyshev_poly(1, 50, T)  # Returns near 0 for middle of series

print(f"P_0,100(50) = {p0}")  # 1.0
print(f"P_1,100(50) = {p1}")  # ~0

# ============================================
# 1.2 chebyshev_polynomials(m, T)
# Get full polynomial matrix
# ============================================
m = 5  # Order of polynomials (0 to m)
P = chebyshev_polynomials(m, T)
# Shape: (m+1, T) = (6, 100)

print(f"Shape: {P.shape}")  # (6, 100)
print(f"First row (P_0): {P[0, :5]}")  # All 1s

# ============================================
# 1.3 verify_orthonormality(m, T)
# Check if polynomials are orthonormal
# ============================================
is_orthonormal = verify_orthonormality(m=5, T=100)
print(f"Orthonormal: {is_orthonormal}")  # True

# (1/T) * sum(P_i * P_j) = 1 if i==j, 0 otherwise

# ============================================
# 1.4 chebyshev_matrix(Y, m)
# Construct extended lagged matrix Y^{(m)}_{t-1}
# ============================================
Y = np.random.randn(100, 3)  # T=100, k=3 variables
m = 2

Y_m = chebyshev_matrix(Y, m)
# Shape: (T-1, (m+1)*k) = (99, 9)

print(f"Input shape: {Y.shape}")   # (100, 3)
print(f"Output shape: {Y_m.shape}")  # (99, 9)

# ============================================
# 1.5 fourier_coefficients(beta_t, T, m)
# Compute Chebyshev expansion coefficients
# ============================================
beta_t = np.random.randn(100, 3)  # Time-varying beta
xi = fourier_coefficients(beta_t, T=100, m=2)
# Shape: (m+1, k) = (3, 3)

print(f"Coefficients shape: {xi.shape}")  # (3, 3)

# ============================================
# 1.6 reconstruct_beta(xi, T)
# Reconstruct beta_t from coefficients
# ============================================
beta_reconstructed = reconstruct_beta(xi, T=100)
print(f"Reconstructed shape: {beta_reconstructed.shape}")  # (100, 3)

---

2. Matrix Computations

from tvrcoint.matrices import (
    compute_s_matrices,
    solve_eigenvalue_problem,
    compute_lr_statistic
)
import numpy as np

np.random.seed(42)

# ============================================
# 2.1 compute_s_matrices(delta_Y, Y_m, X, include_intercept)
# Compute S00, S11, S01, S10 matrices
# ============================================
T_eff, k, m = 98, 3, 2
delta_Y = np.random.randn(T_eff, k)  # Differenced data
Y_m = np.random.randn(T_eff, (m+1)*k)  # Extended lagged data

S = compute_s_matrices(delta_Y, Y_m, include_intercept=True)

print(f"S00 shape: {S['S00'].shape}")  # (3, 3)
print(f"S11 shape: {S['S11'].shape}")  # (9, 9)
print(f"S01 shape: {S['S01'].shape}")  # (3, 9)
print(f"S10 shape: {S['S10'].shape}")  # (9, 3)

# Verify properties
print(f"S00 symmetric: {np.allclose(S['S00'], S['S00'].T)}")  # True
print(f"S10 = S01.T: {np.allclose(S['S10'], S['S01'].T)}")    # True

# ============================================
# 2.2 solve_eigenvalue_problem(S11, S10, S00, S01, r)
# Solve generalized eigenvalue problem
# ============================================
eigenvalues, eigenvectors = solve_eigenvalue_problem(
    S['S11'], S['S10'], S['S00'], S['S01'], r=3
)

print(f"Eigenvalues: {eigenvalues}")  # Ordered, largest first
print(f"All in [0,1]: {np.all((eigenvalues >= 0) & (eigenvalues <= 1))}")  # True

# ============================================
# 2.3 compute_lr_statistic(lambda_0, lambda_m, T, r)
# Compute LR test statistic
# ============================================
lambda_0 = np.array([0.8, 0.5, 0.3])  # Eigenvalues under H0
lambda_m = np.array([0.85, 0.55, 0.35])  # Eigenvalues under Ha

lr_stat = compute_lr_statistic(lambda_0, lambda_m, T=100, r=3)
print(f"LR statistic: {lr_stat:.4f}")  # Non-negative value

---

3. Data Simulation

from tvrcoint.simulation import (
    generate_cointegrated_data,
    dgp_bm,
    dgp_js,
    dgp_ey,
    simulate_critical_values
)

# ============================================
# 3.1 generate_cointegrated_data(T, k, r, seed)
# Generate cointegrated data under H0
# ============================================
Y = generate_cointegrated_data(T=200, k=3, r=1, seed=42)
print(f"Data shape: {Y.shape}")  # (200, 3)

# Reproducible with same seed
Y2 = generate_cointegrated_data(T=200, k=3, r=1, seed=42)
print(f"Reproducible: {np.allclose(Y, Y2)}")  # True

# ============================================
# 3.2 dgp_bm(T, k, p, seed)
# Bierens-Martins (2010) simulation design
# ============================================
Y_bm = dgp_bm(T=200, k=2, p=2, seed=42)
# Bivariate system with r=1, specific alpha/beta
print(f"BM DGP shape: {Y_bm.shape}")  # (200, 2)

# ============================================
# 3.3 dgp_js(T, k, seed)
# Johansen-Swensen (2002) simulation design
# ============================================
Y_js = dgp_js(T=200, k=3, seed=42)
# Trivariate system with r=1
print(f"JS DGP shape: {Y_js.shape}")  # (200, 3)

# ============================================
# 3.4 dgp_ey(T, k, seed)
# Engle-Yoo (1987) simulation design
# ============================================
Y_ey = dgp_ey(T=200, k=3, seed=42)
# Trivariate system with r=2
print(f"EY DGP shape: {Y_ey.shape}")  # (200, 3)

# ============================================
# 3.5 simulate_critical_values(m, k, r, T, n_simulations)
# Monte Carlo simulation for critical values
# ============================================
# Note: This is computationally intensive
results = simulate_critical_values(
    m=2, k=2, r=1, T=100,
    n_simulations=1000,  # Use 10000 for accurate results
    seed=42,
    verbose=True
)

print(f"Simulated 5% CV: {results.critical_values[0.05]:.4f}")
print(f"Empirical size at asymptotic 5%: {results.empirical_size[0.05]:.4f}")

---

4. TV-VECM Estimation

from tvrcoint import TVVECM
from tvrcoint.tvvecm import fit_vecm, fit_tvvecm
from tvrcoint.simulation import generate_cointegrated_data
import numpy as np

np.random.seed(42)
Y = generate_cointegrated_data(T=200, k=3, r=1)

# ============================================
# 4.1 TVVECM class
# Time-Varying Vector Error Correction Model
# ============================================
model = TVVECM()

# Standard VECM (m=0)
results = model.fit(Y, r=1, m=0, p=2, include_drift=True)

print(f"Sample size T: {results.T}")
print(f"Variables k: {results.k}")
print(f"Cointegration rank r: {results.r}")
print(f"Alpha (adjustment) shape: {results.alpha.shape}")  # (3, 1)
print(f"Beta (cointegrating) shape: {results.beta.shape}")  # (3, 1)
print(f"Residuals shape: {results.residuals.shape}")  # (T_eff, 3)
print(f"Eigenvalues: {results.eigenvalues[:3]}")
print(f"Log-likelihood: {results.log_likelihood:.2f}")

# TV-VECM (m > 0)
results_tv = model.fit(Y, r=1, m=5, p=2, include_drift=True)

print(f"\nTV-VECM with m=5:")
print(f"Xi shape: {results_tv.xi.shape}")  # ((m+1)*k, r) = (18, 1)
print(f"Log-likelihood: {results_tv.log_likelihood:.2f}")

# ============================================
# 4.2 Reconstruct time-varying beta
# ============================================
beta_t = results_tv.beta_t(T=200)  # Shape (T, k, r)
print(f"Time-varying beta shape: {beta_t.shape}")  # (200, 3, 1)

# Plot time evolution of first cointegrating vector
# import matplotlib.pyplot as plt
# plt.plot(beta_t[:, :, 0])

# ============================================
# 4.3 Convenience functions
# ============================================
# Quick standard VECM
results_quick = fit_vecm(Y, r=1, p=2)

# Quick TV-VECM
results_tv_quick = fit_tvvecm(Y, r=1, m=3, p=2)

---

5. LR Test for Time-Varying Cointegration

from tvrcoint import TVCTest
from tvrcoint.lr_test import tvc_test, critical_values_chi2, TVCTestResults
from tvrcoint.simulation import generate_cointegrated_data
import numpy as np

np.random.seed(42)
Y = generate_cointegrated_data(T=200, k=3, r=1)

# ============================================
# 5.1 TVCTest class
# LR test: H0 (time-invariant) vs H1 (time-varying)
# ============================================
test = TVCTest()
results = test.test(Y, r=1, m=5, p=1, include_drift=True)

print("LR Test Results:")
print(f"  LR statistic: {results.statistic:.4f}")
print(f"  Degrees of freedom: {results.df}")  # m*k*r = 5*3*1 = 15
print(f"  Asymptotic p-value: {results.pvalue:.4f}")

# Access eigenvalues
print(f"  Eigenvalues under H0: {results.eigenvalues_0}")
print(f"  Eigenvalues under H1: {results.eigenvalues_m}")

# ============================================
# 5.2 results.summary()
# Get formatted summary
# ============================================
print(results.summary())

# ============================================
# 5.3 test_multiple_m()
# Test multiple Chebyshev orders
# ============================================
multi_results = test.test_multiple_m(
    Y, r=1, m_values=[1, 2, 5, 10], p=1
)

print("\nResults for different m values:")
for m, res in multi_results.items():
    print(f"  m={m:2d}: LR={res.statistic:8.2f}, df={res.df:3d}, p={res.pvalue:.4f}")

# ============================================
# 5.4 tvc_test() convenience function
# ============================================
results = tvc_test(Y, r=1, m=5, p=1)
print(f"\nQuick test: LR={results.statistic:.2f}, p={results.pvalue:.4f}")

# ============================================
# 5.5 critical_values_chi2()
# Get asymptotic critical values
# ============================================
cv = critical_values_chi2(df=15, levels=[0.10, 0.05, 0.01])
print(f"\nAsymptotic CV (df=15): 10%={cv[0.10]:.2f}, 5%={cv[0.05]:.2f}, 1%={cv[0.01]:.2f}")

---

6. Bootstrap Tests (Recommended)

from tvrcoint import BootstrapTVCTest
from tvrcoint.bootstrap import bootstrap_tvc_test
from tvrcoint.simulation import generate_cointegrated_data
import numpy as np

np.random.seed(42)
Y = generate_cointegrated_data(T=150, k=2, r=1)

# ============================================
# 6.1 BootstrapTVCTest class
# Wild Bootstrap (recommended)
# ============================================
test = BootstrapTVCTest()

results = test.test(
    Y,
    r=1,                 # Cointegration rank
    m=5,                 # Chebyshev polynomial order
    p=1,                 # VAR lag order
    method='wild',       # 'wild' or 'iid'
    restricted=False,    # Use unrestricted residuals (recommended)
    B=399,               # Number of bootstrap replications
    include_drift=True,
    seed=42              # For reproducibility
)

print("Wild Bootstrap Results:")
print(f"  LR statistic: {results.statistic:.4f}")
print(f"  Bootstrap p-value: {results.pvalue_bootstrap:.4f}")
print(f"  Asymptotic p-value: {results.pvalue_asymptotic:.4f}")
print(f"  Method: {results.method}")
print(f"  Replications (B): {results.B}")

# ============================================
# 6.2 Bootstrap critical values
# ============================================
print(f"\nBootstrap Critical Values:")
print(f"  10%: {results.critical_values[0.10]:.4f}")
print(f"  5%:  {results.critical_values[0.05]:.4f}")
print(f"  1%:  {results.critical_values[0.01]:.4f}")

# ============================================
# 6.3 Access bootstrap distribution
# ============================================
bs_stats = results.bootstrap_statistics
print(f"\nBootstrap distribution:")
print(f"  Mean: {np.mean(bs_stats):.4f}")
print(f"  Std:  {np.std(bs_stats):.4f}")
print(f"  Min:  {np.min(bs_stats):.4f}")
print(f"  Max:  {np.max(bs_stats):.4f}")

# ============================================
# 6.4 results.summary()
# ============================================
print(results.summary())

# ============================================
# 6.5 i.i.d. Bootstrap
# For i.i.d. Gaussian errors
# ============================================
results_iid = test.test(
    Y, r=1, m=5, p=1,
    method='iid',
    restricted=False,
    B=399,
    seed=42
)

print(f"\ni.i.d. Bootstrap p-value: {results_iid.pvalue_bootstrap:.4f}")

# ============================================
# 6.6 Restricted vs Unrestricted residuals
# ============================================
# Unrestricted: residuals from TV-VECM (m>0) - recommended
results_ur = test.test(Y, r=1, m=5, p=1, method='wild', restricted=False, B=399)

# Restricted: residuals from standard VECM (m=0)
results_r = test.test(Y, r=1, m=5, p=1, method='wild', restricted=True, B=399)

print(f"\nUnrestricted p-value: {results_ur.pvalue_bootstrap:.4f}")
print(f"Restricted p-value: {results_r.pvalue_bootstrap:.4f}")

# ============================================
# 6.7 bootstrap_tvc_test() convenience function
# ============================================
results = bootstrap_tvc_test(
    Y, r=1, m=5, p=1,
    method='wild', B=399, seed=42
)
print(f"\nQuick bootstrap: p={results.pvalue_bootstrap:.4f}")

---

7. Output Formatting

from tvrcoint.output import (
    format_results,
    results_to_latex,
    results_to_markdown,
    results_to_html,
    format_pvalue,
    create_comparison_table
)
from tvrcoint import BootstrapTVCTest
from tvrcoint.simulation import generate_cointegrated_data
import numpy as np

np.random.seed(42)
Y = generate_cointegrated_data(T=150, k=2, r=1)

test = BootstrapTVCTest()
results = test.test(Y, r=1, m=5, p=1, method='wild', B=199, seed=42)

# ============================================
# 7.1 format_pvalue()
# Format p-values with significance stars
# ============================================
print("P-value formatting:")
print(f"  0.001 -> {format_pvalue(0.001)}")   # '0.0010***'
print(f"  0.03  -> {format_pvalue(0.03)}")    # '0.0300**'
print(f"  0.08  -> {format_pvalue(0.08)}")    # '0.0800*'
print(f"  0.15  -> {format_pvalue(0.15)}")    # '0.1500'

# ============================================
# 7.2 results_to_latex()
# Export to LaTeX table
# ============================================
latex_output = results_to_latex(results)
print("\nLaTeX output:")
print(latex_output)

# Save to file
with open('results_table.tex', 'w') as f:
    f.write(latex_output)

# ============================================
# 7.3 results_to_markdown()
# Export to Markdown table
# ============================================
md_output = results_to_markdown(results)
print("\nMarkdown output:")
print(md_output)

# ============================================
# 7.4 results_to_html()
# Export to HTML with styling
# ============================================
html_output = results_to_html(results)

# Save to file
with open('results_table.html', 'w') as f:
    f.write(html_output)

print("\nHTML output saved to results_table.html")

# ============================================
# 7.5 format_results()
# Generic formatter
# ============================================
text_output = format_results(results, 'text')
latex_output = format_results(results, 'latex')
md_output = format_results(results, 'markdown')
html_output = format_results(results, 'html')

# ============================================
# 7.6 create_comparison_table()
# Compare multiple test results
# ============================================
results_list = []
for m in [1, 2, 5]:
    r = test.test(Y, r=1, m=m, p=1, method='wild', B=199, seed=m)
    results_list.append(r)

comparison_latex = create_comparison_table(
    results_list,
    labels=['m=1', 'm=2', 'm=5']
)
print("\nComparison table:")
print(comparison_latex)

---

8. Critical Values

from tvrcoint.critical_values import (
    get_critical_value,
    get_asymptotic_critical_value,
    print_critical_value_table,
    compare_with_asymptotic,
    CRITICAL_VALUES
)

# ============================================
# 8.1 get_asymptotic_critical_value()
# Chi-square critical values
# ============================================
cv = get_asymptotic_critical_value(m=5, k=2, r=1, level=0.05)
# df = m*k*r = 10
print(f"Asymptotic 5% CV (df=10): {cv:.4f}")

cv_01 = get_asymptotic_critical_value(m=5, k=3, r=1, level=0.01)
# df = 15
print(f"Asymptotic 1% CV (df=15): {cv_01:.4f}")

# ============================================
# 8.2 get_critical_value()
# Get pre-computed or asymptotic CV
# ============================================
# Pre-computed (if available)
cv = get_critical_value(m=2, k=2, r=1, T=100, level=0.05)
print(f"CV for m=2, k=2, r=1, T=100: {cv:.4f}")

# Falls back to asymptotic if not pre-computed
cv_fallback = get_critical_value(m=15, k=5, r=2, T=500, level=0.05)
print(f"CV (fallback): {cv_fallback:.4f}")

# ============================================
# 8.3 print_critical_value_table()
# Display pre-computed tables
# ============================================
print_critical_value_table(k=2, r=1)

# ============================================
# 8.4 compare_with_asymptotic()
# Compare simulated vs asymptotic CVs
# ============================================
compare_with_asymptotic(k=2, r=1)

# ============================================
# 8.5 Access raw data
# ============================================
# CRITICAL_VALUES[k][r][T][m] = {'cv_10': ..., 'cv_05': ..., 'cv_01': ...}
if 2 in CRITICAL_VALUES and 1 in CRITICAL_VALUES[2]:
    print("\nAvailable T values for k=2, r=1:")
    print(list(CRITICAL_VALUES[2][1].keys()))

---

Complete Workflow Example

"""
Complete workflow: PPP Hypothesis Testing
"""
import numpy as np
from tvrcoint import BootstrapTVCTest
from tvrcoint.simulation import generate_cointegrated_data
from tvrcoint.output import results_to_latex

# 1. Load or simulate data
np.random.seed(42)
Y = generate_cointegrated_data(T=200, k=3, r=1)
# Or: Y = pd.read_csv('ppp_data.csv').values

# 2. Set test parameters
r = 1    # Cointegration rank
m = 5    # Chebyshev polynomial order
p = 1    # VAR lag order
B = 399  # Bootstrap replications

# 3. Perform bootstrap test
test = BootstrapTVCTest()
results = test.test(
    Y, r=r, m=m, p=p,
    method='wild',
    restricted=False,
    B=B,
    seed=42
)

# 4. Display results
print(results.summary())

# 5. Interpretation
if results.pvalue_bootstrap < 0.05:
    print("Conclusion: Reject H0 -> Time-varying cointegration")
else:
    print("Conclusion: Cannot reject H0 -> Time-invariant cointegration")

# 6. Export for publication
latex_table = results_to_latex(results)
with open('ppp_results.tex', 'w') as f:
    f.write(latex_table)

---

Citation

If you use this library in your research, please cite:

@article{bierens2010time,
  title={Time-Varying Cointegration},
  author={Bierens, Herman J. and Martins, Luis F.},
  journal={Econometric Theory},
  volume={26},
  number={5},
  pages={1453--1490},
  year={2010},
  publisher={Cambridge University Press}
}

@article{martins2015bootstrap,
  title={Bootstrap Tests for Time Varying Cointegration},
  author={Martins, Luis F.},
  journal={Econometric Reviews},
  year={2015},
  publisher={Taylor \& Francis}
}

Author

Dr Merwan Roudane

• Email: [e-mail]
• GitHub: https://github.com/merwanroudane/tvrcoint

License

This project is licensed under the MIT License - see the LICENSE file for details.