narayanpop 0.0.1

Narayan-Popp ADF Unit Root Test with Two Structural Breaks

narayanpop

Narayan-Popp ADF Unit Root Test with Two Structural Breaks

A Python implementation of the unit root test with two structural breaks proposed by Narayan and Popp (2010).

Reference

Narayan, P. K. and Popp, S. (2010), "A new unit root test with two structural breaks in level and slope at unknown time", Journal of Applied Statistics, 37(9), 1425-1438.

DOI: 10.1080/02664760903039883

Features

• ✅ Exact replication of the original GAUSS code and paper methodology
• ✅ Two model specifications:
  • Model 1 (Model A): Two breaks in level
  • Model 2 (Model C): Two breaks in level and trend
• ✅ Sequential break date selection procedure
• ✅ Innovational Outlier (IO) model for gradual breaks
• ✅ Flexible lag selection: AIC, SIC, or t-statistic criterion
• ✅ Publication-ready output formatted for top-tier journals
• ✅ Critical values for various sample sizes (T ≤ 50, 50 < T ≤ 200, 200 < T ≤ 400, T > 400)
• ✅ Panel data support for testing multiple series

Installation

pip install narayanpop

Or install from source:

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

Quick Start

Basic Usage

import numpy as np
import pandas as pd
from narayanpop import adf_2breaks

# Generate sample data
np.random.seed(42)
y = np.cumsum(np.random.randn(100))

# Run test with Model 1 (breaks in level only)
result = adf_2breaks(y, model=1)

# Print formatted results
print(result.summary())

Working with Time Series Data

import pandas as pd
from narayanpop import adf_2breaks

# Load your data
dates = pd.date_range('1960', periods=100, freq='Y')
y = pd.Series(np.cumsum(np.random.randn(100)), index=dates)

# Run test with Model 2 (breaks in level and trend)
result = adf_2breaks(y, model=2, pmax=8, ic=3, trimm=0.10)

# Access results
print(f"Test Statistic: {result.test_statistic:.4f}")
print(f"First Break: {result.break1}")
print(f"Second Break: {result.break2}")
print(f"Optimal Lag: {result.optimal_lag}")
print(f"Critical Values: {result.critical_values}")

Panel Data Analysis

import pandas as pd
from narayanpop import adf_2breaks_panel

# Load panel data
data = pd.DataFrame({
    'GDP': np.cumsum(np.random.randn(100)),
    'CPI': np.cumsum(np.random.randn(100)),
    'Unemployment': np.cumsum(np.random.randn(100))
})

# Test all series
results_df = adf_2breaks_panel(data, model=1)
print(results_df)

Methodology

Models

Model 1 (Model A): Break in Level

Δy_t = ρy_{t-1} + α_1 + β*t + θ_1*D(TB)_{1,t} + θ_2*D(TB)_{2,t} 
       + δ_1*DU_{1,t-1} + δ_2*DU_{2,t-1} + Σβ_j*Δy_{t-j} + ε_t

Model 2 (Model C): Break in Level and Trend

Δy_t = ρy_{t-1} + α* + β*t + κ_1*D(TB)_{1,t} + κ_2*D(TB)_{2,t}
       + δ*_1*DU_{1,t-1} + δ*_2*DU_{2,t-1} + γ*_1*DT_{1,t-1} + γ*_2*DT_{2,t-1}
       + Σβ_j*Δy_{t-j} + ε_t

Where:

• DU_{i,t} = 1 if t > TB_i, 0 otherwise (level shift dummy)
• DT_{i,t} = (t - TB_i) if t > TB_i, 0 otherwise (trend shift dummy)
• D(TB)_{i,t} = 1 if t = TB_i + 1, 0 otherwise (impulse dummy)

Break Date Selection

The test uses a sequential procedure:

1. First Break: Maximize |t_θ1| (Model 1) or |t_κ1| (Model 2)
2. Second Break: Conditional on the first, maximize |t_θ2| or |t_κ2|

This approach is computationally efficient (2T operations vs T² for grid search).

Null and Alternative Hypotheses

• H₀: Unit root with structural breaks (y_t is I(1) with breaks)
• H₁: Trend stationary with structural breaks (y_t is I(0) around a broken trend)

Parameters

adf_2breaks(y, model, pmax=8, ic=3, trimm=0.10)

Parameter	Type	Description	Default
y	array-like	Data series (1D array or pandas Series)	Required
model	int	Model specification: 1 (level breaks) or 2 (level & trend breaks)	Required
pmax	int	Maximum number of lags for Δy	8
ic	int	Information criterion: 1 (AIC), 2 (SIC), 3 (t-stat)	3
trimm	float	Trimming rate for break search (0 < trimm < 0.5)	0.10

Output

ADF2BreaksResult Object

Attribute	Type	Description
test_statistic	float	ADF test statistic
break1	int/date	First break location
break2	int/date	Second break location
optimal_lag	int	Selected lag length
critical_values	dict	Critical values at 1%, 5%, 10% levels
model	int	Model specification used
nobs	int	Number of observations

Methods

• summary(): Returns formatted output suitable for journal publication

Critical Values

Critical values from Narayan & Popp (2010), Table 3:

Model 1 (Break in Level)

Sample Size	1%	5%	10%
T ≤ 50	-5.259	-4.514	-4.143
50 < T ≤ 200	-4.958	-4.316	-3.980
200 < T ≤ 400	-4.731	-4.136	-3.825
T > 400	-4.672	-4.081	-3.772

Model 2 (Break in Level and Trend)

Sample Size	1%	5%	10%
T ≤ 50	-5.949	-5.181	-4.789
50 < T ≤ 200	-5.576	-4.937	-4.596
200 < T ≤ 400	-5.318	-4.741	-4.430
T > 400	-5.287	-4.692	-4.396

Examples

Example 1: Nelson-Plosser Data

import pandas as pd
from narayanpop import adf_2breaks

# Real GNP data (1909-1970)
data = pd.read_csv('nelson_plosser.csv', index_col=0)
y = data['Real_GNP']

# Test with Model 2
result = adf_2breaks(y, model=2, pmax=8, ic=3)
print(result.summary())

Output:

======================================================================
Narayan-Popp ADF Unit Root Test with Two Structural Breaks
======================================================================
Model: Model C (Break in Level and Trend)
Number of observations: 62
Optimal lag length: 2

Test Results:
----------------------------------------------------------------------
ADF test statistic:    -5.5970

Critical Values:
  1% level:     -5.9490
  5% level:     -5.1810
 10% level:     -4.7890

Structural Breaks:
  First break:  1921 (19.35%)
  Second break: 1938 (46.77%)

Conclusion: Reject H0 at 5% level: Evidence AGAINST unit root **
======================================================================
Note: *** 1%, ** 5%, * 10% significance levels
H0: Unit root with structural breaks
H1: Trend stationary with structural breaks
======================================================================

Example 2: US Macroeconomic Data

from narayanpop import adf_2breaks

# CPI data (1948-2007)
result = adf_2breaks(cpi_data, model=1, pmax=8, ic=3, trimm=0.10)

if result.test_statistic < result.critical_values['5%']:
    print(f"Reject unit root at 5% level")
    print(f"Breaks detected at: {result.break1}, {result.break2}")
else:
    print("Cannot reject unit root hypothesis")

Example 3: Monte Carlo Simulation

import numpy as np
from narayanpop import adf_2breaks

# Simulation parameters
T = 100
n_sims = 1000
rejections = 0

for i in range(n_sims):
    # Generate I(1) data with no breaks
    y = np.cumsum(np.random.randn(T))
    
    # Run test
    result = adf_2breaks(y, model=1, pmax=8, ic=3)
    
    # Check rejection at 5% level
    if result.test_statistic < result.critical_values['5%']:
        rejections += 1

print(f"Empirical size at 5% level: {rejections/n_sims:.3f}")
# Should be close to 0.05

Comparison with Related Tests

Test	Breaks	Under H₀	Under H₁	Type
Narayan-Popp (2010)	2	Yes	Yes	ADF-IO
Lee-Strazicich (2003)	2	Yes	Yes	LM
Lumsdaine-Papell (1997)	2	No	Yes	ADF-IO
Perron (1989)	1	Yes	Yes	ADF-IO
Zivot-Andrews (1992)	1	No	Yes	ADF-IO

Key Advantage: Narayan-Popp allows for breaks under both null and alternative hypotheses, avoiding spurious rejections that occur with tests that only allow breaks under H₁.

Testing Strategy

Step 1: Choose Model

• Use Model 1 if only level shifts are expected
• Use Model 2 if both level and trend changes are possible

Step 2: Set Parameters

• pmax: Rule of thumb: int(12*(T/100)^{1/4}) or 8 for T ≈ 100
• ic: Use 3 (t-stat) for general-to-specific approach
• trimm: Keep at 0.10 (following Zivot-Andrews, Lumsdaine-Papell)

Step 3: Interpret Results

1. Compare test statistic to critical values
2. If reject H₀: evidence of trend stationarity with breaks
3. Check break dates for economic/historical relevance
4. Verify optimal lag is reasonable

Step 4: Robustness Checks

• Try both models
• Vary pmax
• Check sensitivity to trimming rate

Validation

This implementation has been validated against:

1. ✅ Original GAUSS code (Saban Nazlioglu)
2. ✅ Critical values from Narayan & Popp (2010), Table 3
3. ✅ Nelson-Plosser dataset results
4. ✅ Monte Carlo simulations for size and power properties

Technical Notes

Innovational Outlier (IO) Model

The IO model assumes breaks occur gradually rather than instantaneously:

• More realistic for economic time series
• Breaks affect the series through the same dynamic process as innovations
• Specified through the inclusion of Ψ*(L) in the deterministic component

Computational Efficiency

• Sequential procedure: ~2T operations
• Grid search: ~T² operations
• For T=100: Sequential is ~50x faster

Trimming

Default trimming (0.10) excludes:

• First 10% of observations from break1 search
• Last 10% of observations from break2 search
• Ensures sufficient observations on each side of breaks

Troubleshooting

Issue: "Data contains missing values"

Solution: Remove or interpolate NaN values before testing

y = y.dropna()  # or y.fillna(method='ffill')

Issue: "Optimal lag is 0"

Solution: Normal if series is white noise or pmax too small. Consider:

• Increasing pmax
• Using different ic criterion
• Checking data quality

Issue: "No clear breaks detected"

Solution:

• Try different model specification
• Check if breaks actually exist in data
• Consider single-break tests first

Citation

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

@article{narayan2010unit,
  title={A new unit root test with two structural breaks in level and slope at unknown time},
  author={Narayan, Paresh Kumar and Popp, Stephan},
  journal={Journal of Applied Statistics},
  volume={37},
  number={9},
  pages={1425--1438},
  year={2010},
  publisher={Taylor \& Francis}
}

@software{narayanpop2024,
  author = {Roudane, Merwan},
  title = {narayanpop: Python implementation of Narayan-Popp unit root test},
  year = {2024},
  url = {https://github.com/merwanroudane/narayanpop},
  version = {0.0.1}
}

Related Packages

• statsmodels: General econometrics (ADF, KPSS, etc.)
• arch: ARCH/GARCH models and unit root tests
• linearmodels: Panel data econometrics

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/NewFeature)
3. Commit your changes (git commit -am 'Add NewFeature')
4. Push to the branch (git push origin feature/NewFeature)
5. Open a Pull Request

License

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

Author

Dr Merwan Roudane

• Email: [e-mail]
• GitHub: @merwanroudane

Acknowledgments

• Original GAUSS code by Saban Nazlioglu
• Narayan & Popp (2010) for the methodology
• The econometrics community for valuable feedback

Changelog

Version 0.0.1 (2024)

• Initial release
• Full implementation of Narayan-Popp test
• Model 1 and Model 2 support
• Panel data functionality
• Publication-ready output formatting

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Note: This is an independent implementation for research purposes. For commercial applications, please verify results with the original paper and code.