tsecon

High-performance time series econometrics: a Rust core with a Python-first API.

Pre-1.0 software under active development. The name is settled — tsecon is what you install and import — but the API may still change before 1.0.

tsecon brings the scattered toolkit of modern time series econometrics — diagnostics and specification tests, ARIMA, GARCH, VARs and structural identification, local projections, Bayesian VARs, nowcasting, predictive regressions, panels, the term structure — into one fast, validated, Python-first library. The compute core is written from scratch in Rust (no BLAS, no heavy dependencies) so bootstrap inference and Monte Carlo work that is painfully slow elsewhere runs in seconds, with results bit-reproducible at any thread count.

Every estimator is validation-gated: its numbers are checked against a reference implementation (statsmodels, SciPy, NumPy, arch, linearmodels, scikit-learn, ArviZ) or a documented closed form before it ships, and a cross-library parity gate re-verifies the agreement in CI on every push. The library ships no data loaders and makes no network calls — the only runtime dependency is NumPy.

Install

pip install tsecon

A single self-contained wheel whose only runtime dependency is NumPy — no Rust toolchain, no system BLAS. Prebuilt wheels cover Linux (x86_64, aarch64), macOS on Apple Silicon (arm64), and Windows (x64), for every Python >= 3.9. Plotting is an optional extra:

pip install 'tsecon[plots]'    # adds matplotlib for the .plot_*() methods

Then check what you got:

import tsecon
print(tsecon.__version__)   # 0.2.0

There is no prebuilt wheel for Intel macOS (x86_64); on that platform, and for building from a checkout, pip compiles from the source distribution, which needs a Rust toolchain and Python >= 3.9. Contributors build with maturin:

pip install maturin
maturin develop --release -m bindings/python/Cargo.toml   # builds + installs into the active venv

A taste

import numpy as np
import tsecon

rng = np.random.default_rng(0)
y = np.cumsum(rng.standard_normal(300))          # a random walk

tsecon.check_stationarity(y)["recommendation"]   # -> "Difference"

# Fit a VAR, read off impulse responses, decompose the variance
data = rng.standard_normal((200, 3))
irf  = tsecon.var_irf(data, lags=2, horizon=16)          # [h][response][shock]
fevd = tsecon.var_fevd(data, lags=2, horizon=16)

# Robust SEs, exact-MLE ARIMA with a forecast fan, GARCH with robust SEs,
# instrumented local projections, a fiscal multiplier, a Bayesian VAR...
tsecon.ols(y, X, se_type="hac")
tsecon.arima_fit(y, p=1, d=1, q=1, forecast_steps=12, conf_alpha=0.1)
tsecon.garch_fit(returns, vol="gjr", dist="t")
tsecon.lp_multiplier(y_out, spending, instrument, horizons=20)
tsecon.bvar_irf_draws(data, lags=2, horizon=12, n_draws=800)

Every function takes plain NumPy arrays and returns arrays or dicts of documented keys. An opt-in tsecon.results layer wraps the same calls in objects that render themselves (.summary(), .plot_irf()) without changing the dict contract.

What's here today

126 functions across the full applied workflow: the diagnostic battery and unit-root workflow (ADF, KPSS, check_stationarity, and the one-call check_series battery with model recommendations); specification and stability tests (White, Breusch-Pagan, RESET, Chow, CUSUM); robust and HAC standard errors; the bootstrap family; an exact-diffuse Kalman filter; ARIMA, GARCH/GJR/EGARCH, and GAS score-driven volatility; VAR/SVAR with sign-restricted identification, FAVAR, and Diebold-Yilmaz connectedness; local projections (lag-augmented, LP-IV, state-dependent, and the Ramey-Zubairy integral multiplier); a Minnesota-NIW Bayesian VAR; GMM and IV-GMM; predictive regressions with IVX inference; heterogeneous panels (mean group, CCE-MG, PMG); cointegration and Markov switching; MIDAS and DFM nowcasting with a news decomposition; multivariate GARCH; realized volatility; spectral analysis; long memory; recession-probability models; survey-expectations tools; the Nelson-Siegel/Svensson term structure with the arbitrage-free (AFNS) adjustment; and a linear rational-expectations (DSGE-lite) solver.

The library ships with complete type stubs (py.typed), so autocomplete and type checking work out of the box.

Learn more

The full documentation — a 15-chapter guide to time series econometrics, model cards for every estimator family, a worked figure gallery, two replications of published results, a Monte Carlo validation suite, and an honest benchmark harness — lives in the project repository.

License

MIT OR Apache-2.0.

Project links

Data verified by PyPI on Sep 3, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.6.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for ccoleman from gravatar.com ccoleman

Credits

Author: Chase Coleman

GitHub Statistics

Data verified by PyPI on Sep 3, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT OR Apache-2.0
View SPDX License List

Requires

Python >=3.9

Provides Extra

all plots polars

Tags

time series econometrics forecasting VAR SVAR BVAR local projections GARCH ARIMA Kalman filter impulse response Bayesian nowcasting macroeconomics statistics

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

tsecon-0.5.0.tar.gz (1.9 MB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

tsecon-0.5.0-cp39-abi3-win_amd64.whl (4.7 MB view details)

Uploaded CPython 3.9+Windows x86-64

tsecon-0.5.0-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.3 MB view details)

Uploaded CPython 3.9+manylinux: glibc 2.17+ x86-64

tsecon-0.5.0-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (4.4 MB view details)

Uploaded CPython 3.9+manylinux: glibc 2.17+ ARM64

tsecon-0.5.0-cp39-abi3-macosx_11_0_arm64.whl (4.0 MB view details)

Uploaded CPython 3.9+macOS 11.0+ ARM64

File details

Details for the file tsecon-0.5.0.tar.gz.

File metadata

  • Download URL: tsecon-0.5.0.tar.gz
  • Upload date:
  • Size: 1.9 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for tsecon-0.5.0.tar.gz
Algorithm Hash digest
SHA256 4c4075872bdf814b1cb5610ab3a23dba4fd9d9a5b9ec6bc0f16bd4dd43b6bb74
MD5 5128c6c1ed321a275482fe1e08baa3c3
BLAKE2b-256 8629c1483dde8fed5b3d70d844a0ae6bd2b3a3181a860ad7642c498d17eaeb78

See more details on using hashes here.

Provenance

The following attestation bundles were made for tsecon-0.5.0.tar.gz:

Publisher: release.yml on cacoleman16/tsecon

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file tsecon-0.5.0-cp39-abi3-win_amd64.whl.

File metadata

  • Download URL: tsecon-0.5.0-cp39-abi3-win_amd64.whl
  • Upload date:
  • Size: 4.7 MB
  • Tags: CPython 3.9+, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for tsecon-0.5.0-cp39-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 1959bdc29e7d205e8788edc2dd6a5008877e2a66f0c4b50554db31545508b440
MD5 67968402718674df609f91b8b0a46934
BLAKE2b-256 746d2f0ca4eee65950e1be65457b2e73504ce4545527e14dec85726c90bc5749

See more details on using hashes here.

Provenance

The following attestation bundles were made for tsecon-0.5.0-cp39-abi3-win_amd64.whl:

Publisher: release.yml on cacoleman16/tsecon

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file tsecon-0.5.0-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for tsecon-0.5.0-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 3099f2e62facea1d45af808cece60af9b136acfee027e5858e45dafc868f359e
MD5 1c350a264155c3e05762e4123c8c4407
BLAKE2b-256 487423f525b2187da9cc8848aed7bc1a7e4400e20f82da9d8a036a31e62c0e58

See more details on using hashes here.

Provenance

The following attestation bundles were made for tsecon-0.5.0-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl:

Publisher: release.yml on cacoleman16/tsecon

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file tsecon-0.5.0-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for tsecon-0.5.0-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 f05fc9c91cd0f63b2f1093715e99937ab9394301f58fbbea0d748782ecc37448
MD5 2a07af19fe1e4b03ceff6317b8e8f2cf
BLAKE2b-256 a93d0231b74b51776aa02b128234c394e7dd5699bb50105e1692735a36fac7c0

See more details on using hashes here.

Provenance

The following attestation bundles were made for tsecon-0.5.0-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl:

Publisher: release.yml on cacoleman16/tsecon

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file tsecon-0.5.0-cp39-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for tsecon-0.5.0-cp39-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 c7676e615e71ca0b1d7a40bb91e7b5edc10cfa509ebf5b60d1917625c0bd4f0b
MD5 97aed9cd47239eb1f6bf17dc2036cc6f
BLAKE2b-256 499b1e55a895953a54e6de53bc9256fa22b597e11f95fb8447ed70d870a48b29

See more details on using hashes here.

Provenance

The following attestation bundles were made for tsecon-0.5.0-cp39-abi3-macosx_11_0_arm64.whl:

Publisher: release.yml on cacoleman16/tsecon

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

0.6.0

5 files

This release

0.5.0 This release

5 files

0.4.0

5 files

0.3.0

5 files

0.2.0

5 files

0.1.0

5 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page