a software for Bayesian time-series econometrics applications
Project description
Alexandria
Alexandria is a Python package for Bayesian time-series econometrics applications. This is the first official release of the software. For its first release, Alexandria includes only the most basic model: the linear regression. However, it proposes a wide range of Bayesian linear regressions:
- maximum likelihood / OLS regression (non-Bayesian)
- simple Bayesian regression
- hierarchical (natural conjugate) Bayesian regression
- independent Bayesian regression with Gibbs sampling
- heteroscedastic Bayesian regression
- autocorrelated Bayesian regression
Alexandria is user-friendly and can be used from a simple Graphical User Inteface (GUI). More experienced users can also run the models directly from the Python console by using the model classes and methods.
===============================
Installing Alexandria
Alexandria can be installed from pip:
pip install alexandria-python
A local installation can also obtain by copy-pasting the folder containing the toolbox programmes. The folder can be downloaded from the project website or Github repo:
https://alexandria-toolbox.github.io
https://github.com/alexandria-toolbox
===============================
Getting started
Simple Python example:
# imports
from alexandria.linear_regression import IndependentBayesianRegression
from alexandria.datasets import data_sets as ds
import numpy as np
# load Taylor dataset, split as train/test
taylor_data = ds.load_taylor()
y_train, X_train = taylor_data[:198,0], taylor_data[:198,1:]
y_test, X_test = taylor_data[198:,0], taylor_data[198:,1:]
# set prior mean and prior variance for the model
b = np.array([1.5, 0.5])
b_const = 1
V = np.array([0.01, 0.0025])
V_const = 0.01
# create and train regression
br = IndependentBayesianRegression(endogenous=y_train, exogenous=X_train,
constant=True, b_exogenous=b, V_exogenous=V, b_constant=b_const, V_constant=V_const)
br.estimate()
# get predictions on test sample, run forecast evaluation, display log score
estimates_forecasts = br.forecast(X_test, 0.95)
br.forecast_evaluation(y_test)
print('log score on test sample : ' + str(round(br.forecast_evaluation_criteria['log_score'], 2)))
===============================
Documentation
Complete manuals and user guides can be found on the project website and Github repo:
https://alexandria-toolbox.github.io
https://github.com/alexandria-toolbox
===============================
Contact
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for alexandria_python-0.1-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d0582557f6894a79b79562cae120a0089c9ba1203c4fecd1d261d3d5e614a098 |
|
MD5 | 938b69168f74e7469093f73072c8c589 |
|
BLAKE2b-256 | ea628413b249f4b209462e77a19b1647e1f79316a5d4f54858efcbf82d7dd768 |