alexandria-python 0.1

a software for Bayesian time-series econometrics applications

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.

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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

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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)))

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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

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Contact

[e-mail]