Metadata-Version: 2.2
Name: alexandria-python
Version: 2.0.1
Summary: a software for Bayesian vector autoregressions and other Bayesian time-series applications
Author: Romain Legrand
Author-email: alexandria.toolbox@gmail.com
License: Other/Proprietary License
Keywords: python,Bayesian,time-series,econometrics
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: License :: Other/Proprietary License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: keywords
Dynamic: license
Dynamic: requires-python
Dynamic: summary

# Alexandria

**Alexandria** is a Python package for Bayesian time-series econometrics applications. This is the second official release of the software, which introduces Bayesian vector autorgressions.

This is version 2.0, which includes Bayesian regression, Bayesian vector autoregression, and Bayesian VEC/VARMA models.

Alexandria offers a range of Bayesian linear regression models:

- 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 also offers a large number of Bayesian vector autoregression models and applications:

- maximum likelihood (OLS) VAR
- Litterman Minnesota prior
- normal-Wishart prior
- independent prior with Gibbs sampling
- dummy observation prior
- large Bayeisian VAR prior
- Bayesian oxy-SVAR

prior customization:
- constrained coefficients
- dummy extensions (sums-of-coefficients, initial observation,long-run prior)
- stationary priors
- hyperparameter optimization from marginal likelihood

structural identification:
- Cholesky
- triangular factorization
- restrictions:  sign and zero restrictions on IRFs, narrative on shocks and historical decomposition

applications:
- forecasts
- impulse response function
- forecast error variance decomposition
- historical decomposition
- conditional forecasts (agnostic and sctructural approaches, allowing for hard and soft conditions)

The current version includes Bayesian VEC and VARMA models, along with many applications:

- Bayesian VEC: uninformative, horseshoe and selection priors; general and reduced-rank approaches
- Bayesian VARMA: Minnesota prior on autoegressive and lag coefficients; residuals estimated from Bayesian state-space modelling
- structural identification and applications are the same as the Bayesian VAR models


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 import NormalWishartBayesianVar
	from alexandria import DataSets
	from alexandria import Graphics
	import numpy as np

	# load ISLM dataset
	ds = DataSets()
	islm_data = ds.load_islm()[:,:4]

	# create and train Bayesian VAR with default settings
	var = NormalWishartBayesianVar(endogenous = islm_data)
	var.estimate()

	# estimate forecasts for the next 4 periods, 60% credibility level
	forecast_estimates = var.forecast(4, 0.6)

	# create graphics of predictions
	gp = Graphics(var)
	gp.forecast_graphics(show=True, save=False)

===============================

**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**

alexandria.toolbox@gmail.com
