Metadata-Version: 2.1
Name: alexandria-python
Version: 1.0.1
Summary: a software for Bayesian vector autoregressions and other Bayesian time-series applications
Home-page: UNKNOWN
Author: Romain Legrand
Author-email: alexandria.toolbox@gmail.com
License: Other/Proprietary License
Description: # 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.
        
        From version 0.1, 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
        
        The current version adds 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:
        - holesky
        - 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)
        
        
        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
        
Keywords: python,Bayesian,time-series,econometrics
Platform: UNKNOWN
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
