0.7 Release

Release 0.7.0

Release summary

Note: This version has never been officially released. Several models have been refactored, improved or bugfixed in 0.8.

The following major new features appear in this version.

Principal Component Analysis

Author: Kevin Sheppard

A new class-based Principal Component Analysis has been added. This class replaces the function-based PCA that previously existed in the sandbox. This change bring a number of new features, including:

  • Options to control the standardization (demeaning/studentizing)

  • Scree plotting

  • Information criteria for selecting the number of factors

  • R-squared plots to assess component fit

  • NIPALS implementation when only a small number of components are required and the dataset is large

  • Missing-value filling using the EM algorithm

import statsmodels.api as sm
from statsmodels.multivariate.pca import PCA

data = sm.datasets.fertility.load_pandas().data

columns = map(str, range(1960, 2012))
data.set_index('Country Name', inplace=True)
dta = data[columns]
dta = dta.dropna()

pca_model = PCA(dta.T, standardize=False, demean=True)
pca_model.plot_scree()

Note : A function version is also available which is compatible with the call in the sandbox. The function version is just a thin wrapper around the class-based PCA implementation.

Regression graphics for GLM/GEE

Author: Kerby Shedden

Added variable plots, partial residual plots, and CERES residual plots are available for GLM and GEE models by calling the methods plot_added_variable, plot_partial_residuals, and plot_ceres_residuals that are attached to the results classes.

State Space Models

Author: Chad Fulton

State space methods provide a flexible structure for the estimation and analysis of a wide class of time series models. The Statsmodels implementation allows specification of state models, fast Kalman filtering, and built-in methods to facilitate maximum likelihood estimation of arbitrary models. One of the primary goals of this module is to allow end users to create and estimate their own models. Below is a short example demonstrating the ease with which a local level model can be specified and estimated:

import numpy as np
import statsmodels.api as sm
import pandas as pd

data = sm.datasets.nile.load_pandas().data
data.index = pd.DatetimeIndex(data.year.astype(int).astype(str), freq='AS')

# Setup the state space representation
class LocalLevel(sm.tsa.statespace.MLEModel):
    def __init__(self, endog):
        # Initialize the state space model
        super(LocalLevel, self).__init__(
            endog, k_states=1, initialization='approximate_diffuse')

        # Setup known components of state space representation matrices
        self.ssm['design', :] = 1.
        self.ssm['transition', :] = 1.
        self.ssm['selection', :] = 1.

    # Describe how parameters enter the model
    def update(self, params, transformed=True):
        params = super(LocalLevel, self).update(params, transformed)
        self.ssm['obs_cov', 0, 0] = params[0]
        self.ssm['state_cov', 0, 0] = params[1]

    def transform_params(self, params):
        return params**2  # force variance parameters to be positive

    # Specify start parameters and parameter names
    @property
    def start_params(self):
        return [np.std(self.endog)]*2

    @property
    def param_names(self):
        return ['sigma2.measurement', 'sigma2.level']

# Fit the model with maximum likelihood estimation
mod = LocalLevel(data['volume'])
res = mod.fit()
print res.summary()

The documentation and example notebooks provide further examples of how to form state space models. Included in this release is a full-fledged model making use of the state space infrastructure to estimate SARIMAX models. See below for more details.

Time Series Models (ARIMA) with Seasonal Effects

Author: Chad Fulton

A model for estimating seasonal autoregressive integrated moving average models with exogenous regressors (SARIMAX) has been added by taking advantage of the new state space functionality. It can be used very similarly to the existing ARIMA model, but works on a wider range of specifications, including:

  • Additive and multiplicative seasonal effects

  • Flexible trend specications

  • Regression with SARIMA errors

  • Regression with time-varying coefficients

  • Measurement error in the endogenous variables

Below is a short example fitting a model with a number of these components, including exogenous data, a linear trend, and annual multiplicative seasonal effects.

import statsmodels.api as sm
import pandas as pd

data = sm.datasets.macrodata.load_pandas().data
data.index = pd.DatetimeIndex(start='1959-01-01', end='2009-09-01',
                              freq='QS')
endog = data['realcons']
exog = data['m1']

mod = sm.tsa.SARIMAX(endog, exog=exog, order=(1,1,1),
                     trend='t', seasonal_order=(0,0,1,4))
res = mod.fit()
print res.summary()

Generalized Estimating Equations GEE

Author: Kerby Shedden

Enhancements and performance improvements for GEE:

  • EquivalenceClass covariance structure allows covariances to be specified by arbitrary collections of equality constraints #2188

  • add weights #2090

  • refactored margins #2158

MixedLM

Author: Kerby Shedden with Saket Choudhary

Enhancements to MixedLM (#2363): added variance components support for MixedLM allowing a wider range of random effects structures to be specified; also performance improvements from use of sparse matrices internally for random effects design matrices.

Other important new features

  • GLM: add scipy-based gradient optimization to fit #1961 (Kerby Shedden)

  • wald_test_terms: new method of LikelihoodModels to compute wald tests (F or chi-square) for terms or sets of coefficients #2132 (Josef Perktold)

  • add cov_type with fixed scale in WLS to allow chi2-fitting #2137 #2143 (Josef Perktold, Christoph Deil)

  • VAR: allow generalized IRF and FEVD computation #2067 (Josef Perktold)

  • get_prediction new method for full prediction results (new API convention)

Major Bugs fixed

  • see github issues for a full list

  • bug in ARMA/ARIMA predict with exog #2470

  • bugs in VAR

  • x13: python 3 compatibility

Backwards incompatible changes and deprecations

  • List backwards incompatible changes

Development summary and credits

Note

Thanks to all of the contributors for the 0.7 release:

Note

  • Alex Griffing

  • Antony Lee

  • Chad Fulton

  • Christoph Deil

  • Daniel Sullivan

  • Hans-Martin von Gaudecker

  • Jan Schulz

  • Joey Stockermans

  • Josef Perktold

  • Kerby Shedden

  • Kevin Sheppard

  • Kiyoto Tamura

  • Louis-Philippe Lemieux Perreault

  • Padarn Wilson

  • Ralf Gommers

  • Saket Choudhary

  • Skipper Seabold

  • Tom Augspurger

  • Trent Hauck

  • Vincent Arel-Bundock

  • chebee7i

  • donbeo

  • gliptak

  • hlin117

  • jerry dumblauskas

  • jonahwilliams

  • kiyoto

  • neilsummers

  • waynenilsen

These lists of names are automatically generated based on git log, and may not be complete.