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RLS: Final clean up prior to release
Final documentation cleanup
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‎.travis.yml

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- PANDAS=pandas
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- COVERAGE=true
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- SKIPSLOW=""
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- DOCTR_PUSH_TYPE=""
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- secure: "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"
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matrix:
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- pip install sphinx --upgrade
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- export PYTHONHASHSEED=0
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- export MKL_NUM_THREADS=1
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- if [[ ${TRAVIS_TAG} ]]; then export DOCTR_PUSH_TYPE="--no-require-master"; fi
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- echo ${DOCTR_PUSH_TYPE}
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install:
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- python setup.py develop
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script:
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- pytest -n 2 --cov-config .coveragerc --cov=linearmodels linearmodels --durations=10 ${SKIPSLOW}
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- set -e
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- if [ ${DOCBUILD} ]; then cd doc && make html && make html && cd .. && doctr deploy doc; fi
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- if [ ${DOCBUILD} ]; then cd doc && make html && make html && cd .. && doctr deploy doc ${DOCTR_PUSH_TYPE}; fi
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after_success:
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- if [ ${COVERAGE} = true ]; then codecov; fi

‎doc/source/asset-pricing/convert-lyx.cmd

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@@ -3,4 +3,5 @@ pandoc -s mathematical-detail.tex -o mathematical-detail.rst
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copy /Y mathematical-detail.rst mathematical-detail-pre.txt
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del mathematical-detail.rst
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python -c "open('mathematical-detail.txt','w').write(''.join(open('mathematical-detail-pre.txt').readlines()[3:]))"
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del mathematical-detail-pre.txt
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del mathematical-detail-pre.txt
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del mathematical-detail.tex

‎doc/source/asset-pricing/covariance.rst

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.. _asset_pricing-module-reference-covariance:
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Linear Asset Pricing Model Covariance Estimators
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------------------------------------------------
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.. module:: linearmodels.asset_pricing.covariance
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:synopsis: Covariance estimators for asset pricing models
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Heteroskedasticity Robust Covariance Estimation
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===============================================
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.. autoclass:: HeteroskedasticCovariance
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:members:
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:inherited-members:
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HAC Robust (Kernel) Covariance Estimation
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=========================================
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.. autoclass:: KernelCovariance
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:members:
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:inherited-members:

‎doc/source/asset-pricing/introduction.rst

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LinearFactorModel Estimation Summary
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================================================================================
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No. Test Portfolios: 6 R-squared: 0.8879
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No. Factors: 3 J-statistic: 30.694
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No. Factors: 3 J-statistic: 39.109
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No. Observations: 819 P-value 0.0000
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Date: Thu, May 18 2017 Distribution: chi2(3)
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Time: 22:23:54
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Date: Sun, May 21 2017 Distribution: chi2(3)
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Time: 21:18:56
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Cov. Estimator: kernel
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Risk Premia Estimates
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==============================================================================
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Parameter Std. Err. T-stat P-value Lower CI Upper CI
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------------------------------------------------------------------------------
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MktRF 0.0060 0.0113 0.5305 0.5958 -0.0162 0.0282
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SMB 0.0001 0.0081 0.0178 0.9858 -0.0157 0.0160
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HML 0.0045 0.0032 1.4041 0.1603 -0.0018 0.0108
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MktRF 0.0060 0.0016 3.7381 0.0002 0.0029 0.0092
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SMB 0.0001 0.0011 0.1281 0.8980 -0.0021 0.0023
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HML 0.0045 0.0012 3.7904 0.0002 0.0022 0.0068
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==============================================================================
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See full_summary for complete results
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Covariance estimator:
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KernelCovariance, Kernel: bartlett, Bandwidth: 4
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See full_summary for complete results

‎doc/source/asset-pricing/mathematical-detail.txt

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g_{t}=\left[\begin{array}{c}
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f_{c,t}\otimes\epsilon_{t}\\
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\beta^{\prime}\left(r_{t}-\left[1_{N}\,\beta\right]\lambda\right)\\
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\left[1_{N}\,\beta\right]^{\prime}\left(r_{t}-\left[1_{N}\,\beta\right]\lambda\right)\\
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r_{t}-\left[1_{N}\,\beta\right]\lambda-\alpha
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\end{array}\right]
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\left[\begin{array}{c}
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f_{c,t}\otimes\epsilon_{t}\\
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\beta^{\prime}\Sigma^{-1}\left(r_{t}-\left[1_{N}\,\beta\right]\lambda\right)\\
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r_{t}-\left[1_{N}\,\beta\right]\lambda-\alpha
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\beta_{c}^{\prime}\Sigma^{-1}\left(r_{t}-\beta_{c}\lambda\right)\\
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r_{t}-\beta_{c}\lambda-\alpha
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\end{array}\right]
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The Jacobian is than
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.. math::
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\hat{G}=-\left[\begin{array}{cccc}
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I_{N}\otimes\Sigma_{f_{c}} & 0 & 0 & 0\\
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G_{12} & -\hat{\beta}_{c}^{\prime}\Sigma^{-1}\hat{\beta} & \hat{\beta}_{c}^{\prime}\Sigma^{-1}\hat{\beta}_{c} & 0\\
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I_{N}\otimes\left[0\ \tilde{\lambda}\right] & -\hat{\beta} & \hat{\beta}_{c} & I_{N}
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\hat{G}=-\left[\begin{array}{ccc}
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I_{N}\otimes\Sigma_{f_{c}} & 0 & 0\\
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G_{12} & \hat{\beta}_{c}^{\prime}\Sigma^{-1}\hat{\beta}_{c} & 0\\
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I_{N}\otimes\left[0\ \tilde{\lambda}\right] & \hat{\beta}_{c} & I_{N}
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\end{array}\right]
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:math:`\beta_{c}=\left[1_{N}\,\beta\right]`.

‎doc/source/asset-pricing/reference.rst

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models
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results
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covariance
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‎doc/source/panel/covariance.rst

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.. autoclass:: DriscollKraay
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:members:
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:inherited-members:
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AC Robust Estimator
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===================
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.. autoclass:: ACCovariance
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:members:
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:inherited-members:
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Fama-MacBeth Covariance Estimator
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=================================
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.. autoclass:: FamaMacBethCovariance
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:members:
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:inherited-members:
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Fama-MacBeth HAC Covariance Estimator
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=====================================
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.. autoclass:: FamaMacBethKernelCovariance
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:members:
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:inherited-members:

‎doc/source/panel/pandas.rst

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.. _panel-pandas-differences:
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Comparison with pandas PanelOLS
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===============================
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Comparison with pandas PanelOLS and FamaMacBeth
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===============================================
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pandas deprecated ``PanelOLS`` (``pandas.stats.plm.PanelOLS``) in 0.18 and
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dropped it in 0.20. :class:`linearmodels.panel.model.PanelOLS` provides
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pandas deprecated ``PanelOLS`` (``pandas.stats.plm.PanelOLS``) and
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``FamaMacBeth`` (``pandas.stats.plm.FamaMacBeth``) in 0.18 and
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dropped it in 0.20. :class:`linearmodels.panel.model.PanelOLS`
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and :class:`linearmodels.panel.model.FamaMacBeth` provide
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a similar set of functionality with a few notable differences:
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1. When using a MultiIndex DataFrame, this package expects the MultiIndex

‎doc/source/plan.rst

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Module Plans
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------------
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The plan for this modules is to add some key missing linear models. This plan is intentionally
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limited since there is no intention to statsmodels.
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limited since there is no intention to replicate estimators available in statsmodels.
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Univariate
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==========
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* GMM IV Estimation - :class:`linearmodels.iv.model.IVGMM`
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* GMM-CUE Estimation - :class:`linearmodels.iv.model.IVGMMCUE`
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In Progress
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***********
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* 2-step Linear Asset Pricing Model Estimation - :class:`linearmodels.asset_pricing.model.LinearFactorModel`
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* Linear Asset Pricing Model Estimation for Traded Factors - :class:`linearmodels.asset_pricing.model.TradedFactorModel`
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Panel
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=====
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* Fama-MacBeth - :class:`linearmodels.panel.model.FamaMacBeth`
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Asset Pricing
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*************
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* 2-step Linear Asset Pricing Model Estimation - :class:`linearmodels.asset_pricing.model.LinearFactorModel`
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* GMM Linear Asset Pricing Model Estimation - :class:`linearmodels.asset_pricing.model.LinearFactorModelGMM`
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* Linear Asset Pricing Model Estimation for Traded Factors - :class:`linearmodels.asset_pricing.model.TradedFactorModel`
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Instrumental Variable Estimators
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********************************
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‎linearmodels/asset_pricing/covariance.py

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"""
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Covariance estimators for linear factor models
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"""
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from numpy.linalg import inv
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from linearmodels.iv.covariance import KERNEL_LOOKUP, kernel_optimal_bandwidth, _cov_kernel
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class KernelCovariance(HeteroskedasticCovariance):
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"""
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Heteroskedasticity robust covariance estimator
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Heteroskedasticity-autocorrelation (HAC) robust covariance estimator
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Parameters
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----------

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