仪器化主成分分析_Python_下载.zip

# Instrumented Principal Components Analysis [](https://travis-ci.org/bkelly-lab/ipca) This is a Python implementation of the Instrumtented Principal Components Analysis framework by Kelly, Pruitt, Su (2017). ## Usage Exemplary use of the ipca package. The data is the seminal Grunfeld data set as provided on [statsmodels](http://www.statsmodels.org). Note, the `fit` method takes a panel of data, `X`, with the following columns: 1. entity id (numeric) 2. time (numeric) 3. and following columns contain characteristics. as well as a series of dependent variables, `y`, of the same length as `X`. ```python import numpy as np from statsmodels.datasets import grunfeld data = grunfeld.load_pandas().data data.year = data.year.astype(np.int64) # Establish unique IDs to conform with package N = len(np.unique(data.firm)) ID = dict(zip(np.unique(data.firm).tolist(),np.arange(1,N+1))) data.firm = data.firm.apply(lambda x: ID[x]) # use multi-index for panel groups data = data.set_index(['firm', 'year']) y = data['invest'] X = data.drop('invest', axis=1) # Call ipca from ipca import InstrumentedPCA regr = InstrumentedPCA(n_factors=1, intercept=False) regr = regr.fit(X=X, y=y) Gamma, Factors = regr.get_factors(label_ind=True) ``` ## Installing The latest release can be installed using pip ```bash pip install ipca ``` The master branch can be installed by cloning the repo and running setup ```bash git clone https://github.com/bkelly-lab/ipca.git cd ipca python setup.py install ``` ## Documenation The lastest documenation is published [HERE](https://bkelly-lab.github.io/ipca/). ## Requirements ### Running With the exception of Python 3.6+, which is a hard requirement, the others are the version that are being used in the test environment. It is possible that older versions work. * **Python 3.7+**: * NumPy (1.19+) * SciPy (1.6+) * Numba (0.53+) * progressbar (2.5+) * joblib (1.0.1+) ### Testing * pandas (1.2.3+) * scikit-learn (0.24+) * pytest (4.3+) * statsmodels (0.11+) ## Acknowledgements The implementation is inspired by the MATLAB code for IPCA made available on [Seth Pruitt's](https://sethpruitt.net/research/) website. ## References 1. Kelly, Pruitt, Su (2017). "Instrumented Principal Components Analysis" [SSRN](https://ssrn.com/abstract=2983919) ----- The package is still in the development phase, hence please share your comments and suggestions with us. Contributions welcome! \- **Matthias Buechner, Leland Bybee**