GAS Regression models¶
Example¶
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | import numpy as np
import pyflux as pf
import pandas as pd
from pandas.io.data import DataReader
from datetime import datetime
a = DataReader('AMZN', 'yahoo', datetime(2012,1,1), datetime(2016,6,1))
a_returns = pd.DataFrame(np.diff(np.log(a['Adj Close'].values)))
a_returns.index = a.index.values[1:a.index.values.shape[0]]
a_returns.columns = ["Amazon Returns"]
spy = DataReader('SPY', 'yahoo', datetime(2012,1,1), datetime(2016,6,1))
spy_returns = pd.DataFrame(np.diff(np.log(spy['Adj Close'].values)))
spy_returns.index = spy.index.values[1:spy.index.values.shape[0]]
spy_returns.columns = ['S&P500 Returns']
one_mon = DataReader('DGS1MO', 'fred',datetime(2012,1,1), datetime(2016,6,1))
one_day = np.log(1+one_mon)/365
returns = pd.concat([one_day,a_returns,spy_returns],axis=1).dropna()
excess_m = returns["Amazon Returns"].values - returns['DGS1MO'].values
excess_spy = returns["S&P500 Returns"].values - returns['DGS1MO'].values
final_returns = pd.DataFrame(np.transpose([excess_m,excess_spy, returns['DGS1MO'].values]))
final_returns.columns=["Amazon","SP500","Risk-free rate"]
final_returns.index = returns.index
model = pf.GASReg('Amazon ~ SP500',data=final_returns, family=pf.GASt()) # dynamic beta model
|
Class Arguments¶
Class Methods¶
-
adjust_prior
(index, prior)¶ Adjusts the priors of the model. index can be an int or a list. prior is a prior object, such as Normal(0,3).
Here is example usage for adjust_prior()
:
1 2 3 4 5 | import pyflux as pf
# model = ... (specify a model)
model.list_priors()
model.adjust_prior(2,pf.Normal(0,1))
|
-
fit
(method, **kwargs)¶ Estimates latent variables for the model. Returns a Results object. method is an inference/estimation option; see Bayesian Inference and Classical Inference sections for options. If no method is provided then a default will be used.
Optional arguments are specific to the method you choose - see the documentation for these methods for more detail.
Here is example usage for fit()
:
1 2 3 4 | import pyflux as pf
# model = ... (specify a model)
model.fit("M-H",nsims=20000)
|
-
plot_fit
(**kwargs)¶ Graphs the fit of the model and the dynamic betas.
Optional arguments include figsize - the dimensions of the figure to plot.
-
plot_z
(indices, figsize)¶ Returns a plot of the latent variables and their associated uncertainty. indices is a list referring to the latent variable indices that you want ot plot. Figsize specifies how big the plot will be.
-
plot_predict
(h, past_values, intervals, oos_data, **kwargs)¶ Plots predictions of the model. h is an int of how many steps ahead to predict. past_values is an int of how many past values of the series to plot. intervals is a bool on whether to include confidence/credibility intervals or not. oos_data is a DataFrame in the same format as the original DataFrame and has data for the explanatory variables to be used for prediction.
Optional arguments include figsize - the dimensions of the figure to plot.
-
plot_predict_is
(h, past_values, intervals, **kwargs)¶ Plots in-sample rolling predictions for the model. h is an int of how many previous steps to simulate performance on. past_values is an int of how many past values of the series to plot. intervals is a bool on whether to include confidence/credibility intervals or not.
Optional arguments include figsize - the dimensions of the figure to plot.
-
predict
(h, oos_data)¶ Returns DataFrame of model predictions. h is an int of how many steps ahead to predict. oos_data is a DataFrame in the same format as the original DataFrame and has data for the explanatory variables to be used for prediction.
-
predict_is
(h)¶ Returns DataFrame of in-sample rolling predictions for the model. h is an int of how many previous steps to simulate performance on.