VAR models

Example

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import numpy as np
import pyflux as pf
from pandas.io.data import DataReader
from datetime import datetime

ibm = DataReader(['JPM','GS','BAC','C','WFC','MS'],  'yahoo', datetime(2000,1,1), datetime(2016,3,28))
opening_prices = np.log(ibm['Open'])
model = pf.VAR(data=opening_prices,lags=1,integ=1)

Class Arguments

class VAR(data, ar, ma, integ, target)
data

pd.DataFrame or array-like : the time-series data

lags

int : the order (p) of the VAR

integ

int : how many times to difference the time series (default: 0)

target

string (data is DataFrame) or int (data is np.array) : which column to use as the time series. If None, the first column will be chosen as the data.

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():

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import pyflux as pf

# model = ... (specify a model)
model.list_priors()
model.adjust_prior(2,pf.Normal(0,1))
fit(method, **kwargs)

Estimates parameters 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():

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import pyflux as pf

# model = ... (specify a model)
model.fit("M-H",nsims=20000)
irf(h, shock_index, shock_value, shock_dir, intervals, cumulative)

Plots impulse response function graphs. h is how many time steps to look ahead for the effects of the shock, shock_index is which variable index to apply the initial shock to, shock_value applies a custom shock, but if it is None (default setting) then a 1 standard deviation shock will be applied, shock_dir is one of ‘positive’ or ‘negative’ and is the direction of the shock, intervals specifies whether to plot prediction intervals or not, and cumulative is a boolean which specifies whether to plot cumulative effects or not.

plot_fit(**kwargs)

Graphs the fit of the model.

Optional arguments include figsize - the dimensions of the figure to plot.

plot_parameters(indices, figsize)

Returns a plot of the parameters and their associated uncertainty. indices is a list referring to the parameter indices that you want ot plot. Figsize specifies how big the plot will be.

plot_predict(h, past_values, intervals, **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.

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)

Returns DataFrame of model predictions. h is an int of how many steps ahead to predict.

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.