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Results

Result objects for VAR post-estimation output.

FEVDResult

Bases: VARResultBase

Result from forecast error variance decomposition.

Attributes:

Name Type Description
idata InferenceData

ArviZ InferenceData with FEVD draws.
horizon int

Number of FEVD horizons.
var_names list[str]

Names of variables.

hdi(prob=0.89)

HDI for FEVD.

median()

Posterior median FEVD.

plot()

Plot FEVD.

to_dataframe()

Convert FEVD to DataFrame.

ForecastResult

Bases: VARResultBase

Result from VAR forecasting.

Attributes:

Name Type Description
idata InferenceData

ArviZ InferenceData with forecast draws.
steps int

Number of forecast steps.
var_names list[str]

Names of forecasted variables.

hdi(prob=0.89)

HDI for forecast.

median()

Posterior median forecast.

plot()

Plot forecast fan chart.

to_dataframe()

Convert to long-format DataFrame.

HDIResult

Bases: ImpulsoBaseModel

Structured HDI output with separate lower/upper bounds.

Attributes:

Name Type Description
lower DataFrame

DataFrame of lower HDI bounds.
upper DataFrame

DataFrame of upper HDI bounds.
prob float

HDI probability level.

HistoricalDecompositionResult

Bases: VARResultBase

Result from historical decomposition.

Attributes:

Name Type Description
idata InferenceData

ArviZ InferenceData with decomposition draws.
var_names list[str]

Names of variables.

hdi(prob=0.89)

HDI for historical decomposition.

median()

Posterior median historical decomposition.

plot()

Plot historical decomposition.

to_dataframe()

Convert historical decomposition to DataFrame.

IRFResult

Bases: VARResultBase

Result from impulse response function computation.

Attributes:

Name Type Description
idata InferenceData

ArviZ InferenceData with IRF draws.
horizon int

Number of IRF horizons.
var_names list[str]

Names of variables.

hdi(prob=0.89)

HDI for IRF.

median()

Posterior median IRF.

plot()

Plot impulse response functions.

to_dataframe()

Convert IRF to DataFrame.

LagOrderResult

Bases: ImpulsoBaseModel

Result from lag order selection.

Attributes:

Name Type Description
aic int

Optimal lag order by AIC.
bic int

Optimal lag order by BIC.
hq int

Optimal lag order by Hannan-Quinn.
criteria_table DataFrame

DataFrame of all criteria values by lag order.

summary()

Return the full criteria table.

Returns:

Type Description
DataFrame

DataFrame with information criteria for each lag order.

VARResultBase

Bases: ImpulsoBaseModel

Base class for VAR post-estimation results.

Attributes:

Name Type Description
idata InferenceData

ArviZ InferenceData holding the result draws.

hdi(prob=0.89) abstractmethod

Compute highest density interval.

Parameters:

Name Type Description Default
prob float

Probability mass for the HDI. Default 0.89.

 0.89

median() abstractmethod

Compute posterior median of the result.

plot() abstractmethod

Plot the result. Subclasses must implement.

to_dataframe() abstractmethod

Convert result to a tidy DataFrame.