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: BaseModel

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: BaseModel

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: BaseModel

Base class for VAR post-estimation results.

Attributes:

Name	Type	Description
idata	InferenceData	ArviZ InferenceData holding the result draws.

hdi(prob=0.89)

Compute highest density interval.

Parameters:

Name	Type	Description	Default
prob	float	Probability mass for the HDI. Default 0.89.	0.89

median()

Compute posterior median of the result.

plot() abstractmethod

Plot the result. Subclasses must implement.

to_dataframe()

Convert result to a tidy DataFrame.