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73171d701c8f4959401cac3816f2d4ddc15fc238
OpenBB/openbb_platform/openbb/package/econometrics.py
Pratyush Shukla 73171d701c Standard convention for multi-ticker symbols (#5549) 
* standardize symbol input param in forex and crypto historical data

* static

* linting
2023-10-12 18:24:13 +05:30

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### THIS FILE IS AUTO-GENERATED. DO NOT EDIT. ###
from typing import Dict, List, Literal, Union
import pandas
import typing_extensions
from annotated_types import Gt
from openbb_core.app.model.obbject import OBBject
from openbb_core.app.static.container import Container
from openbb_core.app.static.filters import filter_inputs
from openbb_provider.abstract.data import Data
from pydantic import validate_call
class ROUTER_econometrics(Container):
"""/econometrics
bgot
coint
corr
dwat
granger
ols
ols_summary
panelbols
panelfd
panelfmac
panelols
panelpols
panelre
unitroot
"""
def __repr__(self) -> str:
return self.__doc__ or ""
@validate_call(config=dict(arbitrary_types_allowed=True))
def bgot(
self,
data: Union[List[Data], pandas.DataFrame],
y_column: str,
x_columns: List[str],
lags: typing_extensions.Annotated[int, Gt(gt=0)] = 1,
) -> OBBject[Data]:
"""Perform Breusch-Godfrey Lagrange Multiplier tests for residual autocorrelation.
Parameters
----------
data: List[Data]
Input dataset.
y_column: str
Target column.
x_columns: str
List of columns to use as exogenous variables.
lags: PositiveInt
Number of lags to use in the test.
Returns
-------
OBBject[Data]
OBBject with the results being the score from the test.
""" # noqa: E501
inputs = filter_inputs(
data=data,
y_column=y_column,
x_columns=x_columns,
lags=lags,
)
return self._run(
"/econometrics/bgot",
**inputs,
)
@validate_call(config=dict(arbitrary_types_allowed=True))
def coint(
self, data: Union[List[Data], pandas.DataFrame], columns: List[str]
) -> OBBject[Data]:
"""Show co-integration between two timeseries using the two step Engle-Granger test.
Parameters
----------
data: List[Data]
Input dataset.
columns: List[str]
Data columns to check cointegration
maxlag: PositiveInt
Number of lags to use in the test.
Returns
-------
OBBject[Data]
OBBject with the results being the score from the test.
""" # noqa: E501
inputs = filter_inputs(
data=data,
columns=columns,
)
return self._run(
"/econometrics/coint",
**inputs,
)
@validate_call(config=dict(arbitrary_types_allowed=True))
def corr(self, data: Union[List[Data], pandas.DataFrame]) -> OBBject[List[Data]]:
"""Get the corrlelation matrix of an input dataset.
Parameters
----------
data : List[Data]
Input dataset.
Returns
-------
OBBject[List[Data]]
Correlation matrix.
""" # noqa: E501
inputs = filter_inputs(
data=data,
)
return self._run(
"/econometrics/corr",
**inputs,
)
@validate_call(config=dict(arbitrary_types_allowed=True))
def dwat(
self,
data: Union[List[Data], pandas.DataFrame],
y_column: str,
x_columns: List[str],
) -> OBBject[Dict]:
"""Perform Durbin-Watson test for autocorrelation.
Parameters
----------
data: List[Data]
Input dataset.
y_column: str
Target column.
x_columns: str
List of columns to use as exogenous variables.
Returns
-------
OBBject[Data]
OBBject with the results being the score from the test.
""" # noqa: E501
inputs = filter_inputs(
data=data,
y_column=y_column,
x_columns=x_columns,
)
return self._run(
"/econometrics/dwat",
**inputs,
)
@validate_call(config=dict(arbitrary_types_allowed=True))
def granger(
self,
data: Union[List[Data], pandas.DataFrame],
y_column: str,
x_column: str,
lag: typing_extensions.Annotated[int, Gt(gt=0)] = 3,
) -> OBBject[Data]:
"""Perform Granger causality test to determine if X "causes" y.
Parameters
----------
data: List[Data]
Input dataset.
y_column: str
Target column.
x_column: str
Columns to use as exogenous variables.
lag: PositiveInt
Number of lags to use in the test.
Returns
-------
OBBject[Data]
OBBject with the results being the score from the test.
""" # noqa: E501
inputs = filter_inputs(
data=data,
y_column=y_column,
x_column=x_column,
lag=lag,
)
return self._run(
"/econometrics/granger",
**inputs,
)
@validate_call(config=dict(arbitrary_types_allowed=True))
def ols(
self,
data: Union[List[Data], pandas.DataFrame],
y_column: str,
x_columns: List[str],
) -> OBBject[Dict]:
"""Perform OLS regression. This returns the model and results objects from statsmodels.
Parameters
----------
data: List[Data]
Input dataset.
y_column: str
Target column.
x_columns: str
List of columns to use as exogenous variables.
Returns
-------
OBBject[Dict]
OBBject with the results being model and results objects.
""" # noqa: E501
inputs = filter_inputs(
data=data,
y_column=y_column,
x_columns=x_columns,
)
return self._run(
"/econometrics/ols",
**inputs,
)
@validate_call(config=dict(arbitrary_types_allowed=True))
def ols_summary(
self,
data: Union[List[Data], pandas.DataFrame],
y_column: str,
x_columns: List[str],
) -> OBBject[Data]:
"""Perform OLS regression. This returns the summary object from statsmodels.
Parameters
----------
data: List[Data]
Input dataset.
y_column: str
Target column.
x_columns: str
List of columns to use as exogenous variables.
Returns
-------
OBBject[Dict]
OBBject with the results being summary object.
""" # noqa: E501
inputs = filter_inputs(
data=data,
y_column=y_column,
x_columns=x_columns,
)
return self._run(
"/econometrics/ols_summary",
**inputs,
)
@validate_call(config=dict(arbitrary_types_allowed=True))
def panelbols(
self,
data: Union[List[Data], pandas.DataFrame],
y_column: str,
x_columns: List[str],
) -> OBBject[Dict]:
"""Perform a Between estimator regression on panel data.
Parameters
----------
data: List[Data]
Input dataset.
y_column: str
Target column.
x_columns: str
List of columns to use as exogenous variables.
Returns
-------
OBBject[Dict]
OBBject with the fit model returned
""" # noqa: E501
inputs = filter_inputs(
data=data,
y_column=y_column,
x_columns=x_columns,
)
return self._run(
"/econometrics/panelbols",
**inputs,
)
@validate_call(config=dict(arbitrary_types_allowed=True))
def panelfd(
self,
data: Union[List[Data], pandas.DataFrame],
y_column: str,
x_columns: List[str],
) -> OBBject[Dict]:
"""Perform a first-difference estimate for panel data.
Parameters
----------
data: List[Data]
Input dataset.
y_column: str
Target column.
x_columns: str
List of columns to use as exogenous variables.
Returns
-------
OBBject[Dict]
OBBject with the fit model returned
""" # noqa: E501
inputs = filter_inputs(
data=data,
y_column=y_column,
x_columns=x_columns,
)
return self._run(
"/econometrics/panelfd",
**inputs,
)
@validate_call(config=dict(arbitrary_types_allowed=True))
def panelfmac(
self,
data: Union[List[Data], pandas.DataFrame],
y_column: str,
x_columns: List[str],
) -> OBBject[Dict]:
"""Fama-MacBeth estimator for panel data.
Parameters
----------
data: List[Data]
Input dataset.
y_column: str
Target column.
x_columns: str
List of columns to use as exogenous variables.
Returns
-------
OBBject[Dict]
OBBject with the fit model returned
""" # noqa: E501
inputs = filter_inputs(
data=data,
y_column=y_column,
x_columns=x_columns,
)
return self._run(
"/econometrics/panelfmac",
**inputs,
)
@validate_call(config=dict(arbitrary_types_allowed=True))
def panelols(
self,
data: Union[List[Data], pandas.DataFrame],
y_column: str,
x_columns: List[str],
) -> OBBject[Dict]:
"""One- and two-way fixed effects estimator for panel data.
Parameters
----------
data: List[Data]
Input dataset.
y_column: str
Target column.
x_columns: str
List of columns to use as exogenous variables.
Returns
-------
OBBject[Dict]
OBBject with the fit model returned
""" # noqa: E501
inputs = filter_inputs(
data=data,
y_column=y_column,
x_columns=x_columns,
)
return self._run(
"/econometrics/panelols",
**inputs,
)
@validate_call(config=dict(arbitrary_types_allowed=True))
def panelpols(
self,
data: Union[List[Data], pandas.DataFrame],
y_column: str,
x_columns: List[str],
) -> OBBject[Dict]:
"""Perform a Pooled coefficvient estimator regression on panel data.
Parameters
----------
data: List[Data]
Input dataset.
y_column: str
Target column.
x_columns: str
List of columns to use as exogenous variables.
Returns
-------
OBBject[Dict]
OBBject with the fit model returned
""" # noqa: E501
inputs = filter_inputs(
data=data,
y_column=y_column,
x_columns=x_columns,
)
return self._run(
"/econometrics/panelpols",
**inputs,
)
@validate_call(config=dict(arbitrary_types_allowed=True))
def panelre(
self,
data: Union[List[Data], pandas.DataFrame],
y_column: str,
x_columns: List[str],
) -> OBBject[Dict]:
"""Perform One-way Random Effects model for panel data.
Parameters
----------
data: List[Data]
Input dataset.
y_column: str
Target column.
x_columns: str
List of columns to use as exogenous variables.
Returns
-------
OBBject[Dict]
OBBject with the fit model returned
""" # noqa: E501
inputs = filter_inputs(
data=data,
y_column=y_column,
x_columns=x_columns,
)
return self._run(
"/econometrics/panelre",
**inputs,
)
@validate_call(config=dict(arbitrary_types_allowed=True))
def unitroot(
self,
data: Union[List[Data], pandas.DataFrame],
column: str,
regression: Literal["c", "ct", "ctt"] = "c",
) -> OBBject[Data]:
"""Perform Augmented Dickey-Fuller unit root test.
Parameters
----------
data: List[Data]
Input dataset.
column: str
Data columns to check unit root
regression: str
Regression type to use in the test. Either "c" for constant only, "ct" for constant and trend, or "ctt" for
constant, trend, and trend-squared.
Returns
-------
OBBject[Data]
OBBject with the results being the score from the test.
""" # noqa: E501
inputs = filter_inputs(
data=data,
column=column,
regression=regression,
)
return self._run(
"/econometrics/unitroot",
**inputs,
)
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