def __init__(self,
y,
x,
window_type='rolling',
window=10,
intercept=True,
nw_lags=None,
nw_lags_beta=None,
entity_effects=False,
time_effects=False,
x_effects=None,
cluster=None,
dropped_dummies={},
verbose=False):
self._window_type = common._get_window_type(window_type)
self._window = window
FamaMacBeth.__init__(self,
y=y,
x=x,
intercept=intercept,
nw_lags=nw_lags,
nw_lags_beta=nw_lags_beta,
entity_effects=entity_effects,
time_effects=time_effects,
x_effects=x_effects,
cluster=cluster,
dropped_dummies=dropped_dummies,
verbose=verbose)
self._index = self._ols_result._index
self._T = len(self._index)
def _set_window(self, window_type, window, min_periods):
self._window_type = scom._get_window_type(window_type)
if self._is_rolling:
assert (window is not None)
if min_periods is None:
min_periods = window
else:
window = len(self._x)
if min_periods is None:
min_periods = 1
self._window = int(window)
self._min_periods = min_periods
def _set_window(self, window_type, window, min_periods):
self._window_type = scom._get_window_type(window_type)
if self._is_rolling:
assert(window is not None)
if min_periods is None:
min_periods = window
else:
window = len(self._x)
if min_periods is None:
min_periods = 1
self._window = int(window)
self._min_periods = min_periods
def _set_window(self, window_type, window, min_periods):
self._window_type = scom._get_window_type(window_type)
if self._is_rolling:
if window is None:
raise AssertionError("Must specify window.")
if min_periods is None:
min_periods = window
else:
window = len(self._x)
if min_periods is None:
min_periods = 1
self._window = int(window)
self._min_periods = min_periods
def _set_window(self, window_type, window, min_periods):
self._window_type = scom._get_window_type(window_type)
if self._is_rolling:
if window is None:
raise AssertionError("Must specify window.")
if min_periods is None:
min_periods = window
else:
window = len(self._x)
if min_periods is None:
min_periods = 1
self._window = int(window)
self._min_periods = min_periods
def __init__(self, y, x, window_type='rolling', window=10,
intercept=True, nw_lags=None, nw_lags_beta=None,
entity_effects=False, time_effects=False, x_effects=None,
cluster=None, dropped_dummies={}, verbose=False):
self._window_type = common._get_window_type(window_type)
self._window = window
FamaMacBeth.__init__(
self, y=y, x=x, intercept=intercept,
nw_lags=nw_lags, nw_lags_beta=nw_lags_beta,
entity_effects=entity_effects, time_effects=time_effects,
x_effects=x_effects, cluster=cluster,
dropped_dummies=dropped_dummies, verbose=verbose)
self._index = self._ols_result._index
self._T = len(self._index)
def _set_window(self, window_type, window, min_periods):
self._window_type = common._get_window_type(window_type)
if self._is_rolling:
if window is None:
raise Exception('Must pass window when doing rolling '
'regression')
if min_periods is None:
min_periods = window
else:
window = len(self._x)
if min_periods is None:
min_periods = 1
self._window = int(window)
self._min_periods = min_periods
def _set_window(self, window_type, window, min_periods):
self._window_type = common._get_window_type(window_type)
if self._is_rolling:
if window is None:
raise Exception('Must pass window when doing rolling '
'regression')
if min_periods is None:
min_periods = window
else:
window = len(self._x)
if min_periods is None:
min_periods = 1
self._window = int(window)
self._min_periods = min_periods
def ols(**kwargs):
"""Returns the appropriate OLS object depending on whether you need
simple or panel OLS, and a full-sample or rolling/expanding OLS.
Will be a normal linear regression or a (pooled) panel regression depending
on the type of the inputs:
y : Series, x : DataFrame -> OLS
y : Series, x : dict of DataFrame -> OLS
y : DataFrame, x : DataFrame -> PanelOLS
y : DataFrame, x : dict of DataFrame/Panel/LongPanel -> PanelOLS
y : Series with MultiIndex, x : Panel/LongPanel -> PanelOLS
Parameters
----------
y: Series or DataFrame
See above for types
x: Series, DataFrame, dict of Series, dict of DataFrame, Panel, or
LongPanel
weights : Series or ndarray
The weights are presumed to be (proportional to) the inverse of the
variance of the observations. That is, if the variables are to be
transformed by 1/sqrt(W) you must supply weights = 1/W
intercept: bool
True if you want an intercept. Defaults to True.
nw_lags: None or int
Number of Newey-West lags. Defaults to None.
nw_overlap: bool
Whether there are overlaps in the NW lags. Defaults to False.
window_type: {'full sample', 'rolling', 'expanding'}
'full sample' by default
window: int
size of window (for rolling/expanding OLS). If window passed and no
explicit window_type, 'rolling" will be used as the window_type
Panel OLS options:
pool: bool
Whether to run pooled panel regression. Defaults to true.
entity_effects: bool
Whether to account for entity fixed effects. Defaults to false.
time_effects: bool
Whether to account for time fixed effects. Defaults to false.
x_effects: list
List of x's to account for fixed effects. Defaults to none.
dropped_dummies: dict
Key is the name of the variable for the fixed effect.
Value is the value of that variable for which we drop the dummy.
For entity fixed effects, key equals 'entity'.
By default, the first dummy is dropped if no dummy is specified.
cluster: {'time', 'entity'}
cluster variances
Examples
--------
# Run simple OLS.
result = ols(y=y, x=x)
# Run rolling simple OLS with window of size 10.
result = ols(y=y, x=x, window_type='rolling', window=10)
print result.beta
result = ols(y=y, x=x, nw_lags=1)
# Set up LHS and RHS for data across all items
y = A
x = {'B' : B, 'C' : C}
# Run panel OLS.
result = ols(y=y, x=x)
# Run expanding panel OLS with window 10 and entity clustering.
result = ols(y=y, x=x, cluster='entity', window_type='expanding', window=10)
Returns
-------
The appropriate OLS object, which allows you to obtain betas and various
statistics, such as std err, t-stat, etc.
"""
pool = kwargs.get('pool')
if 'pool' in kwargs:
del kwargs['pool']
window_type = kwargs.get('window_type')
window = kwargs.get('window')
if window_type is None:
if window is None:
window_type = common.FULL_SAMPLE
else:
window_type = common.ROLLING
else:
window_type = common._get_window_type(window_type)
if window_type != common.FULL_SAMPLE:
kwargs['window_type'] = common._get_window_type_name(window_type)
y = kwargs.get('y')
x = kwargs.get('x')
panel = False
if isinstance(y, DataFrame) or (isinstance(y, Series) and
isinstance(y.index, MultiIndex)):
panel = True
if isinstance(x, (Panel, LongPanel)):
panel = True
if window_type == common.FULL_SAMPLE:
for rolling_field in ('window_type', 'window', 'min_periods'):
if rolling_field in kwargs:
del kwargs[rolling_field]
if panel:
if pool == False:
klass = NonPooledPanelOLS
else:
klass = PanelOLS
else:
klass = OLS
else:
if panel:
if pool == False:
klass = NonPooledPanelOLS
else:
klass = MovingPanelOLS
else:
klass = MovingOLS
return klass(**kwargs)
def ols(**kwargs):
"""Returns the appropriate OLS object depending on whether you need
simple or panel OLS, and a full-sample or rolling/expanding OLS.
Will be a normal linear regression or a (pooled) panel regression depending
on the type of the inputs:
y : Series, x : DataFrame -> OLS
y : Series, x : dict of DataFrame -> OLS
y : DataFrame, x : DataFrame -> PanelOLS
y : DataFrame, x : dict of DataFrame/Panel -> PanelOLS
y : Series with MultiIndex, x : Panel/DataFrame + MultiIndex -> PanelOLS
Parameters
----------
y: Series or DataFrame
See above for types
x: Series, DataFrame, dict of Series, dict of DataFrame, Panel
weights : Series or ndarray
The weights are presumed to be (proportional to) the inverse of the
variance of the observations. That is, if the variables are to be
transformed by 1/sqrt(W) you must supply weights = 1/W
intercept: bool
True if you want an intercept. Defaults to True.
nw_lags: None or int
Number of Newey-West lags. Defaults to None.
nw_overlap: bool
Whether there are overlaps in the NW lags. Defaults to False.
window_type: {'full sample', 'rolling', 'expanding'}
'full sample' by default
window: int
size of window (for rolling/expanding OLS). If window passed and no
explicit window_type, 'rolling" will be used as the window_type
Panel OLS options:
pool: bool
Whether to run pooled panel regression. Defaults to true.
entity_effects: bool
Whether to account for entity fixed effects. Defaults to false.
time_effects: bool
Whether to account for time fixed effects. Defaults to false.
x_effects: list
List of x's to account for fixed effects. Defaults to none.
dropped_dummies: dict
Key is the name of the variable for the fixed effect.
Value is the value of that variable for which we drop the dummy.
For entity fixed effects, key equals 'entity'.
By default, the first dummy is dropped if no dummy is specified.
cluster: {'time', 'entity'}
cluster variances
Examples
--------
# Run simple OLS.
result = ols(y=y, x=x)
# Run rolling simple OLS with window of size 10.
result = ols(y=y, x=x, window_type='rolling', window=10)
print result.beta
result = ols(y=y, x=x, nw_lags=1)
# Set up LHS and RHS for data across all items
y = A
x = {'B' : B, 'C' : C}
# Run panel OLS.
result = ols(y=y, x=x)
# Run expanding panel OLS with window 10 and entity clustering.
result = ols(y=y, x=x, cluster='entity', window_type='expanding', window=10)
Returns
-------
The appropriate OLS object, which allows you to obtain betas and various
statistics, such as std err, t-stat, etc.
"""
pool = kwargs.get('pool')
if 'pool' in kwargs:
del kwargs['pool']
window_type = kwargs.get('window_type')
window = kwargs.get('window')
if window_type is None:
if window is None:
window_type = 'full_sample'
else:
window_type = 'rolling'
else:
window_type = common._get_window_type(window_type)
if window_type != 'full_sample':
kwargs['window_type'] = common._get_window_type(window_type)
y = kwargs.get('y')
x = kwargs.get('x')
panel = False
if isinstance(y, DataFrame) or (isinstance(y, Series)
and isinstance(y.index, MultiIndex)):
panel = True
if isinstance(x, Panel):
panel = True
if window_type == 'full_sample':
for rolling_field in ('window_type', 'window', 'min_periods'):
if rolling_field in kwargs:
del kwargs[rolling_field]
if panel:
if pool == False:
klass = NonPooledPanelOLS
else:
klass = PanelOLS
else:
klass = OLS
else:
if panel:
if pool == False:
klass = NonPooledPanelOLS
else:
klass = MovingPanelOLS
else:
klass = MovingOLS
return klass(**kwargs)
def ols(**kwargs):
"""Returns the appropriate OLS object depending on whether you need
simple or panel OLS, and a full-sample or rolling/expanding OLS.
Parameters
----------
y: Series for simple OLS. DataFrame for panel OLS.
x: Series, DataFrame, or dict of Series for simple OLS.
Dict of DataFrame for panel OLS.
intercept: bool
True if you want an intercept. Defaults to True.
nw_lags: None or int
Number of Newey-West lags. Defaults to None.
nw_overlap: bool
Whether there are overlaps in the NW lags. Defaults to False.
window_type: {'full sample', 'rolling', 'expanding'}
'full sample' by default
window: int
size of window (for rolling/expanding OLS). If window passed and no
explicit window_type, 'rolling" will be used as the window_type
Panel OLS options:
pool: bool
Whether to run pooled panel regression. Defaults to true.
weights: DataFrame
Weight for each observation. The weights are not normalized;
they're multiplied directly by each observation.
entity_effects: bool
Whether to account for entity fixed effects. Defaults to false.
time_effects: bool
Whether to account for time fixed effects. Defaults to false.
x_effects: list
List of x's to account for fixed effects. Defaults to none.
dropped_dummies: dict
Key is the name of the variable for the fixed effect.
Value is the value of that variable for which we drop the dummy.
For entity fixed effects, key equals 'entity'.
By default, the first dummy is dropped if no dummy is specified.
cluster: {'time', 'entity'}
cluster variances
Returns
-------
The appropriate OLS object, which allows you to obtain betas and various
statistics, such as std err, t-stat, etc.
Examples
--------
# Run simple OLS.
result = ols(y=y, x=x)
# Run rolling simple OLS with window of size 10.
result = ols(y=y, x=x, window_type='rolling', window=10)
print result.beta
result = ols(y=y, x=x, nw_lags=1)
# Set up LHS and RHS for data across all items
y = A
x = {'B' : B, 'C' : C}
# Run panel OLS.
result = ols(y=y, x=x)
# Run expanding panel OLS with window 10 and entity clustering.
result = ols(y=y, x=x, cluster='entity', window_type='expanding', window=10)
"""
pool = kwargs.get('pool')
if 'pool' in kwargs:
del kwargs['pool']
window_type = kwargs.get('window_type')
window = kwargs.get('window')
if window_type is None:
if window is None:
window_type = common.FULL_SAMPLE
else:
window_type = common.ROLLING
else:
window_type = common._get_window_type(window_type)
if window_type != common.FULL_SAMPLE:
kwargs['window_type'] = common._get_window_type_name(window_type)
y = kwargs.get('y')
if window_type == common.FULL_SAMPLE:
# HACK (!)
for rolling_field in ('window_type', 'window', 'min_periods'):
if rolling_field in kwargs:
del kwargs[rolling_field]
if isinstance(y, Series):
klass = OLS
else:
if pool == False:
klass = NonPooledPanelOLS
else:
klass = PanelOLS
else:
if isinstance(y, Series):
klass = MovingOLS
else:
if pool == False:
klass = NonPooledPanelOLS
else:
klass = MovingPanelOLS
return klass(**kwargs)
def ols(**kwargs):
"""Returns the appropriate OLS object depending on whether you need
simple or panel OLS, and a full-sample or rolling/expanding OLS.
Parameters
----------
y: Series for simple OLS. DataFrame for panel OLS.
x: Series, DataFrame, or dict of Series for simple OLS.
Dict of DataFrame for panel OLS.
intercept: bool
True if you want an intercept. Defaults to True.
nw_lags: None or int
Number of Newey-West lags. Defaults to None.
nw_overlap: bool
Whether there are overlaps in the NW lags. Defaults to False.
window_type: int
FULL_SAMPLE, ROLLING, EXPANDING. FULL_SAMPLE by default.
window: int
size of window (for rolling/expanding OLS)
Panel OLS options:
pool: bool
Whether to run pooled panel regression. Defaults to true.
weights: DataFrame
Weight for each observation. The weights are not normalized;
they're multiplied directly by each observation.
entity_effects: bool
Whether to account for entity fixed effects. Defaults to false.
time_effects: bool
Whether to account for time fixed effects. Defaults to false.
x_effects: list
List of x's to account for fixed effects. Defaults to none.
dropped_dummies: dict
Key is the name of the variable for the fixed effect.
Value is the value of that variable for which we drop the dummy.
For entity fixed effects, key equals 'entity'.
By default, the first dummy is dropped if no dummy is specified.
cluster: {'time', 'entity'}
cluster variances
Returns
-------
The appropriate OLS object, which allows you to obtain betas and various
statistics, such as std err, t-stat, etc.
Examples
--------
# Run simple OLS.
result = ols(y=y, x=x)
# Run rolling simple OLS with window of size 10.
result = ols(y=y, x=x, window_type=ROLLING, window=10)
print result.beta
result = ols(y=y, x=x, nw_lags=1)
# Set up LHS and RHS for data across all items
y = A
x = {'B' : B, 'C' : C}
# Run panel OLS.
result = ols(y=y, x=x)
# Run expanding panel OLS with window 10 and entity clustering.
result = ols(y=y, x=x, cluster=ENTITY, window_type=EXPANDING, window=10)
"""
try:
import scipy as _
except ImportError:
raise Exception('Must install SciPy to use OLS functionality')
pool = kwargs.get('pool')
if 'pool' in kwargs:
del kwargs['pool']
window_type = kwargs.get('window_type', common.FULL_SAMPLE)
window_type = common._get_window_type(window_type)
y = kwargs.get('y')
if window_type == common.FULL_SAMPLE:
for rolling_field in ('window_type', 'window', 'min_periods'):
if rolling_field in kwargs:
del kwargs[rolling_field]
if isinstance(y, Series):
klass = OLS
else:
if pool == False:
klass = NonPooledPanelOLS
else:
klass = PanelOLS
else:
if isinstance(y, Series):
klass = MovingOLS
else:
if pool == False:
klass = NonPooledPanelOLS
else:
klass = MovingPanelOLS
return klass(**kwargs)
